Early piglet mortality in loose-housed sows related to sow and piglet behaviour and to the progress of parturition

Early piglet mortality in loose-housed sows

related to sow and piglet behaviour and

to the progress of parturition

Lene Juul Pedersen a,*, Erik Jørgensen b, Teresia Heiskanen c,Birgitte I. Damm c

a Danish Institute of Agricultural Sciences, Department of Animal Health,

Welfare and Nutrition, P.O. Box 50, 8830 Tjele, Denmarkb Danish Institute of Agricultural Sciences, Department of Genetics and Biotechnology,

P.O. Box 50, 8830 Tjele, Denmarkc The Royal Veterinary and Agricultural University, Department of Large Animal Science,

Grønnegardsvej 8, 1870 Frederiksberg C, Denmark

Accepted 24 June 2005

Available online 16 August 2005

Abstract

The aim of the present study was to identify characteristics of sow behaviour and parturition

related to early piglet mortality in loose-housed farrowing sows. Data from 152 farrowings that

originated from three different herds with loose-housed sows during parturition were used. Graphical

chain models were used to model the relationships between perinatal behaviour, periparturient

individual conditions (time of day of parturition, rectal temperature 1–3 days postpartum) and causes

of early piglet mortality. Modelling was based on the correlation between variables within herd and

farrowing batch.

The analysis showed that different causes of mortality were linked to different behavioural

variables during the periparturient period and that they grouped into three independent categories.

The first category was associated with stillbirth and death due to other causes. Stillbirth was

positively related to the variation of the inter-birth interval and negatively related to the percentage of

piglets that suckled during the first 8 h after birth of first piglet (BFP). Death due to other causes was

negatively related to the suckling activity during the post-partal period (9–24 h after BFP). The

second category was associated with piglet crushing, which was positively related to much lateral

www.elsevier.com/locate/applanim

Applied Animal Behaviour Science 96 (2006) 215–232

* Corresponding author.

E-mail address: [email protected] (L.J. Pedersen).

0168-1591/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.applanim.2005.06.016

lying the last 4 h before BFP. Finally, the third category was associated with death due to lack of

colostrums ingestion of colostrum, which was linked to the time of parturition and sow rectal

temperature on days 1 and 2 after farrowing. Sows that gave birth during the morning compared to

evening/night had more dead piglets due to lack of colostrums ingestion. Death due to lack of

colostrums ingestion was also higher in sows with high rectal temperature on the day after BFP. The

results further showed that litter size not only influenced mortality but also behavioural variables.

High litter size was related to high nest building activity before BFP, low but more variable inter-birth

intervals, and much lateral lying after BFP. The study points towards several areas of interest for

further research that can help understand early piglet mortality. These include causes of variation in

the progress of parturition, causes of individual differences in sow activity and responsiveness to

piglets, and early management of farrowing. In addition, the study suggests possible indicators of

problematic farrowings rendering liveborn piglets at risk. These include prepartum lateral lying in the

sow, low suckling activity and rate of stillbirth.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Farrowing behaviour; Graphical models; Sus Scrofa

1. Introduction

In sow herds, one of the most important causes of production loss is piglet mortality,

especially in the first few days after farrowing. Data from management information

systems indicate differences between populations both between and within systems. A

recent report (Jultved, 2004) showed that an average of 13.3% of the liveborn piglets die

before weaning in Danish sow herds. Mortality in modern housing systems for loose-

housed sows is rather variable ranging from 9 to 25% of liveborn piglets (Marchant et al.,

2000; Grandinson et al., 2002; Pedersen et al., 2003; Damm et al., 2005b). Thus, early

piglet mortality is of great economic importance but it also constitutes a welfare problem

because the piglets often die of hunger and various traumas caused by thread and crushing

(Fraser, 1990; Vaillancourt and Tubbs, 1992).

Irrespective of production system, the majority of death among piglets occurs within the

first two days postpartum (Bille et al., 1974; English and Smith, 1975; Vaillancourt and

Tubbs, 1992; Kongsted and Larsen, 1999). Stillbirth reports from both crates and loose-

housing systems range from 3 to 6% of the total litter when using references from studies

where post-mortem examinations have been carried out (Kongsted and Larsen, 1999;

Fraser et al., 1997; Damm et al., 2005a; Tuchscherer et al., 2000). Crushing seems to be the

main cause of death among the liveborn piglets and starving and/or hypothermic piglets are

particularly at risk (Fraser, 1990; Vaillancourt and Tubbs, 1992; Marchant et al., 2000;

Damm et al., 2005b). Thus, these factors are also important in the understanding of piglet

mortality.

