Schriftenreihe des Instituts für Tierzucht und Tierhaltung der Christian-Albrechts-Universität zu Kiel, Heft 226, 2019
©2019 Selbstverlag des Instituts für Tierzucht und Tierhaltung der Christian-Albrechts-Universität zu Kiel
Olshausenstraße 40, 24098 Kiel Schriftleitung: Prof. Dr. J. Krieter ISSN: 0720-4272
Gedruckt mit Genehmigung des Dekans der Agrar- und Ernährungswissen- schaftlichen Fakultät der Christian-Albrechts-Universität zu Kiel
Aus dem Institut für Tierschutz und Tierhaltung im Friedrich-Loeffler-Institut in Celle und dem Institut für Tierzucht und Tierhaltung
der Agrar- und Ernährungswissenschaftlichen Fakultät der Christian-Albrechts-Universität zu Kiel
Reducing tail biting in German weaner pigs –
Risk factor identification and prevention using a management tool
Dissertation
zur Erlangung des Doktorgrades
der Agrar- und Ernährungswissenschaftlichen Fakultät der Christian-Albrechts-Universität zu Kiel
vorgelegt von Master of Science Angelika Karin Grümpel
aus Fürth (Bayern)
Kiel, 2018
Dekan: Prof. Dr. Dr. Christian Henning
Erster Berichterstatter: Prof. Dr. Joachim Krieter Zweiter Berichterstatter: Prof. Dr. Lars Schrader Tag der mündlichen Prüfung: 23.01.2019
Die Förderung erfolgte dankenswerter Weise aus Mitteln des Zweckvermögens des Bundes bei der landwirtschaftlichen Rentenbank.
Die Software wurde dankenswerter Weise gefördert durch Tönnies Forschung - Gemeinnüt- zige Gesellschaft zur Förderung der Forschung über die Zukunft des Tierschutzes in der Nutz-
tierhaltung mbH.
“I am fond of pigs.
Dogs look up to us. Cats look down on us.
Pigs treat us as equals.”
Winston Churchill
TABLE OF CONTENTS
GENERAL INTRODUCTION
………. 1
CHAPTER ONE
Factors influencing the risk of tail lesions in weaner pigs (Sus scrofa)
………. 7
CHAPTER TWO
Reducing tail biting risk in German weaner pigs using a management tool
………. 27
CHAPTER THREE
Suitability of behavioural tests for predicting tail biting in growing-finishing pigs
………. 43
CHAPTER FOUR
Tail and ear lesions in suckling piglets: a pilot study on risk factors and a possible relationship with tail and ear lesions in weaner pigs
………. 59
GENERAL DISCUSSION
………. 73
GENERAL SUMMARY
………. 87
ZUSAMMENFASSUNG
………. 90
APPENDIX
………. 93
1
GENERAL INTRODUCTION
Tail biting in pigs is a widespread behavioural disorder (Schrøder-Petersen and Simonsen, 2001), which can affect pigs of all ages and in every housing system. Tail biting describes the oral manipulation of the tail of a pig by another pig. Different kinds of tail biting can be differentiated (Taylor et al., 2010). Besides obsessive tail biting, where one or a few pigs perform forceful biting, sudden forceful tail biting and two-stage tail biting can be listed.
Sudden forceful tail biting often occurs if other pigs block the access to resources like feed or water (Taylor et al., 2010). Two-stage tail biting starts with tail-in-mouth behaviour, where a pigs sucks on the tail without injuries and merges into biting due to blood leakage (Schrøder-Petersen et al., 2003). Tail biting results in tail lesions that range from tooth im- prints to injuries with or without inflammations (Schrøder-Petersen and Simonsen, 2001). In the worst case, the pig partly or completely loses the tail (Smulders et al., 2008). The conse- quences of tail biting are a decrease in the levels of welfare of the bitten pig due to pain and injuries. Additionally, tail biting is an indicator of stress for pigs, so the biters’ level of wel- fare is decreased as well (Schrøder-Petersen and Simonsen, 2001). For the farmer, tail biting is an economic problem because of a decrease of daily weight gain (Wallgren and Lindahl, 1996), for example, and an increase of medication costs (Sinisalo et al., 2012). Tail biting has a multifactorial character with factors ranging from internal to external factors (EFSA, 2014). Internal factors include beside others breed (Breuer et al., 2003), health status (Smulders et al., 2008) or sex of pigs (Zonderland et al., 2010). External factors include inter alia enrichment (Zonderland et al., 2008), housing characteristic (Oostindjer et al., 2011), feed content and management (Hunter et al., 2001; McIntyre and Edwards, 2002) or climatic conditions (Smulders et al., 2008).
A common strategy to reduce the risk for tail biting is the docking of tails, although tail docking is restricted by law (2008/120/EG; European Commission, 2008). Tail docking is a useful method to reduce tail biting (Paoli et al., 2016; Thodberg et al., 2018), although the background and the mechanism still are not fully examined. The reduction of tail biting can be due to a higher sensitivity in the stump, whereby a bitten pig does not tolerate biting.
Additionally a docked tail is harder to grasp by other pigs (Simonsen et al., 1991). Summa- rising tail docking reduces the occurrence of tail biting but it does not eliminate the under- lying influencing factors (Nannoni et al., 2014). Thus, the reduction of risk factors is the more useful and longer lasting strategy to reduce the risk of tail biting.
Due to the multifactorial character of tail biting, many factors must be considered for the reduction of tail biting. In addition to the large amount of risk factors, the factors strongly vary between farms. Thus, it is necessary to analyse the risks of tail biting farm individually.
For the farm-individual analysis and reduction of tail biting risk, the Friedrich-Loeffler-In- stitut (FLI) developed the tail biting intervention programme “SchwIP” (German abbrevia- tion of “Schwanzbeiß-Interventions-Programm”, Appendix). SchwIP adopted the tail biting husbandry advisory tool “HAT” (Taylor et al., 2012), which was developed to reduce the risk of tail biting in British finisher pigs. HAT followed the principles of animal health and welfare planning (AHWP). AHWP is a farm-specific cycle of evaluating the status of risk factors and outcomes followed by the planning and implementation of intervention actions
2
(Ivemeyer et al., 2012). SchwIP was initially developed for the assessment of fattening pigs.
In a follow-up study, SchwIP was adapted to the requirements of weaner pigs. The applica- tion of SchwIP on farm starts with an interview with the person responsible for the pigs (referred to as farmer below). During the interview data on the management, feeding and health of pigs are collected. Following during the barn assessment data on housing charac- teristics, feed and water are gathered. Additionally, the pigs are assessed for lesions and loss of tails and ears. The collected data are subsequently summarised in a farm-individual risk report that shows the present risk factors as well as risk factors that are currently managed in a preventive way. Based on the report the farmer is able to define custom-fit measures to reduce the tail biting risk on farms.
