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. 2018 Jun 29;2(4):383-395.
doi: 10.1093/tas/txy082. eCollection 2018 Oct.

Potential risk factors related to pig body weight variability from birth to slaughter in commercial conditions

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Potential risk factors related to pig body weight variability from birth to slaughter in commercial conditions

Sergi López-Vergé et al. Transl Anim Sci. .

Abstract

The aim of this observational study is to identify risk factors associated with body weight (BW) variability in three data sets (DS) in commercial conditions. A total of 1,009 (DS1), 460 (DS2), and 1304 (DS3) male and female crossbreed pigs (Pietrain × [Landrace × Large White]), respectively, were included in each trial. Pigs were periodically weighed until slaughter. Then, variables such as length of gestation, length of lactation, parity, litter size, sex, birth BW, and ADG were considered. Pigs remaining on the farm after two loads to the slaughterhouse were defined as last group of animals sent to slaughterhouse (LGS). Descriptive statistics of variability were calculated, and a risk analysis approach was used to look for the factors related to LGS. A multiple logistic regression was performed to identify all variables that were significant (P < 0.05). The risk ratio (RR), odds ratio (OR), and population attributable risk (PAR) were calculated for all of the significant variables after transforming all of them into binary factors using the 25th percentile as the cut-off point. Results showed that the major part of the variability (as CV) comes from birth (20% to 25%) and increased only a little during lactation and 14-d post weaning. From this point onwards, CV tended to decrease, as pigs got closer to the marketing weight (down 11.5% to 12.7%). Regarding the indicators selected, RR, OR, and PAR presented similar trends in the three DS studied. Therefore, for the variables finally included, these indicators had their minimum values at the start of the cycle and then gradually increased at the end. Those results, based on an epidemiological approach, suggest that the closer to the end of the cycle the greater the probability for a light piglet of being/becoming LGS. It might be explained by the shorter available time to efficiently implement preventive measures aimed to improve the performance of delayed pigs and, thus, reducing variability.Those results, based on an epidemiological approach, make sense as the probability for a light piglet to be a LGS increases the closer to the end of the cycle, due to the short time to implement preventive measures and increase the performance of delayed pigs and reduce variability. The differential PAR associated with both, the nursery and the growing period, was 1.7% and 1.5% for DS1, 5.1% and 3.1% for DS2, and 3.7% and 2.8% for DS3. For the lactation period, the results were 4.3% for DS2 and 4.5% for DS3. Results suggest that the most critical periods, in relation to retardation of growth in swine, are lactation and nursery. Implementing measures that maintain risk factors under or above thresholds, especially in the initial phases of growth, will reduce the percentage of LGS pigs and positively affect the overall homogeneity of the batch.

Keywords: coefficient of variation; correlation; growth curves; pigs; risk factors; variability.

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Figures

Figure 1.
Figure 1.
Distribution of each 10% of pigs, sorted from heaviest (1) to lightest (2) in data set (DS)1, DS2, and DS3 at birth, weaning, end of nursery, and growing period. The horizontal line relates to the average number of days to reach MBW (t103, 174.6 d). Birth refers to 0 d of age; weaning refers to 21 d of age (DS1 and DS3) or 28 d of age (DS2); end nursery refers to around 64 d of age; growing refers to around 83 d of age; and finishing refers to 125 d of age (116 d for DS1). DS1, data set 1; DS2, data set 2; DS3, data set 3; BBW, birth BW; WBW, weaning BW; NBW, body weight at the end of nursery period; GBW, growing BW.
Figure 2.
Figure 2.
Evolution of BW along productive cycle but represented in terms of SD (above) or CV (below). L, for Lactation; N, Nursery; G–F, Growing–finishing. DS1, data set 1; DS2, data set 2; DS3, data set 3.

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