The variation in early piglet mortality between litters within the same herd is

considerably larger than would be expected with constant mortality rate. Since the

heritability of early piglet mortality is rather low (Knol et al., 2002; Su et al., 2004;

Grandinson et al., 2002), the variation between litters within a herd may to a great extent be

accounted for by non-genetic differences caused by, for example differences in climate,

management and/or sow conditions at the particular farrowing. For example, management

L.J. Pedersen et al. / Applied Animal Behaviour Science 96 (2006) 215–232216

of night-time farrowings may differ from that of day-time farrowings and lameness and/or

obesity may only occur in some sows.

The relationship between mortality and individual conditions around parturition is not

well known. Larger data sets on mortality exist both from experimental herds and from

production herds. Such data sets often lack information about individual conditions around

parturition, making it difficult to point at potential causal mechanisms underlying individual

differences in mortality. In order to focus the experimental design, there is a need beforehand

to identify potential causal mechanism. Multivariate data analysis may be used in an

exploratory phase to identify such potential mechanism underlying the variability. In this

context, graphical models, and especially the so-called chain-graph models are promising

techniques (Wermuth, 2003). The latter technique allows a partitioning of the variables based

on prior knowledge, corresponding to, e.g. Risk factors, Diseases and Symptoms.

In the present study, the techniques of graphical models were used to gain insight into

the relations between early piglet mortality in loose housing systems and perinatal sow and

piglet behaviour and individual conditions around parturition.

2. Materials and methods

2.1. Animals and housing

Data were collected from 152 farrowings. The 152 farrowings took place in three

different herds (Table 1), where sows were loose-housed during farrowing and lactation.

Farrowings within each herd were divided into batches. A batch was defined as a group of

sows that was introduced to the farrowing unit within a 2–4 days period. However, in about

a third of the cases (11 out of 30) it was necessary to include sows which farrowed further

apart in the same batch, in order to avoid batches with very few animals. In these cases the

batch was a compromise between closeness of day of introduction and the number of sows

in the batch.

The body condition of the sows at the time of introduction to the farrowing pens was

observed as well as the condition of the legs. All sows with abnormal body condition

(obese/emaciated sows) and/or with leg problems were excluded from the study.

The three herds differed with respect to pen design and management and the

characteristics of each herd are described below:

� Herd 1: The sows were pure-bred Danish Landrace sows. During farrowing the sows

were housed in Schmid pens (Schmid, 1991, 1993) with modifications that had been

L.J. Pedersen et al. / Applied Animal Behaviour Science 96 (2006) 215–232 217

Table 1

Summary of main production traits in the three herds (L: Danish Landrace, Y: Danish Yorkshire)

Herd Breed Litters Batches Total born

piglets per litter

Dead

per litter

Percentage

dead

Mean

parity

1 L � L 47 11 13.6 4.3 31.7 1.3

2 Y � L 55 15 12.5 3.1 24.3 2.4

3 Y � L 30 6 14.4 2.8 19.2 2.8

made in an attempt to make them more suitable for Danish production methods (see Damm

et al., 2005a). A schematic representation of the pen can be seen in Fig. 1. The concrete

floor pens were 260 cm � 270 cm and divided into a solid floor resting area (2.8 m2) and a

slatted floor activity area (3.9 m2). An octangular piglet box was situated in the centre of

the pen with an opening towards the resting area. A rail was positioned along the back wall

and the corners in the resting area from the time of introduction to the pen until 5 days after

farrowing. Cross-fostering of piglets from large litters to smaller litters was carried out

within the first two days of the piglets’ lives. The sows were manually fed a restricted diet

of standard sow feed from introduction until farrowing and ad libitum fed from farrowing

until weaning. During gestations the animals were housed in pens with partly slatted floor

and bio-fix feeding system in small groups of four–six females. The herd was a member of

the Danish breeding system DanAVL, and previous results from the herd have been

published in Damm et al. (2005b).

� Herd 2: All sows were cross-bred Danish Yorkshire � Danish Landrace sows. During

farrowing they were housed in a pen similar to that in herd 1. The floor, however, was

solid throughout the pen, and the pen was also slightly larger (270 cm � 300 cm). The

piglet box was of approximately the same size as in herd 1 but it was rectangular. Litter-

size was not standardized and no cross-fostering was performed. The sows were

manually fed a restricted diet of standard sow feed from introduction until farrowing

after which sows were fed on a semi-ad libitum basis. During gestation the sows were

confined in stalls. The farm was an experimental herd.