To avoid tail lesions, the prediction of tail biting outbreaks would be useful. As already mentioned, tail biting can be differentiated in three different types (Taylor et al., 2010). Two- stage biting as well as tail-in-mouth behaviour are caused by stress in pigs. Stress in pigs can be detected using behavioural tests, which in turn assess the affective state of pigs (Taylor et al., 2010). Other studies are concerned with the prediction of tail biting using behavioural indicators like increased explorative or activity behaviour (Statham et al., 2009; Zonderland et al., 2011; Larsen et al., 2016). In these studies, a significant relationship between an in- crease in behavioural patterns and tail biting outbreaks could be found. A hanging or pinched tail is also associated with a tail biting outbreak and can be used as prediction indicator (Lahrmann et al., 2018) However, the relationship between the behaviour in behavioural tests and tail biting outbreaks is not yet examined.
Besides studies dealing with risk factors of tail lesions and the reduction of tail biting risk in weaner pigs, there are few studies examining alterations in suckling piglets including lesions and necrosis on tails and ears (e.g. Lewis et al., 2006; van Nieuwamerongen et al., 2015).
There are hardly any studies, which deal with risk factors for lesions in suckling piglets, but several studies investigate the influence of mycotoxins on necrosis on tails and ears of suck- ling piglets (Dacasto et al., 1995; Dänicke et al., 2007; Van Limbergen et al., 2017). Never- theless, management, housing and health could have an influence on lesions on tails and ears of suckling piglets as well (Mouttotou et al., 1999; van Nieuwamerongen et al., 2015), which in turn can influence the occurrence of lesions in weaner pigs.
The first chapter investigates risk factors for the occurrence of tail lesions in weaner pigs.
Data were collected during the application of SchwIP on 25 farms, which were visited up to three times. Farms were managed in a conventional way and distributed throughout Ger- many. On all farms, pigs were housed in closed barns. Farms were advised concerning measures to reduce the risk of tail biting on farms after data collection. Furthermore, regres- sion tree analysis to analyse complex risk-factor data was tested.
In the second chapter, the effect of the application of SchwIP in weaner pigs on farms was assessed. SchwIP was applied on 21 farms for three times at regular intervals. The collected data were replaced by weightings, which were estimated by 61 experts. Using these stand- ardised weightings, risk sums per farm and visit were calculated and analysed concerning significant changes. Additionally, changes in risk factors with a strong influence on the var- iation in risk sums were analysed.
3
The third chapter examines the prediction of tail biting. Behavioural parameters, which were assessed in three behavioural tests, were set in relation with tail biting outbreaks. Over- all, 18 groups of pigs were tested during rearing and fattening phase up to twelve times in three tests, and additionally assessed for lesions on tails. Behaviour was recorded using di- rect observations and video recordings as well. The relationship between behavioural pa- rameters and tail biting outbreaks was analysed.
The fourth chapter deals with risk factors for the occurrence of tail and ear lesions in suck- ling piglets and their relationship to tail and ear lesions in weaner pigs. Additional data of suckling piglets were collected during the application of SchwIP on eight farms. Sows were housed for farrowing in pens with fixation of them on all farms. Suckling piglets and weaner pigs were assessed at the same day. Risk factors analysis was done using again regression tree analysis.
Tail biting often occurs in German weaner pigs. A common method to reduce the risk is tail docking, which is as well an obvious intervention in the pigs’ welfare and thus not a suitable solution. Therefore, risk factors as well as methods to reduce tail biting must be determined.
The aim of this thesis was to find risk factors for tail lesions and to examine methods to reduce tail biting in weaner pigs.
4 REFERENCES
Breuer, K., Sutcliffe, M.E.M., Mercer, J.T., Rance, K.A., Beattie, V.E., Sneddon, I.A., Edwards, S.A., 2003. The effect of breed on the development of adverse social behaviours in pigs. Applied Animal Behaviour Science 84, 59-74.
Dacasto, M., Rolando, P., Nachtmann, C., Ceppa, L., Nebbia, C., 1995. Zearalenone mycotoxicosis in piglets suckling sows fed contaminated grain. Veterinary and Human Toxicology 37, 359-361.
Dänicke, S., Brüssow, K.P., Goyarts, T., Valenta, H., Ueberschär, K.H., Tiemann, U., 2007. On the transfer of the Fusarium toxins deoxynivalenol (DON) and zearalenone (ZON) from the sow to the full-term piglet during the last third of gestation. Food and Chemical Toxicology 45, 1565-1574.
EFSA, 2014. Scientific Opinion concerning a Multifactorial approach on the use of animal and non‐animal‐based measures to assess the welfare of pigs. EFSA Journal 12.
European Commission, 2008. Richtlinie 2008/120/EG des Rates vom 18. Dezember 2008 über Mindestanforderungen für den Schutz von Schweinen. Amtsblatt der
Europäischen Union L47, 18/0272009, 7 - 9.
Hunter, E.J., Jones, T.A., Guise, H.J., Penny, R.H.C., Hoste, S., 2001. The Relationship between Tail Biting in Pigs, Docking Procedure and Other Management Practices.
The Veterinary Journal 161, 72-79.
Ivemeyer, S., Smolders, G., Brinkmann, J., Gratzer, E., Hansen, B., Henriksen, B.I.F., Huber, J., Leeb, C., March, S., Mejdell, C., Nicholas, P., Roderick, S., Stöger, E., Vaarst, M., Whistance, L.K., Winckler, C., Walkenhorst, M., 2012. Impact of animal health and welfare planning on medicine use, herd health and production in European organic dairy farms. Livestock Science 145, 63-72.
Lahrmann, H.P., Hansen, C.F., D'Eath, R., Busch, M.E., Forkman, B., 2018. Tail posture predicts tail biting outbreaks at pen level in weaner pigs. Applied Animal
Behaviour Science 200, 29-35.
Larsen, M.L.V., Andersen, H.M.L., Pedersen, L.J., 2016. Can tail damage outbreaks in the pig be predicted by behavioural change? The Veterinary Journal 209, 50-56.
Lewis, E., Boyle, L.A., O’Doherty, J.V., Lynch, P.B., Brophy, P., 2006. The effect of providing shredded paper or ropes to piglets in farrowing crates on their behaviour and health and the behaviour and health of their dams. Applied Animal Behaviour Science 96, 1-17.
McIntyre, J., Edwards, S.A., 2002. An investigation into the effect of different protein and energy intakes on model tail chewing behaviour of growing pigs. Applied Animal Behaviour Science 77, 93-104.
Mouttotou, N., Hatchell, F.M., Green, L.E., 1999. The prevalence and risk factors associated with forelimb skin abrasions and sole bruising in preweaning piglets.
