Overall research outcome

One big asset of this thesis is the use of a standardized approach in all studies. In other words, the impact of the main factors affecting Irish pig production was done using the same methods and data sources. Biosecurity and feeding practices were assessed using farmers’ interviews, while respiratory disease information was gathered through phone-calls to farmers and veterinarians and slaughterhouse visits. The performance data for all studies was retrieved from the Teagasc e-ProfitMonitor, meaning all performance indicators were calculated using the same formulas and farmers had access to advisory expertise on how to collect on-farm data. This confers an opportunity to compare, from the same standpoint, the effects of each factor on performance, as discussed above. The author wanted all the data to be from the same year but the intensity of the work prevented this from happening.

We can also conclude that the statistical methods employed for research may not be useful to apply the knowledge gained into pig production. Other approaches, such as decision trees could serve the purpose of advancing research – i.e. improving the understanding of factors affecting an outcome, such as ADG or FCR - and, at the same time, be used to give meaningful advice to farmers. As a result of this work, the Chapter on biosecurity practices was submitted to the Porcine Health Management Journal and is currently under review. Chapters 5 and 6 are currently in preparation for submission, with focus on the use of alternative statistical methods, in complement of the multivariable linear models. Figure 7.2 shows a decision tree modelling ADG from the predictors of respiratory disease summarised in Chapter 6: vaccination, serology and pluck lesions.

A decision tree is a predictive model built using a machine learning algorithm. In brief, the algorithm partitions the data into subsets using if/then rules. The partitioning process starts with a binary split and continues using different variables to split the data, until no further splits can be made. Different rules can be applied, and the tree can be pruned to simplify the results. The models produced are easy to interpret, can handle different types of data, and don’t require normality assumptions of the data. In the decision tree (Figure 7.2), cranial pleurisy is confirmed as the main detrimental

96 predictor for ADG, as seen in the correspondent linear model in Chapter 6. Farms with more than 32% of cranial pleurisy have the lowest ADG with a mean of 643 g/day.

Figure 7.2. Decision tree model of ADG (g/day) using vaccination, serology and pluck lesions as predictors.

Legend: ADG – Average Daily Gain, CP – Cranial Pleurisy, SIV_SN – Average S/N values for SIV on farm.

Seven farms fitted this description. On the other hand, farms with less than 32% of cranial pleurisy and with less than 1.4% of pleurisy have the highest ADG with an average of 790 g/day. Naturally, the average live weight sold conditions positively ADG, like discussed in Chapter 6. Finally, within farms with high cranial pleurisy (>=

32%), higher pleurisy (>=1.4), and with smaller live weights at slaughter, farms less exposed to SIV (SIV_SN > 0.63) have a higher ADG when compared to farms in the same circumstances but more exposed to SIV. Interestingly, the SIV S/N value to split the tree is very close to the threshold value for positiveness in the ELISA kit used, which was set at 0.6. Thus, this kind of analysis guides the understanding of the results in a more comprehensive way.

97 Finally, all experimental Chapters were based on observational cross-sectional studies.

As stated repeatedly, their nature implies the classification of the results as associations and precludes the inference of causation. In the pyramid of evidence-based medicine (Figure 7.3, A), these studies are positioned intermediately, meaning their strength of evidence may be lacking when compared to other studies, like randomized control trials (RCT). The latter are designed to minimize bias and to eliminate confounding factors. RCT are useful when the factors to study are already characterized and the objective is to study their impact in other variables. According to Vandeweerd et al. (2012), observational studies are prevalent in the literature and their usefulness is mainly connected with economic and logistic reasons. The authors state that these studies are favoured if the study subjects “are not easy to control for practical and ethical reasons”. Recently, Murad et al. (2016) proposed two modifications to the pyramid of evidence (Figure 7.3, B and C). In the second figure (Figure 7.3 B), the authors argue that the quality of the evidence cannot be solely based on study design because other factors like imprecision and inconsistency may also affect the results. On the other hand, the quality of evidence provided by some observational studies should be graded up, provided that their results are robust. In the second change (Figure 7.3 C), the authors sustain that some systematic reviews and meta-analyses are based in other studies which may contain flaws and inconsistencies.

Therefore, their relevance should be carefully analysed, which leads to the suggestion of using them as “a lens through which other types of studies should be seen”. Other authors criticize the poor representativeness of the results obtained by RCTs. For example, Nyachoti et al. (2004), in a review on voluntary feed intake, stated that most of the data available on the subject derived from RCT studies, which were designed to evaluate one single factor at a time and involving small groups or individually housed pigs. The authors argue that these data do not indicate how various factors affect feed intake in pigs, and therefore the results are “often difficult to extend to commercial production systems”. The objectives of this thesis were to characterize the factors affecting Irish pig production and to draw the prevalence of key respiratory pathogens.

Likewise, this approach was the most suitable to meet that purpose.

98 Figure 7.3. New pyramid of evidence (Source: Murad et al. (2016)).