Fig. 1. Design of farrowing pen in herd 1–3. Piglet box indicates creep area.

� Herd 3: The sows were cross-bred Danish Yorkshire � Danish Landrace sows. During

farrowing the sows were housed in pens measuring 290 m � 400 m. The pens consisted

of a piglet area (150 m � 260 m) separated from the rest of the pen by vertical bars. In

the piglet area a heating panel was placed in the corner farthest away from the sow area

forming the creep area. The sow area was divided into a resting and a dunging area as

shown in Fig. 1. Cross-fostering of piglets from large litters to smaller litters was carried

out within the first two days of the piglets’ lives. Twice daily the sows were manually fed

a restricted ration before parturition. From farrowing until weaning they were fed on a

semi-ad libitum basis. During gestation the sows were confined in stalls. The farm was

an experimental herd.

The production traits in the three herds are shown in Table 1.

2.2. Maternal behaviour and reproduction

The behaviour of the sows and piglets was observed from 4 h prior to parturition until

24 h after Birth of first piglet (BFP) using 24 h time-lapse video recordings. One–zero

sampling (Martin and Bateson, 1986) at 15 min intervals was used to record whether the

sow performed nest building (pawing, rooting, carrying or arranging straw) and whether

she remained in lateral recumbency throughout the 15 min period. These recordings were

subsequently split into two time intervals: before BFP (from �4 to 0 h before BFP) and

after BFP (from 0 to 8 h after). The average number of 15 min periods where the sow

performed nest-building activities was calculated as well as the number of periods where

the sows lay in lateral recumbency during the whole 15 min period (LatBef, LatAft).

Furthermore, instantaneous scan sampling of the number of piglets suckling (teat in mouth

while performing sucking movements) as well as the number of piglets in contact with the

udder (piglet in contact with the udder without sucking on a teat) was carried out. The

average percentage of piglets suckling at the udder during the instantaneous samplings was

subsequently calculated from BFP until 8 h after (Suckl8) and from 9 h after BFP until 24 h

after (Suckl24). Similarly, the average percentage of piglets in contact with the udder was

calculated from BFP until 8 h (Udder8) after and from 9 h after BFP until 24 h after

(Udder24). In addition, the time from BFP to the time of birth of each subsequent piglet

was recorded. From this, the average inter-birth interval (IBInt) and the standard deviation

of the inter-birth interval (StdIBInt) were calculated.

On the day of parturition and the following two days, the rectal temperature of the sows

was measured (Temp0, Temp1, Temp2). The length of gestation (days), the total number of

piglets born by the same sow (TotBorn) and the number of the sows’ own piglets dying each

day after birth were recorded. All piglets that died while still being with their biological

mother (including still born piglets) were marked with date of death and sow number, and

then frozen. Each month the dead piglets were thawed and the cause of death was determined

by macroscopic examination and divided into four categories: still born (StillB), piglets dying

without injuries and without milk in their stomach (NoMilk), piglets dying from injuries

caused by the sow either with or without milk in their stomach (Crush) and piglets dying from

other causes including intestinal and infectious diseases and being culled by the stock person

due to low viability (OthCau). A detailed description of each category can be found in Damm


et al. (2005b). Note that some of the piglets that died from injuries (Crush) had not yet

ingested colostrum.

For use in the statistical analysis to model the effect of the diurnal variation of BFP the

sinus and cosinus of BFP were calculated, i.e. sin BFP = cos(BFP � 2p/24). Furthermore,

RelSuckl8 was calculated as the Proportion of piglets at the udder 0–8 h after BFP that

suckles, where RelSuckl8 = Suckl8/Udder8.

Variables included in the analysis are described and summarized in Table 2.

2.3. Statistical analysis

The model used was a graphical chain model (Lauritzen and Wermuth, 1989) also called

a block-recursive model (Højsgaard and Thiesson, 1995). A chain-graph model facilitates

the search for the optimal statistical model of a multivariate data set. In recent years,

standard software for estimation in these models have become available, such as MIM

(Edwards, 2000) and GraphFitI (Blauth et al., 2000). This has lead to a number of

applications especially within social science, Wermuth (2003) lists some examples and

Mohamed et al. (1998) illustrate the application in a study of infant mortality in humans. In

special cases, the directed links may be considered as modelling causal relations. We refer

to Lauritzen and Richardson (2002) for a detailed discussion of this aspect.