Preventive Veterinary Medicine 39, 231-245.
5
Nannoni, E., Valsami, T., Sardi, L., Martelli, G., 2014. Tail docking in pigs: A review on its short- and long-term consequences and effectiveness in preventing tail biting.
Italian Journal of Animal Science 13, 98-106.
Oostindjer, M., van den Brand, H., Kemp, B., Bolhuis, J.E., 2011. Effects of environmental enrichment and loose housing of lactating sows on piglet behaviour before and after weaning. Applied Animal Behaviour Science 134, 31-41.
Paoli, M.A., Lahrmann, H.P., Jensen, T., D'Eath, R.B., 2016. Behavioural differences between weaner pigs with intact and docked tails. Animal Welfare 25, 287-296.
Schrøder-Petersen, D.L., Simonsen, H.B., 2001. Tail biting in pigs. The Veterinary Journal 162, 196 - 210.
Schrøder-Petersen, D.L., Simonsen, H.B., Lawson, L.G., 2003. Tail-in-mouth behaviour among weaner pigs in relation to age, gender and group composition regarding gender. Acta Agriculturae Scandinavica - Section A: Animal Science 53, 29-34.
Simonsen, H.B., Klinken, L., Bindseil, E., 1991. Histopathology of intact and docked pigtails. British Veterinary Journal 147, 407-412.
Sinisalo, A., Niemi, J.K., Heinonen, M., Valros, A., 2012. Tail biting and production performance in fattening pigs. Livestock Science 143, 220-225.
Smulders, D., Hautekiet, V., Verbeke, G., Geers, R., 2008. Tail and ear biting lesions in pigs: an epidemiological study. Animal Welfare 17, 61 - 69.
Statham, P., Green, L., Bichard, M., Mendl, M., 2009. Predicting tail-biting from
behaviour of pigs prior to outbreaks. Applied Animal Behaviour Science 121, 157- 164.
Taylor, N.R., Main, D.C.J., Mendl, M., Edwards, S.A., 2010. Tail-biting: A new perspective. The Veterinary Journal 186, 137-147.
Taylor, N.R., Parker, R.M.A., Mendl, M., Edwards, S.A., Main, D.C.J., 2012. Prevalence of risk factors for tail biting on commercial farms and intervention strategies. The Veterinary Journal 194, 77-83.
Thodberg, K., Herskin, M.S., Jensen, T., Jensen, K.H., 2018. The effect of docking length on the risk of tail biting, tail-directed behaviour, aggression and activity level of growing pigs kept under commercial conditions. Animal 12, 2609-2618.
Van Limbergen, T., Devreese, M., Croubels, S., Broekaert, N., Michiels, A., De Saeger, S., Maes, D., 2017. Role of mycotoxins in herds with and without problems with tail necrosis in neonatal pigs. Veterinary Record 181, 539.
van Nieuwamerongen, S.E., Soede, N.M., C.M.C, P.-S., Kemp, B., Bolhuis, J., 2015.
Development of piglets raised in a new multi-litter housing system vs. conventional single-litter housing until 9 weeks of age. Journal of Animal Science 93, 5442- 5454.
Wallgren, P., Lindahl, E., 1996. The influence of tail biting on performance of fattening pigs. Acta veterinaria Scandinavica 37, 453-460.
6
Zonderland, J.J., Bracke, M.B.M., den Hartog, L.A., Kemp, B., Spoolder, H.A.M., 2010.
Gender effects on tail damage development in single- or mixed-sex groups of weaned piglets. Livestock Science 129, 151-158.
Zonderland, J.J., Schepers, F., Bracke, M.B.M., den Hartog, L.A., Kemp, B., Spoolder, H.A.M., 2011. Characteristics of biter and victim piglets apparent before a tail- biting outbreak. Animal 5, 767-775.
Zonderland, J.J., Wolthuis-Fillerup, M., van Reenen, C.G., Bracke, M.B.M., Kemp, B., Hartog, L.A.d., Spoolder, H.A.M., 2008. Prevention and treatment of tail biting in weaned piglets. Applied Animal Behaviour Science 110, 269-281.
7
CHAPTER ONE
Factors influencing the risk of tail lesions in weaner pigs (Sus scrofa)
Angelika Grümpela, Joachim Krieterb, Christina Veita, Sabine Dippela
a Institute of Animal Welfare and Animal Husbandry, Friedrich-Loeffler-Institut, Dörnbergstraße 25/27, 29223 Celle, Germany
b Institue of Animal Breeding and Husbandry, Christian-Albrechts-University, Hermann-Rodewald-Straße 6, 24098 Kiel, Germany
Published in Livestock Science, 2018, 216: 219 – 226
8 ABSTRACT
Tail biting is a behavioural disorder in pigs, which results in tail lesions. Many factors must be considered to reduce the risk for tail biting due to the multifactorial character of this behaviour. We developed a software-based tail biting management tool called “SchwIP” for analysing farm individual risk factors for tail biting in weaner pigs. SchwIP was applied on 25 conventional farms throughout Germany who kept weaner pigs in closed barns (median 1,800 weaning places). The farms were visited up to three times between August 2016 and November 2017 and a total of 368 pens were assessed. Data regarding enrichment, pen en- vironment, feed, water, climate, health, farm management, transport and regrouping were analysed with regression tree analysis (RT) using pen level prevalence of tail lesions (%) as the outcome variable. There were five primary influencing factors for tail lesions: docking status, stocking density, daily weight gain, suckling piglet losses and number of litters mixed during weaning. The correlation between observed and predicted prevalence of tail lesions across all pens was 0.6. Most of the factors may represent combinations of influences on a farm, which agree with the multifactorial nature of the problem. Even though weight gain may also be influenced by tail biting behaviour and thus be a parallel outcome, it could be used by farmers as an indicator for initiating closer examination and intervention. The use of RT for visualising complex risk factor analyses is recommendable, though their analytical suitability for clustered data should further be evaluated.
Keywords
Pig, Tail lesions, Tail biting, Regression tree, CART
INTRODUCTION
Tail lesions result mostly from tail biting. Tail biting is a behavioural disorder in pigs, which can occur in pigs of all ages and in any kind of production system. It is a multifactorial problem and risk factors range from internal factors such as health status (Smulders et al., 2008), breed (Breuer et al., 2003) or sex of the pigs in a pen (Zonderland et al., 2010), to external factors, for example management and housing characteristics (Oostindjer et al., 2011), feed content and management (Hunter et al., 2001; McIntyre and Edwards, 2002) or climatic conditions (Smulders et al., 2008).
