Delimitación circunstancial

Parasitological data are often presented as prevalence or mean (worm or egg count). Prevalence data can be analysed as a binomial distribution, while count data will follow a Poisson or negative binomial distribution. In case of FEC, the raw data are, before accounting for the dilution factor, Poisson distributed (Torgerson et al., 2012). Normally, the first step of a statistical analysis is to check how the data actually are distributed. The effect of explanatory variables (clinical welfare indicators) on the response variable (helminth infection) could have been investigated under a generalised linear model using the correct distribution. However, the statistical analysis of the data from Study II was analysed using a graphical model (Whittaker, 1990; Lauritzen, 1996). Graphical models present the result systematically in a graph or network. Directed graphical models use the casual structure of the variables to guide the construction of the graph, meaning that information about the casual structure is needed. Undirected graphical models are suitable in cases where little or no information of the casual structure is known, as in Study II.

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In graphical models, the variables included are represented as points (vertices) and lines (edge) that connect the points. A line (edge) will connect two variables (two points) if the conditional correlation between them, given the other variables, is significantly different from zero (Abreu & Labouriau, 2010; Kristensen et al., 2010, see figure 6 for an example). If two points are not connected with a line (variable A and C in figure 6), the two variables are not significantly correlated when the other variables are included. In graphical models, two variables will only be connected by a line in the graph if, and only if, they carry new information that is not already contained in the other variables in the model (Whittaker, 1990; Abreu & Labouriau, 2010;

Kristensen et al., 2010). However, two variables that are indirectly connected might be correlated (variable A and C in figure 6), but this correlation is spurious, meaning that the correlation is completely explained by another variable in the model. This is further elucidated with an example (Figure 6). In this graphical model, variable A is directly associated with variable B, whereas variable A and variable C are indirectly connected through variable B. This means that when variable B is given, variable A will not contain any information about variable C, which is not already contained in variable B and vice versa. In other words, variables A and C are conditionally non-correlated as long as variable B is given, i.e. the direct association between variables A and C is spurious as it disappears when controlling for (conditioning) variable B. The same is applied for the connection between variables A and D, and variables C and D.

All clinical welfare indicators, with at least 5% of the hens diagnosed with poor welfare

condition with respect of the given indicators (see Paper II), are included in an undirected graphical model, as no casual structure between the variables is known in Paper II. The included variables were keel bone deformities at peak and end of lay, back feathering at peak and end of lay, body (neck, belly and tail) feathering at peak and end of lay, A. galli/Heterakis infection at end of lay, hen’s age in weeks at end of lay and housing system. Two variables were included as descriptive

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variables; age in weeks at end of lay due to the large age range (62 to 77 weeks) and housing system due to the apparent difference between single-tiered and multi-tiered systems. The graphical model allows variables to perform a circular connection, and the length of the lines contains no

information as the graphs are unweighted.

Fisher’s exact test was used to analyse the level of significance between the directly associated indicators in the graphical model, between not directly associated indicators, and for investigating whether the animal-based welfare indicators differed at the two time points. The Fisher’s exact test is applicable when having low frequencies in r x c contingency tables (Blæsild & Granfeldt, 2002), which is the case for some of the clinical welfare indicators in Paper II.

Qualitative interview study (Study III)

The aim of Study III was to identify management strategies suitable for controlling mortality and endoparasite infections at organic egg farms. To address this, qualitative research methodology is appropriate, as the qualitative interview is a method which attempts to understand and explore the interviewed persons’ view and experiences (Kvale & Brinkmann, 2008). The qualitative interviews were conducted in order to explore and understand the management practices on the farms by encouraging the producers to tell their stories and experiences in their own words. Each new interview will add new perspectives or experiences to the subject or support statements by others. The interviewer’s task is to follow up and explore what the interviewee says and to keep the interview within the theme as outlined in Vaarst et al. (2007) and Vaarst & Sørensen (2009). The qualitative interview methods are beneficial when exploring farm practices as the answers should fit into predefined categories, as they would, if quantitative methods were used as a questionnaire. Therefore, the qualitative interview method explores the producer’s perception and practices, which can be used to identify different strategies, practices or perceptions. The qualitative result can and should not be generalised beyond the in-depth understanding within the field of study, as the aim is not to present a representative sample of opinion or quantify the experience (Kvale & Brinkmann, 2008). This is in contrast to the questionnaire study, where it is possible to test the prevalence of certain beliefs and practices.

The method used was a semi-structured qualitative research face-to-face interview conducted according to a specific interview guide (Appendix 6) including selected themes and open-ended questions (Kvale & Brinkmann, 2008). Seven interviews were conducted and tape-recorded with a length from 22 to 50 minutes. At the seventh interview, the interviews were quite repetitious and,

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consequently, it was concluded that the point of saturation was reached. The point of saturation is the point where further interviews are expected to give only little or no new knowledge. All interviewed producers were informed that the interview and quotes from the interview would be published in an anonymised form. At the beginning of the interview, the producers were informed that the aim was to hear their stories, and in order to get the producers familiar with the process, the first theme was their egg production in general, with a special focus on the batch included in the observational studies. This approach was chosen in order to have an open conversation about the fact that the interviewer had been on the farm before as an observer during the other part of the project. Sometimes, the producer referred to the interviewer’s knowledge about the production, in such cases, the producer was asked to explain it further in order to ensure a common understanding.