The independence structure of a multivariate data set can be shown as a so-called

conditional independence graph. Each variable in the data set is shown with a node (or

vertex), and edges (or links) between the nodes indicate whether the variables are

conditionally dependent. In chain-graph models the variables are partitioned into blocks. In

the present study, four blocks of variables were identified based on the temporal ordering of


Table 2

Variables included in the analysis of mortality

Description Abbreviation Block

Herd, batch, parity Herd, Batch, Parity Design

Total born piglets TotBorn Sow

Birth of first piglet BFP

Sinus component of BFP sin BFP

Cosinus component of BFP cos BFP Prenatal

Lateral lying before BFP LatBef

Intervals with nest building before BFP NestBef

Still born piglets StillB

Piglets that died without milk in stomach NoMilk

Piglets that died with injuries from the sow Crush

Piglets that died from other causes OthCau

Rectal temperature day 0, 1, 2 (8C) Temp0, Temp1, Temp2

Average inter-birth interval in the litter IBInt Postnatal

Standard deviation of the inter-birth interval (min) StdIBInt

Lateral lying the first 8 h after BFP LatAft

Intervals with nest building the first 8 h after BFP NestAft

Piglets suckling 0–8 and 9–24 h after BFP (%) Suckl8, Suckl24

Piglets at the udder 0–8 and 9–24 h after BFP (%) Udder8, Udder24

the variables. The first block consisted of variables related to the Design of the study with

the variables Herd, Batch and Parity. The second block was related to the individual Sow

and consisted only of Total number of piglets (TotBorn). The third block consisted of

variables observed in the Pre-natal period. Finally, the fourth block consisted of variables

observed in the Peri- and Postnatal period. The details of the blocking can be seen in

Table 2. This partitioning implies that links between variables within a block are required to

be undirected, whereas links between blocks are directed according to the ordering of the

blocks. A directed link indicates a dependency similar to the association between

explanatory and response variables in regression models and is indicated with an arrow

between the two nodes.

The Batch effect is a random effect, and mainly used to model the dependency between

observations within the same Batch. This introduces complications. First of all, no program

for analysis is readily available. Secondly, dependency between variables may exist at

several levels of the hierarchy. Therefore, we have chosen to correct the observations for

the effect of Batch before the chain-graph model analysis. The initial model runs showed

that many of the dependent variables were related to the herd, parity and total number of

born piglets. Therefore, the following model was estimated initially

YðkÞhi ¼ m

ðkÞh pðiÞ þ B

ðkÞhtðiÞ þ b1kXhi þ b2kX

2hi þ eðkÞhi (1)

where YðkÞhi is the i observed value of dependent variable k in the h herd, m

ðkÞh pðiÞ the ‘mean’

level of variable k in herd h and the parity p(i) corresponding to the ith observation. BðkÞhtðiÞ is

the random effect of batch t(i) within herd h, BðkÞhtðiÞ �Nð0; s2

BkÞ, b�k a regression coefficient

for the linear and the quadratic effect of total number of born piglets, XhieðkÞhi is the residual

assumed distributed as Nð0; s2ekÞ.

The ‘corrected’ values of the dependent variables YðkÞhi are thus calculated as

YðkÞhi ¼ Y

ðkÞhi � Y

ðkÞhi , where Y

ðkÞhi is the predicted values from the model in Eq. (1). The

calculations were made using the lme in the nlme package (Pinheiro et al., 2004) in R (R

Development Core Team, 2004).

The corrected values were analysed with a Block Recursive Model (Chain-graph) using

the MIM3.1-program (Edwards, 2000), where the variables are supplemented with a

discrete factor containing the herd � parity levels in block no. 1 (to investigate interactions

between herd � parity and the variables). The mimR-package (Højsgaard, 2003) was used

to call the MIM-program from R. The Bayesian Information Criteria (BIC) were used to

find the final model. The BIC usually leads to more parsimonious models than simple

iterative significance testing, where the number of tests severely invalidates the overall

level of significance. In order to avoid local minima in the model search, the search started

with a forward stepwise procedure from the model with only main effects. The ‘incoherent’

search strategy (i.e. repeated investigation of links) between decomposable graphs was

used. Three selection criteria were used: first the likelihood criteria, secondly the Akaike’s

information criteria (AIC) and third the BIC. Finally, a backward selection strategy with