Many different factors must be taken into account in order to determine the relative influence of risk factors on a multifactorial outcome, which necessitates large datasets with observa- tions from many farms. Currently, there are only few publications with large sets of on-farm data for tail lesions (Moinard et al., 2003; Smulders et al., 2008; Scollo et al., 2017).
Correct analysis of large numbers of risk factors needs to take into account potential collin- earity between factors as well as the often suboptimal distribution of data. In addition, data are often clustered, because several pigs in a pen and several pens on a farm are observed.
In many studies, linear mixed models (Smulders et al., 2006; Smulders et al., 2008) like logistic regression models (Moinard et al., 2003; Taylor et al., 2012) were used for analysing
9
risk factors for tail or ear lesions, with (Scollo et al., 2016) or without (Moinard et al., 2003;
Smulders et al., 2008) stepwise factor selection. Though linear models have frequently been used for analysing risk factors of multifactorial problems, they may not be the best approach.
Linear models often become unstable if they contain many independent factors which are context dependent (Durst and Roth, 2012) and data often struggle to meet the requirement of normal distribution of residuals (De'Ath and Fabricius, 2000). In a recent publication, Scollo et al. (2017) used classification and regression trees (CART) for analysing risk factors for tail lesions in heavy Italian finishing pigs on 60 farms. CART were recommended by EFSA (2014) for risk factor analysis for tail lesions because they are better at integrating potential interrelationships between factors. They have previously successfully been used for risk factor analyses in human medicine (Hess et al., 1999; Navarro Silvera et al., 2014), ecology (Ruesink, 2005) or bee farming (Van Engelsdorp et al., 2010).
The aim of the present study was to identify risk factors for tail lesions in German weaner pigs using CART.
MATERIAL AND METHODS Farm characteristics
Weaner pig rearing farms were recruited through advertisements in journals and on relevant websites without criteria for participation. Farms were distributed throughout Germany and visited up to three times within 18 months. The first visit included 27 farms, which were visited between August and November 2016. Twenty-four out of the 27 farms were visited a second time between February and May 2017, and 22 farms were visited a third time be- tween August and November 2017. Farms not visited again cancelled participation for vari- ous reasons. Two farms with open barns were not used for analysis because they differed considerably from the other farms regarding climatic condition, pen structure and group size.
Hence, the presented analysis includes data from 25 farms.
The majority of farms (15 out of 25) were farrow-to-finish farms, 5 farms were farrow-to- wean, 3 farms were wean-to-finish, and 2 farms were rearing weaners only. The median number of weaner places per farm was 1,800 (120 | 15,000). The median number of pigs per pen was 25 (8 | 170), and on average 25 (8 | 57) pigs were scored per pen. Sixteen farms kept docked pigs only, five farms kept docked as well as undocked pigs and four farms kept undocked pigs only. In 310 pens distributed across 21 farms, pigs were tail docked, in 2 pens on one farm docked and undocked pigs were housed together, and in 56 pens distributed across nine farms the pigs were undocked. All farms had fully ventilated barns with fully slatted flooring in 355 and partly slatted flooring in 13 of the assessed pens.
Data collection
Data for this publication were collected in the course of a project on reducing tail biting risk in weaner pigs on German pig farms using the tail biting management tool SchwIP (German abbreviation for “Schwanzbeiß-Interventions-Programm”). SchwIP is a software-based
10
management tool derived from the tail biting husbandry advisory tool (HAT) by Taylor et al. (2012), which had been adapted to German growing-finishing pig conditions. In the cur- rent project (project A-SchwIP), SchwIP was adapted for weaner pigs by including factors specific for the age group and testing it on farms.
A farm assessment with SchwIP took one day (4 to 6 hours) and included data collection through an interview and barn assessment as well as discussing the data with the person responsible for managing the weaner pigs on the farm (referred to as farmer below). The interview included questions on productivity, feed content and management data, while the barn assessment focussed on the current housing conditions and state of the weaner pigs.
Data were collected at farm, barn (building), room and pen level. The discussion was based on a farm specific risk report and concludes with an intervention plan drawn up by the farmer. Farm visits were repeated at regular intervals to promote improvement.
Barn, room and pen selection was problem-based because the primary aim of the SchwIP assessments was to provide management help for reducing tail biting risk on farm. Therefore, preference was given to pens for which the farmer reported current or recurring tail lesion problems. Overall, a median number of 6 (minimum 2 | maximum 12) pens, 3 (1 | 6) rooms, and 1 (1 | 3) barn were assessed per farm. On 71 % of farms, 4 to 6 pens were assessed. In each pen, 30 pigs were scored for presence or absence of any tail lesions. If there were less than 30 pigs in the pen, all pigs were scored. If there were more than 30 pigs in the pen, a random sample was scored. The animal sampling strategy was the outcome of discussions with farm advisers and veterinarians (the focus group of the SchwIP management tool) and based on sampling strategies by Welfare Quality ® (Welfare Quality® consortium, 2009) and “Real Welfare” (Pandolfi et al., 2017). A pig was counted as having tail lesions, if any scratches, wounds, necrosis or swelling could be seen from a distance of 1 m. Loss of tail length was also recorded independently of tail lesions. However, it was excluded from anal- ysis because it was not clear in many pens, whether differences in tail length were due to tail biting or irregular tail docking. Before a pen was entered, manipulative behaviour was ob- served using scan sampling of not lying pigs. The results were summarised for analysis as the mathematical difference between the number of pigs that manipulated enrichment or pen surroundings and the number of pigs that manipulated other pigs. The first author assessed all data except for barn climate measurements and drinker flow rates, which were done by a technician.
Data management
A total of 14 factors were calculated out of recorded data (e.g. temperature-humidity-index, a value expressing the combined effect of air temperature and relative humidity on health and comfort, calculated based on formula of National Research Council (U.S.) (1971)). The data were checked for data entry errors and these were corrected based on related information if possible or else deleted (n = 1 pen). In addition, analysis was restricted to factors with a sufficient amount of observations and suitable distribution to allow sound analysis. Only factors with < 20 % of all observations missing and sufficient distribution of data were re- tained for analysis. For checking the distribution of categorical factors, the proportion of
11
answers in each answer category was calculated. If a category contained < 20 % of observa- tions, answer categories were merged if this was reasonable. If distribution could not be improved through merging, the factor was excluded from analysis. Many factors had to be dropped because over 90 % of observations were in one answer category. Distribution of continuous factors was checked graphically and by calculating distribution measures (inter- quartile range in relation to the first and third quartile). Factors with skewed or distorted distribution were categorised based on reasonable cut-offs and then subjected to the distri- bution check described for categorical factors. Out of approximately 200 factors recorded on each farm, 53 factors from 10 different areas of influence were left for the regression tree analysis (Table 1, Table 2): pen environment (3 factors), enrichment (4), farm / management (8), diet / water / feeding (12), climate (5), performance (5), suckling piglets / weaning (2), behavioural indicators (1), transport / regrouping (6) and health / hygiene (7).