BIC as the criterion was performed. This procedure was used because simple backwards

selection from the full saturated model consistently ended up with less satisfactory models

(higher BIC-values) than the forward search from a main effects only model. A detailed

description of the search procedures mentioned can be found in Edwards (2000). The


search strategy resulted in an overall fit better than both the backward and the forward

selection alone. Subsequently, the sub model candidates identified via the MIM program

were reformulated for analysis using standard mixed model formulation, and analysed

using the lme and GLMM function of R (Bates and Sarkar, 2004). In this final step

interactions and main design factors such as herd and parity were sequentially omitted

from the model if they were not significant. In most cases, the effect of these factors were

additive and results have been presented as least square means, using the average of the

herd and parity effect. In one case, the effect of herd interacted with the relation between

two behaviour variables, in this case we have presented the relationship for each herd.

The magnitude of the relationships between two variables was calculated as marginal

changes in the dependent variables as a function of a change in each independent variables

from the average level. Such calculations should, however, be treated with caution because

of the collinearity of the variables due to the non-experimental design of the data

collection.

Due to missing values for some of the temperature measurements the model found for

the full dataset without temperature, was supplemented with an additional model search

with the data with temperature included.

3. Results

Herd, Parity and Batch all influenced Total number of piglets born (TotBorn) and the

first step of the analysis was therefore to correct data for these design factors. TotBorn also

influenced most behavioural variables. The percentage of intervals with nest building

before birth (NestBef) increased from an average of 64.9 to 65.9% when litter size

increased from 13 to 14. An increase in TotBorn of one piglet resulted in a 9% decrease of

the Average inter-birth interval in the litter (IBInt), and an increase of Standard deviation

of the inter-birth interval (StdIBInt) with 6%. Lateral lying the first 8 h after BFP (LatAft)

increased slightly with 1.6% when litter size increased from 13 to 14 piglets.

The influence on specific causes of mortality is described in detail under each

subsection.

3.1. The total model: the structural relationship

After correction for Batch, the model selection resulted in the graphical model shown in

Fig. 2. To reduce the complexity of the figure the links from variables in the Design and

Sow block have been omitted.

The BIC of the final model was 4682 using 1640 degrees of freedom. The change in BIC

(which measures the effect of reducing the model with a particular variable) was larger

when links between two behavioural variables compared to a behavioural variable and a

mortality variable were omitted (see Fig. 2). The final graphical model showed that the four

different causes of death grouped into three groups related to different behavioural

variables. One group comprised Piglets that died without milk in stomach (NoMilk) and

variables related to sow rectal temperature after parturition and to time of birth of the first

piglet. Another comprised Still born piglets (StillB) and Piglets that died from other causes


(OthCau) and was related to suckling activity, birth interval and time spent in lateral

recumbency after birth. Finally, the third comprised Piglets that died with injuries from the

sow (Crush) and was related to time spent in lateral recumbency before birth, nest building

activity and sow rectal temperature on the day of parturition. The three groups were almost

independent of each other. The relationships between variables within each group are

described in detail in the following subsections.

3.2. Quantification of the submodels

3.2.1. Still born piglets (StillB)

The most common cause of death was stillbirth. The estimated mean value of Still born

piglets (StillB) was 7.2% of the total born piglets. The influence of Total number of piglets

born (TotBorn) was nonlinear with a marginal increase of 0.7% when TotBorn increased

from 13 to 14 piglets (see Fig. 3). As also seen in Fig. 3 StillB was directly linked to the

Standard deviation of the inter-birth interval (StdIBInt) in that increased StdIBInt resulted

in increased StillB.

StdIBInt was further positively correlated to Average inter-birth interval in the litter

(IBInt) in that a 1% increase in IBInt increased StdIBInt with 1.4%.

Stillbirth was also linked to Percentage of piglets suckling 0–8 h after BFP (Suckl8) and

Percentage of piglets at the udder 0–8 h after BFP (Udder8). However, these two variables

were highly correlated, as Udder8 is the maximum level of Suckl8. This resulted in

complicated models of the relationship. Therefore, in the detailed analysis Proportion of


Fig. 2. Selected block recursive model. See Table 2 for description of variable names. The nodes with a double

line indicate the four different mortality causes. The numbers at the links indicate marginal increase in BIC from

removing the link from the model. Removal of links indicated by (*) would lead to non-decomposable models

and have not been validated. BIC values at links marked with (*) were not calculated due to missing values. Links

from the Design and Sow are omitted to reduce the complexity of the figure.

piglets at the udder 0–8 h after BFP that suckles (RelSuckl8) was analysed instead of

Suckl8, and a more parsimonious model was found. As shown in Fig. 4, the expected level

of RelSuckl8 decrease with increasing number of StillB.

Sows with litters with low Suckl8 also showed little Lateral lying the first 8 h after BFP

(LatAft). However, there was no direct link between StillB and LatAft.

3.2.2. Piglets that died from other causes (OthCau)

On average 4.6% of the Total number of piglets born died of OthCau. OthCau increased

nonlinearly with a marginal increase of 0.6% when TotBorn increased from 13 to 14 piglets as

shown in Fig. 5. Death from OthCau primarily included piglets that died of disease or were

culled by the stock person due to poor viability. As can be seen in Fig. 2, this death cause was

related to thesamebehaviouralvariablesasstillbirth.OthCauwasdirectly linkedtoPercentage


Fig. 3. Estimated mortality (solid line) due to Still born piglets (StillB) as a function of Total number of piglets

born (TotBorn) (left) and Standard deviation of the inter-birth interval (StdIBInt) (right). The dotted lines

indicates 0.95 confidence interval. The rug indicates observed values.

Fig. 4. Relationship between number of stillborn piglets in the litter and the proportion of time spent suckling.

of piglets suckling 9–24 h after BFP (Suckl24) where an increase in mortality due to other

causeswas linkedtodecreasedSuckl24(seeFig.5).ThevariableSuckl24wasrelated toSuckl8

and Udder8 and thus indirectly also to Piglets that died with injuries from the sow (Crush).

3.2.3. Crush

The estimated mean value of the percentage of piglets per litter that died from injuries

caused by the sows (Crush) was 6.8% of the Total number of piglets born. The influence of

TotBorn showed a marginal increase of 1.2% when it increased from 13 to 14 piglets (see

Fig. 6).

As can be seen in Fig. 2, the only behavioural variable that related to Crush was Lateral

lying before BFP (LatBef), in that the more LatBef the more Crush. The apparent link

between IBInt and Crush found in the model selection step was not confirmed by the more

detailed analysis.

LatBef was highly correlated to Intervals with nest building before BFP (NestBef) in

that the sows spent almost all of the 16 observation periods performing either behaviour.


Fig. 5. Relation between TotBorn and Percentage of piglets suckling 9–24 h after BFP (Suckl24) and mortality

due to Other causes (OthCau). The solid line indicates expected level. The dotted lines indicate 0.95 confidence

interval. The rug indicates observed values.

Fig. 6. Relation between TotBorn, Lateral lying before BFP (LatBef) and mortality due to crushing (Crush). The

solid line indicates expected level. The dotted lines indicates 0.95 confidence interval. The rug indicates observed

values.

Thus, NestBef was related to Intervals with nest building the first 8 h after BFP (NestAft),

whereas there was no link between LatBef and LatAft. Sows with high NestBef also had

high NestAft (see Fig. 7) and high Rectal temperature day 0 (Temp0).

3.2.4. Piglets that died without milk in stomach (NoMilk)

The estimated mean percentage of piglet that died solely due to lack of colostrum uptake

(NoMilk) was 0.8% of the total number of piglets born. The detailed analysis (Fig. 2)

showed that NoMilk was related to BFP as described by a sinoid function and to Rectal

temperature day 1 (Temp1) as shown in Fig. 8.

Temp1 and Rectal temperature day 2 (Temp2) were strongly related whereas none of

them were related to Temp0.

4. Discussion

Several studies, including the present one, have shown that increased total number of

born piglets is followed by an increased rate of mortality (Bille et al., 1974; Roehe and


Fig. 7. Relation between nest building behaviour and lateral lying.

Fig. 8. Effect of farrowing start (BFP) (left) and temperature (Temp1) (right) on mortality due to no milk in the

stomach (NoMilk). The solid line indicates expected level. The dotted lines indicate 0.95 confidence interval. The

rug indicates observed values.