Table 1 Overview of continuous factors used for RT. Area describes the corresponding risk area, level describes whether a factor is assessed on farm, barn, room or pen level. The cal- culations for median, minimum, maximum, q25 and q75 are based on all 368 pens.
level area factor me-
dian
min max q25 q75 miss- ing n farm farm /
manage- ment
number of sows on farm 390 28 4800 170 520 9
number of weaning places on farm
1600 90 15000 720 2300 0
perfor- mance
weight difference of pigs at weaning (% of mean weaning weight)
50 20 111.7 43.8 61.6 3
daily weight gain (g/d; mean of last six months)
460 360 760 410 490 5
total number of piglets born per litter (alive and dead; mean of last six months)
15.8 13.4 20.5 15.1 16.7 9 suckling piglet losses (% of to-
tal born piglets, i.e. live born plus still born; mean of last six months)
20.3 11.3 33.7 17.9 23.8 9
suckling piglets / weaning
age of piglets at weaning (d) 26 18 28 24 28 0
transport / re- grouping
number of litters mixed at weaning
10 2 100 5.5 25 12
transport distance after wean- ing (km)
0.1 0.001 120 0.05 1 0
pen behav- ioural in- dicators
(no. of pig manipulating en- richment or pen surroundings) - (no. of pigs manipulating other pigs)
1 -8 17 0 3 18
climate temperature humidity index 73.2 59.4 85.1 71.0 75.5 25 carbon dioxide concentration
(Vol%)
0.2 0 0.5 0.1 0.3 58
ammonia concentration (ppm) 13 0 80 7 25 58
enrich- ment
number of pigs that simultane- ously have access to enrich- ment (%; subjective estimate)
30 0 100 15 40 2
pen en- viron- ment
stocking density (kg/m2) 38.0 11.3 99.6 28.5 45.8 0
12
Table 2 Overview of categorical factors used for RT. Area describes the corresponding risk area, level describes whether a factor was assessed at farm, barn, room or pen level. n is the number of observations at the respective level, % the percentage of chosen answers, and missing n describes the number of missing answers per factor.
level area factor answer categories n % missing
n farm diet /
water / feeding
source of water municipal water 30 44.1
well with water treatment 18 26.5 0 well without water treatment 20 29.4
animal protein in feed yes 24 42.1
no 33 57.9 11
feed analysis during last 12 months
yes 16 23.9
no / partly 51 76.1 1
structure of feed grist / flour 34 50.0
pellets / crumb / granulate 34 50.0 0 blending feed over more
than five days
yes 50 74.6
no 17 25.4 1
number of rations dur- ing weaning (prestarter not included)
2 rations 37 54.4
> 2 rations 31 45.6 0
feeding system dry feeding 20 29.4
liquid or wet feeding 48 70.6 0 water analysis during
last 12 months
yes 39 57.4
no/partly 29 42.7 0
enrich- ment
amount of loose organic enrichment per serving
≥ 1 handful 25 41.7
none 35 58.3 8
farm / man- agement
animals on farm weaners and others, no fatteners 21 30.9 weaners and fatteners and oth- 0
ers
47 69.1 animals sold from farm weaner pigs, sows, piglets 18 26.5 slaughter pigs, sows, piglets 28 41.2 0 weaner pigs and fatteners 22 32.4 group sizes on farm ≤ 50 pigs per group 46 67.7
> 50 pigs per group, or ≤ 50 as 0 well as > 50 pigs per group
22 32.4 care for pigs at work
peaks
measures are taken later 19 27.9
unchanged 49 72.1 0
separation pen on farm yes 56 76.7
no 17 23.3 4
health / hygiene
rooms empty after cleaning / disinfection
≤ 1 day 24 36.9
2 days 14 21.5 3
≥ 3 days 27 41.5
deworming of pigs irregularly 13 20.0
only sows regularly 33 50.8 3 weaners regularly, or weaners
and sows regularly
19 29.2
13 Table 2 (continued)
level area factor answer categories n % missing
n farm health /
hygiene
health checks for wean- ers during last six months
yes 44 64.7
no 24 35.3 0
suckling piglet health problems
yes / partly 39 63.9
no 22 36.1 7
perfor- mance
average duration of rearing
> 6 weeks 47 71.2 2
< 6 weeks / > 6 weeks 19 28.8 suckling
piglets / weaning
additional trough after weaning
for feed and / or water 53 77.9 0
no extra trough 15 22.1
transport / re- group- ing
type of transport after weaning
pigs walk 26 38.2 0
loaded (e.g. on trailer) 42 61.8 number of regrouping
or splitting of groups during rearing
none 45 66.2 0
≥ 1 23 33.8
being moved to new pen during rearing
at least once 17 25.0 0
not moved 51 75.0
remaining in farrowing pen after weaning
yes 16 26.2 7
no 45 73.8
room climate prevention of direct sun light in pen
yes 41 25.5 38
no 120 74.5
pen climate irritation of eyes or lung due to bad air
yes 152 41.3 0
no 216 58.7
diet / water / feeding
sufficient number of drinkers for number of pigs per pen
≤ 10 pigs per drinker 273 74.2 0
> 10 pigs per drinker 95 25.8
feeding system slop feeding or different 191 51.9 0
wet feeding 75 20.4
dry feeding 102 27.7
drinkers in pen only nipple drinkers 149 40.5 0
trough, bowl or similar with / without nipple drinkers
219 59.5
drinkers technically OK yes 227 61.7 0
no 141 38.3
enrich- ment
type of inorganic en- richment
plastic object and combinations 140 40.6 23 feed chain and combinations 116 33.6
others 89 25.8
type of organic enrich- ment
loose: hay, straw, silage etc. 104 34.2 64 solid objects: rope, wood etc. 200 65.8
farm / manage- ment
sex one sex only 159 43.2 0
mixed sexes 209 56.8
14 Table 2 (continued)
level area factor answer categories n % missing
n pen health /
hygiene
docking status undocked 105 28.8 3
docked 260 71.2
indications for respira- tory problems
yes 156 42.4 0
no 212 57.6
size of pigs in pen var- ies > 20 %
yes 109 29.6 0
no 259 70.4
pen en- viron- ment
cover over lying area cover 126 34.6 4
no cover 238 65.4
lying area structurally separated from activity area
yes 146 40.2 5
no 217 59.8
Regression tree analysis (RT)
The pen level prevalence of tail lesions (%) was the outcome for a regression tree analysis (RT) in the packages rpart (Therneau et al., 2017) and rpartplot (Milborrow, 2017) in R 3.4 (R Core Team, 2017). Explanatory variables (factors) were continuous (15 factors; Table 1) as well as categorical (38 factors; Table 2).