Kalm, 2000; Su et al., 2004). The present study further demonstrates that also behavioural

variables describing prepartum nesting behaviour and progress of parturition are

influenced by litter size. With increasing litter size sows performed more prepartum

nesting, had shorter but more variable inter-birth intervals and more partal–postpartal

lateral lying. The increase in prepartum nesting in sows with large litters may be a

consequence of stronger physiological signals in large litters than in small litters. This is

supported by Castren et al. (1993), who found negative correlations between litter size and

plasma concentrations of somatostatin during the last 3 days prepartum as well as between

plasma somatostatin and the time spent carrying straw on day �2 in relation to farrowing

(i.e. the period from 48 to 24 h before birth of the first piglet). The decreased inter-birth

interval may also be explained by increased physiological signals from the unborn piglets.

The birth of a piglet stimulates a pulsatile release of oxytocin in response to the mechanical

stimulation of the birth canal (Gilbert et al., 1994). Oxytocin release in response to the new

born piglets’ stimulation of the mammary glands may also be seen (Castren, 1993). Such

mechanisms may contribute to the decreased inter-birth intervals but do not explain the

increased variability in the inter-birth interval also seen in larger litters. Thus, a more

complex relationship between litter size, physiological signals and birth progress may exist

that on one hand speeds up the birth process but one the other hand also induces more

variability. The greater stimulation from large litters of the mammary glands may motivate

the sows for nursing, thus explaining the prolonged postpartum lateral lying.

The final graphical model (Fig. 2) of the present study showed that the four different

causes of death separated into three categories that each were related to different

behavioural variables. The three groups were unrelated to each other indicating that

different causal relationships may be involved in each cause of death. An exception,

however, was stillbirth and other causes, that both were related to behavioural variables

describing early suckling activity by the piglets.

Crushing (with or without lack of colostrums) was the most common cause of death

among the liveborn piglets. Crushing was related to lateral lying before parturition. Sows

that were in lateral recumbency (without standing or sitting) in most 15 min periods during

the last 4 h before parturition started, crushed more piglets after farrowing. In fact, sows

with low crushing only had few 15 min periods with constant lateral lying during the last

4 h before birth. The most obvious explanation is that extensive lateral lying is caused by

illness, bad leg constitution or obesity in the sow. These conditions may make movement

less frequent, but also make the few movements that occur highly dangerous for the piglets,

either because the sow is less motivated for carefulness when changing posture, or because

her ability to control the movements is reduced (see Damm et al., 2005b). Illness may also

cause lack of milk making the piglets less viable and thereby more at the risk of being

crushed. However, several aspects of the study design and results contradict this. Firstly,

sows with poor leg constitution and extreme body conditions were excluded from the study.

Secondly, there was no association between prepartum and partal–postpartal lying. This

would have been expected, as the mentioned conditions (leg problems and extreme body

condition) are not relieved simply through giving birth. Finally, there was no association

between prepartum lateral lying and body temperature, which would also have been

expected if lateral lying was caused by illness. A more likely explanation may therefore be

that prepartum lateral lying reflects general passivity. The passivity was related to low


nesting activity in the present study and may also be related to low postpartum motivation

for behavioural patterns that make postural changes safer for the piglets, e.g. pre-lying

behaviour (Clough and Baxter, 1984; Marchant et al., 2001) and responsiveness to screams

of piglets trapped underneath the sow’s body (Wechsler and Hegglin, 1997; Janczak et al.,

2003). Therefore, in solving the problem of piglet crushing it might be advantageous to

focus on stimulating sows to change posture safely rather than on mere reduction of the

occurrence of postural changes. One way may be through the provision of nesting materials

and the succeeding feed-back from a completed nest (Herskin et al., 1998, 1999; Pedersen

et al., 2003).

It has previously been shown that piglets are crushed when the sow rolls or lies down

from standing (Weary et al., 1996, 1998; Marchant et al., 2001). This would suggest that

much partal–postpartal lateral lying would be beneficial for piglet survival (Jarvis et al.,

1999; Pedersen et al., 2003) due to reduced number of postural changes. However, in the

present study no such link was found between crushing and partal–postpartal lateral lying,

which support our previous suggestion of focusing on making postural changes safer rather

than on reducing their frequency. Neither pre-partal nor partal–postpartal nesting activity

was related to partal–postpartal lateral lying, whereas partal–postpartal lateral lying was

related to the piglets’ suckling activity. Thus, the results suggests that partal–postpartal

lateral lying is mainly a reflection of piglets suckling activity or vice verse and not related

to pre-partal passivity.