RT starts with a root node, which contains all observations. In order to build the tree, the dataset is split successively into subsets (child nodes) with the aim of maximizing the heter- ogeneity between child nodes (Protopopoff et al., 2009; Van Engelsdorp et al., 2010;
Henrard et al., 2015). Splits always result in two child nodes; hence, for splitting categorical variables with more than two categories, categories are combined in a way, which maximizes homogeneity within the nodes (Speybroeck, 2012; Henrard et al., 2015). For splitting con- tinuous variables, all values of the variable are evaluated regarding the best cut off with most homogenous child nodes (Speybroeck, 2012; Henrard et al., 2015). The explanatory varia- bles selected for splitting are called primary splitters. If a value of the primary splitter is missing, a surrogate variable is used to classify the missing observation. Surrogate variables and primary splitters have a similar distribution relative to the outcome and maximise the heterogeneity between child nodes in a similar way (Van Engelsdorp et al., 2010). This means they are mathematically related (collinear; Henrard et al., 2015) but not necessarily logically associated. The agreement value (range 0 to 1) indicates how well the primary splitter is replaced by the surrogate, with a higher value indicating better fit. The splitting ends in so-called terminal nodes.
The package rpart performs a 5 fold cross-validation of the tree during RT as recommended by Breiman et al. (1984). In order to do this, the dataset is randomly divided in five subsets of almost equal parts (73 or 74 observations per subset) for cross validation. Four subsets are used to build the largest possible tree, which is tested against the remaining subset in order to detect the error rate of the selected tree. This is repeated with random combinations of four subsets, until each subset has been used as validation subset (i.e. five runs in total).
Error rates from the subset tests are used for pruning the tree, which is built by using the
15
whole dataset (Van Engelsdorp et al., 2010). Additionally, the minimum number of obser- vations per node was fixed at 5 % of all observations (n = 19) in order to avoid over-fitting.
An additional pruning using the complexity parameter was not performed due to a low num- ber of observations in relation to a large number of variables and the high variation in the dataset. The quality of the RT was assessed by calculating the correlation between the ob- served and predicted prevalences of tail lesions.
RESULTS
Risk factors for tail lesions
The median prevalence of tail lesions across all pens was 10 % (minimum 0 | maximum 100). Out of the 368 pens assessed, 259 contained at least one pig with tail lesions. At least one pen without any tail lesions was assessed on each farm, except for one farm, which kept a total of three groups of weaner pigs with 125 to 150 pigs per pen. Five factors out of 54 were selected as primary splitters during the tree building process: docking status, stocking density, daily weight gain, suckling piglet losses and number of litters mixed during wean- ing. The RT ended in seven terminal nodes with six splitting nodes (suckling piglet losses were used twice). Pens with undocked pigs (n = 56) or undocked pigs mixed with docked pigs (n = 2) had a higher prevalence of tail lesions (48 %) compared to pens, which housed docked pigs (15 %, n = 310; Figure 1). In pens with undocked pigs, prevalence of tail lesions was higher for pigs with daily weight gain < 470 g (71 %, n = 22) compared to pigs with a daily weight gain of ≥ 470 g per day (34 %, n = 36). The data of pens with docked animals were subsequently split up to four times. Pigs with lower stocking density (< 38 kg/m2) had a lower prevalence of tail lesions (8 %, n = 149) than pigs in pens with a stocking density
≥ 38 kg/m2 (22 %, n = 161). In pens with stocking density ≥ 38 kg/m2, prevalence of tail lesions was lower, if suckling piglet losses (% of total born piglets, i.e. live born plus still born) were < 18 % (12 %, n = 45) and higher, if suckling piglet losses were ≥ 18 % (26 %, n = 116). For pens with suckling piglet losses ≥ 18 %, prevalence of tail lesions was lower in pens in which ≥ 7.5 litters had been mixed (22 %, n = 94) than in pens in which < 7.5 litters had been mixed at weaning (41 %, n = 22). In the pens where ≥ 7.5 litters had been mixed, prevalence of tail lesions was lower if suckling piglet losses were ≥ 23 % (11 %, n = 34) compared to pens where suckling piglet losses were < 23 % (29 %, n = 60). The corre- lation between observed and predicted prevalence of tail lesions was r = 0.6.
16
Figure 1 Regression tree using prevalence of tail lesions per pen as outcome variable. Five explanatory variables were used as primary splitters. The upper number in the nodes repre- sents the prevalence of tail lesions (%), the lower number stands for the number of observa- tions left in the node. The tree was pruned by having at least 19 observations in each terminal node. The algorithm grouped two pens with docked as well as undocked pigs together with pens with undocked pigs only.
Surrogate variables
No surrogate variables were used for docking status and stocking density because there were no missing values. In undocked pigs, the distribution of tail lesion prevalences was similar for pens on farms with daily weight gain < 470 g/d vs. ≥ 470 g/d compared to farms with
≥ 650 vs. < 650 weaning places (agreement 0.81; Table 3). In docked pigs stocked at ≥ 38 kg/m2, daily weight gain as well as amount of loose organic enrichment per serving were used as surrogate variables for suckling piglet losses. Tail lesion prevalences were higher, if suckling piglet losses were ≥ 18 %, weight gain was < 490 g/d or ≥ 1 handful of loose or- ganic enrichment was provided. The total number of piglets born per litter as well as the amount of loose organic enrichment given per serving were used as surrogates for the num- ber of litters mixed at weaning, with tail lesion prevalence being higher if < 7.5 litters were mixed, litter size was < 14.7 piglets or no loose enrichment was given to the pigs. Lastly, weaning age was used as surrogate for suckling piglet losses in the lowest splitting node for docked pigs. Here, tail lesion prevalence was higher if suckling piglet losses were < 23 % or weaning age was < 25.3 days (Table 3).
17
Table 3 Surrogate variables used for primary splitters (Figure 1), which contained missing observations. Agreement explains how well the surrogate replaces the primary splitter. Num- ber of splits = number of observation which were split based on the surrogate variable.
primary splitter
miss- ing ob-
serva- tions
value pri- mary splitter
tail le- sion preva-
lence
surrogate value surro- gate
tail lesion preva-
lence (surro-
gate)
agree- ment surro- gate
num- ber of
splits
daily weight gain (g/d)
6 < 470 higher
number of weaning places on farm
≥ 650 higher 0.81 6
suckling piglet losses (%)
15 ≥ 18 higher
daily weight
gain (g/d) < 490 higher 0.79 13 amount of
loose organic enrichment per serving
≥ 1
handful higher 0.76 2
number of litters mixed
40 < 7.5 higher
total number of piglets born per litter
< 14.7 higher 0.88 30 amount of
loose organic enrichment per serving
none higher 0.79 10
suckling piglet losses (%)
10 < 23 higher weaning age < 25.3 higher 0.85 10
DISCUSSION
In the present study, risk factors for tail lesions in weaner pigs in 368 pens on 25 farms were identified using regression tree analysis. The factors docking status, stocking density, daily weight gain, suckling piglet losses and number of litters mixed during weaning were identi- fied as risk factors for tail lesions.
Dataset characteristics
The background of the A-SchwIP project was to advise farmers regarding tail biting using the management tool SchwIP. All farmers had volunteered for the project because they had problems with tail biting in their pens. No farm selection criteria were applied. Thus, data were collected on farms with very different organisation of management. As a result, farms were very similar regarding some factors (e.g. type of flooring), which consequently could not be analysed due to lack of variation. At the same time farms differed strongly in other factors such as number of weaning places or the frequency of regrouping during rearing.
The advisory background was the reason why pens were selected problem-based rather than randomly. Due to this approach, farm prevalences of tail lesions will have been overesti- mated and more pens with tail lesions than without assessed on most farms. Nevertheless, at least one pen without tail lesions was assessed on 24 of 25 farm so that basic requirements for analysis were fulfilled.
18
The unfavourable distribution of data was reflected in the only moderate correlation between predicted and observed tail lesion prevalence. Nevertheless, the five identified factors appear to play an important role as they were selected as primary splitters in all cross-validations.
Regression tree analysis
RT from CART can handle categorical as well as continuous explanatory variables (Klemelä et al., 2000) unlike many other regression trees, which use continuous variables only. Both RT and linear models can be strongly influenced by small changes in data (Prasad et al., 2006; Henrard et al., 2015). We chose RT over linear models because it better integrates collinearity and nonlinearities (De'Ath and Fabricius, 2000; Prasad et al., 2006; Protopopoff et al., 2009; Henrard et al., 2015) and has been recommended for complex and unbalanced data with missing values (Van Engelsdorp et al., 2010; Speybroeck, 2012; EFSA, 2014).
Even though the disadvantage of RT to split continuous variables into discrete categories needs to be considered (Henrard et al., 2015), we found RT to be a useful alternative to linear regression models. We had tried generalised linear mixed models, too, but results from those varied greatly depending on whether certain observations or factors were included or not, despite applying methods for avoiding collinearity. The results of RT were more repeatable than those from modelling and RT therefore seemed to be a more robust method.
We analysed all data at pen level, including farm level factors. This results in pseudo-repli- cation. Possible alternatives using RT would have been to calculate one tree for each level at which factors were assessed (farm, barn, room, pen) or one tree at farm level only. The first option would necessitate summarising tail lesion prevalences at each level, while the second option would additionally require summary of influencing factors at each level. De- pending on the summary method (mean, maximum etc.) summary values may differ, and in any case information is lost. On the other hand, the effects of pseudo-replication in our anal- ysis are reduced by firstly, four to six pens having been assessed on 71 % of the farms, and secondly, the 5-fold cross-validation during tree generation. We therefore concluded that analysis at pen-level would be the best solution under the circumstances, while being aware that the method of analysis is not optimal.
Factors influencing tail lesions
The strongest distinction was between docked and undocked pigs, with higher tail lesion prevalences in the latter. This is in line with other studies (Arey, 1991; Di Martino et al., 2015; Paoli et al., 2016; Thodberg et al., 2018) even though it is still not quite clear, why tail docking has such a strong effect. Theories include increased sensitivity of the tail stump and thus earlier defensive reaction by bitten pigs, as well as shorter tails being harder to be grasped (Simonsen et al., 1991). However, tail docking does not eliminate the underlying causes so that tail biting is not completely avoided (Nannoni et al., 2014). The present study included much fewer pens with undocked pigs than with docked pigs (n = 58 vs. 310), which might explain why more subsequent factors were identified for docked than for undocked pigs.
In undocked pigs in our study, the prevalence of tail lesions was lower in pens on farms where the pigs had higher daily weight gain. However, this might be a two-way relationship
19
as well as a proxy parameter. Pigs in pens with high levels of tail lesions (Veit et al., 2016) as well as victims of tail biting and other oral manipulations (Camerlink et al., 2012) have lower daily weight gain due to the disruption and pain caused by tail biting. On the other hand, weight gain is influenced by other factors, which may be stressful for the pigs, such as inadequate nutritional supply (Colyer, 1970; McIntyre and Edwards, 2002) or health prob- lems caused by PCV2 (Kristensen et al., 2011) or Mycoplasma hyopneumoniae (Jensen et al., 2002). Therefore, daily weight gain is related to the prevalence of tail lesions but the kind and direction of the relationship are not clear. But even if it is only a proxy parameter or a result of tail biting, it could be a useful indicator for farmers which is already recorded on many farms.
In docked pigs in our study, the prevalence of tail lesions was lower in pens with a lower stocking density compared to pens with a higher stocking density. The splitting value of 38 kg/m2 was markedly lower than the German national requirements for weaner pigs (57 kg/m²; TierSchNutztV, 2006) or a recommendation by EFSA (2005). Higher stocking den- sity can lead to more stress for the pigs due to e.g. impeded access to resources and increased fighting (Bryant and Ewbank, 1972). Most studies report more tail biting in pens with higher stocking densities (Moinard et al., 2003; Bracke et al., 2004). Only Arey (1991) reported an outbreak in two groups of 25 pigs directly after they were regrouped and moved to new pens with twice as much space as before. In general, stocking density seems to be of limited in- fluence on its own, but it contributes to various other stressors which in turn increase tail biting risk (EFSA, 2014).
Suckling piglet losses were used twice as primary splitter but splitting direction was contra- dictory. While in docked pigs with stocking density ≥ 38 kg/m2 prevalence of tail lesions was lower in pens on farms with lower losses of suckling piglets, the reverse was found in the subgroup with suckling piglet losses ≥ 18 % and ≥ 7.5 litters mixed at weaning. The splitting value of suckling piglet losses is lower than, for example, the average loss of suck- ling piglets, which is calculated based on the number of total born piglets, on farms associ- ated with the consultancy association VzF (26 %; VzF GmbH, 2017). Suckling piglet losses are influenced by a number of factors ranging from the health of the sow (Alonso-Spilsbury et al., 2007) and the piglets (English and Morrison, 1984) over management around birth (Vasdal et al., 2011) to housing characteristics (Cronin and van Amerongen, 1991). In our dataset, they might also reflect different combinations of management characteristics (see below).