In the present study, a great proportion of the crushed piglets were dead without

ingestion of colostrum and similar results have been found in previous studies (see

Edwards (2002) for review). Furthermore, Weary et al. (1996) has shown that starving

piglets are more at risk of being crushed. This suggests a link between crushing and piglet

suckling activities. The lack of relationship between crushing and measures of piglet

viability in the present data, may be explained by the fact that death due to other causes also

involved piglets culled by the stock person due to pure growth. Thus, some of the piglets

that suffered from starvation and therefore were in risk of being crushed may have been

culled by the stock person and therefore not crushed later by the sow. This may have

weakened the expected link between piglet viability and crushing and strengthened the link

between piglet viability and death due to other causes.

Low piglet activities were solely related to litters with much Stillbirth and with many

deaths due to other causes. In turn, stillbirth was related to variation in the inter-birth

interval, which suggests that liveborn piglets in litters suffering from much stillbirth were

subjected to hypoxia during birth due to the occurrence of some long birth intervals (Fraser

et al., 1997). Thus, birth problems may render liveborn piglets less viable after birth and

more prone to death due to other causes (Herpin et al., 1996; Leenhouwers et al., 1999).

Therefore, the consequences of problematic births could be viewed as a continuum with

stillbirth as the most extreme outcome and varying degrees of reduced viability and vitality

as less extreme outcome. A high occurrence of stillbirth might therefore be used as an

indicator of litters with particular risk of death amongst the liveborn and hence of litters

requiring extra attention and care.

Death due to lack of colostrum ingestion was more common when parturition started in

the morning than when it started in the evening. There was on the contrary, no link to

suckling activities, suggesting that piglets dying from lack of colostrum in general come


from healthy viable litters. Thus, the probability that management was involved rather than

piglet viability seems therefore likely. Certain management procedures may be carried out

sooner in relation to birth when parturition starts in the morning rather than in the evening

or nights. Piglets may be cross-fostered sooner, possibly before having obtained adequate

amounts of colostrum. Piglets may also be isolated in the piglet creep area during the first

morning feeding with the intention of providing them with heat and securing that they are

not crushed, but at the same time preventing their colostrum intake. Such morning

procedures were unsystematically observed in all herds during the video analysis of the

present study. No studies have investigated the effects and timing of such procedures on

piglet survival, and controlled experimental studies relating the procedures to piglet

mortality needs to be carried out.

Sows with a high percentage of piglets dying due to lack of colostrum ingestion also had

higher rectal temperature days 1 and 2 after parturition. These sows may have suffered

from the agalactia that is part of the MMA (Mastitis–Metritis–Agalactia) syndrome

(Furniss, 1987). Rectal temperature on the day of parturition was not associated with

starvation of the piglets, but with nesting before parturition. Temperature at this time

probably reflects the physical strain of nesting and giving birth, and not yet any developing

disease.

The approach of using chain-graph models to analyse multivariate data in an

exploratory phase, to identify potential causal mechanisms that may later be investigated in

control experimental designs, turned out to be valuable. The major advantage over the more

commonly used factor analysis, was that the chain-graph models allowed estimation of

quantitative relationships between original variables that would not have been possible

using factor analysis. Finally, it should be mentioned that the present analysis only

investigated the association between variables at the sow/litter level of the hierarchy.

Similar analysis could have been performed on the herd and batch level. For example, the

estimates of the batch values from the random component of each of the linear models,

could have been analysed using the MIM-program, to identify the structure of the

covariance in batch effects. However, to obtain consistent estimates, this would require a

larger dataset than the present one.

5. Conclusion

In conclusion, the analysis showed that different causes of mortality were linked to

different behaviour variables during the periparturient period and that they grouped into

three categories. The first category was associated with stillbirth, which was linked to the

progress of parturition and to piglet viability/suckling activity. The second category was

associated with piglet crushing, which was linked to lateral lying and nesting activity

before parturition. And finally, the third category was associated with starvation, which

was linked to the time of parturition and sow rectal temperature on days 1 and 2 after

farrowing. The results further showed that litter size not only influenced mortality but also

most behavioural variables, even those occurring before parturition.

The study points towards several areas of interest for further research that can help

understand early piglet mortality. These include causes of variation in the progress of


parturition, causes of individual differences in sow activity and responsiveness to piglets,

and early management of farrowing.

In addition, the study suggests possible indicators of problematic farrowings rendering

liveborn piglets at risk. These include prepartum lateral lying in the sow, suckling

behaviour and rate of stillbirth.

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Early piglet mortality in loose-housed sows related to sow and piglet behaviour and to the progress of parturition