We found higher prevalences of tail lesions in pens on farms where < 7.5 litters were mixed at weaning. This result is surprising because various studies showed that the prevalence of tail lesions was higher when more litters were mixed (Friend et al., 1983; Hötzel et al., 2011;
Colson et al., 2012). Colson et al. (2012) found that pigs, which were mixed with unfamiliar pigs and rehoused in a new pen, showed more object directed manipulation compared to non-mixed pigs. Additionally, aggression among unfamiliar mixed pigs is higher (Friend et al., 1983; Hwang et al., 2016) and leads to more injuries due to fighting (Hötzel et al., 2011) which may cause stress and thus increase tail biting risk, even though Veit et al. (2017) found no effect of mixing at weaning on tail biting during rearing. As the cut-off point of litters
20
mixed was rather high (7.5 litters), it is possible that number of litters mixed represents other farm characteristics. Farms mixing < 7.5 litters also had smaller litters sizes and lacked or- ganic enrichment (Table 3).
Surrogate variables
Surrogate variables correlate with the primary factor and have a similar association with the outcome (Van Engelsdorp et al., 2010; Henrard et al., 2015). They have not been used as primary splitters because the factor used as primary splitter resulted in more homogenous child nodes. Nevertheless, they can help interpreting of results.
Where number of weaning places on the farm was used as surrogate for daily weight gain in undocked pigs, tail lesion prevalence was higher on larger farms. This agrees with previous studies which also found more tail lesions on larger farms (Moinard et al., 2003; Rodenburg and Koene, 2007), farms with more pigs per stockperson (Scollo et al., 2017) or farms with fewer labour hours per pig (Colyer, 1970).
In docked pigs with higher stocking density, tail lesion prevalences were higher, if suckling piglet losses were higher (primary), weight gain lower or loose organic enrichment was pro- vided. Similar to the above described ambiguous association between weight gain and tail lesions, loose organic enrichment has been shown to reduce tail biting (Beattie et al., 1996;
Moinard et al., 2003; Zonderland et al., 2008) but is also used as an intervention measure after tail biting occurred. Within tail-docked pigs stocked at ≥ 38 kg/m2 the latter seems more likely, because lower daily weight gain and higher suckling piglet losses both indicate prob- lems on the farm.
In pigs stocked at higher density on farms with higher suckling piglet losses, tail lesion prev- alence was higher if fewer litters were mixed (primary splitter), or if litter size was smaller or loose enrichment was absent. Piglets from smaller litters usually have better weights (Beaulieu et al., 2010; Andersen et al., 2011), lower mortality (Rutherford et al., 2013) and are generally fitter at weaning, which reduces tail lesion risk. We assume that litter size in our dataset represents other farm characteristics, which may influence risk for tail lesions, as it was associated with fewer litters mixed at weaning (representing smaller farrowing batches) and absence of loose organic enrichment.
Weaning age was used as a surrogate for suckling piglet losses in a way, which might explain the unexpectedly higher tail lesion prevalence on farms with lower suckling piglet losses. In pigs stocked at higher density with higher suckling piglet losses and more litters mixed at weaning, greater weaning age might have counterbalanced the suckling piglet losses. In gen- eral, piglets weaned later are more developed and perform less manipulative behaviour after weaning (Weary et al., 1999; Worobec et al., 1999; Jarvis et al., 2008; van der Meulen et al., 2010), which reduces risk for tail lesions.
21 CONCLUSIONS
The identified risk factors were mostly related to other farm characteristics, which can in- crease stress and hence tail lesion risk in the pigs. Future research should therefore investi- gate, whether at least some of these factors may be integrative parameters for tail lesion risk on a farm. Regression tree analysis can be recommended for datasets with missing values, suboptimal data distribution and high collinearity. However, statistical methods with similar robustness, but which can additionally account for clustering are needed for analysing mul- tifactorial problems.
ACKNOWLEDGEMENT
Funding: The project was supported by funds of the German Government's Special Purpose Fund held at Landwirtschaftliche Rentenbank. The software was financially supported by
“Tönnies Forschung - Gemeinnützige Gesellschaft zur Förderung der Forschung über die Zukunft des Tierschutzes in der Nutztierhaltung mbH“.
22 REFERENCES
Alonso-Spilsbury, M., Ramirez-Necoechae, R., Gonzales-Lozano, M., Mota-Rojas, D., Trujillo-Ortega, M., 2007. Piglet Survival in Early Lactation: A review. Journal of Animal and Veterinary Advances 6, 76-86.
Andersen, I.L., Nævdal, E., Bøe, K.E., 2011. Maternal investment, sibling competition, and offspring survival with increasing litter size and parity in pigs (Sus scrofa).
Behavioral Ecology and Sociobiology 65, 1159-1167.
Arey, D.S., 1991. Tail-biting in pigs. Farm Building progress 105, 20-23.
Beattie, V.E., Walker, N., Sneddon, I.A., 1996. An investigation of the effect of
environmental enrichment and space allowance on the behaviour and production of growing pigs. Applied Animal Behaviour Science 48, 151-158.
Beaulieu, A.D., Aalhus, J.L., Williams, N.H., Patience, J.F., 2010. Impact of piglet birth weight, birth order, and litter size on subsequent growth performance, carcass quality, muscle composition, and eating quality of pork1. Journal of Animal Science 88, 2767-2778.
Bracke, M.B.M., Hulsegge, B., Keeling, L., Blokhuis, H.J., 2004. Decision support system with semantic model to assess the risk of tail biting in pigs: 1. Modelling. Applied Animal Behaviour Science 87, 31-44.
Breiman, L., Friedman, J., Olshen, R., Stone, C., 1984. Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, California, USA.
Breuer, K., Sutcliffe, M.E.M., Mercer, J.T., Rance, K.A., Beattie, V.E., Sneddon, I.A., Edwards, S.A., 2003. The effect of breed on the development of adverse social behaviours in pigs. Applied Animal Behaviour Science 84, 59-74.
Bryant, M.J., Ewbank, R., 1972. Some Effects of Stocking Rate and Group Size Upon Agonistic Behaviour in Groups of Growing Pigs. British Veterinary Journal 128, 64-70.
Camerlink, I., Bijma, P., Kemp, B., Bolhuis, J.E., 2012. Relationship between growth rate and oral manipulation, social nosing, and aggression in finishing pigs. Applied Animal Behaviour Science 142, 11-17.
Colson, V., Martin, E., Orgeur, P., Prunier, A., 2012. Influence of housing and social changes on growth, behaviour and cortisol in piglets at weaning. Physiology &
Behavior 107, 59-64.
Colyer, R.J., 1970. Tail biting in pigs. Agriculture 77, 215 - 218.
Cronin, G.M., van Amerongen, G., 1991. The effects of modifying the farrowing
environment on sow behaviour and survival and growth of piglets. Applied Animal Behaviour Science 30, 287-298.
De'Ath, G., Fabricius, K.E., 2000. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178-3192.
