Meta-analysis of relationship between weaning age and daily weight gain of piglets in the farrowing and nursery phases

A systematic review of the literature was carried out to perform a meta-analysis to assess effects of age at weaning on the performance of piglets in the range of 14 to 42 days old. This step consisted in defining the databases and keywords to be employed in searching for papers for the meta-analysis. To that end, the databases Capes Publication Portal and Google Scholar were searched for researches published from 2001 to 2019. After the acceptance and exclusion criteria had been defined and applied, 28 papers were selected. The data were collected with Excel® software of Microsoft Office for later statistical analysis. Orthogonal polynomial contrasts were used and the linear and quadratic regression equations were fitted with hypotheses tested at the 5% significance level. The results confirmed the disadvantages of early weaning and the benefits of weaning at around 28 days old. In the farrowing phase, piglets weaned after 35 days had lower daily weight gain (DWG) (P =0.002) than those weaned at 27 days. The derivative of the regression of DWG while nursing on age revealed that a weaning age of 26.34 days yielded the maximum DWG among the ages assessed. After weaning, the maximum DWG was achieved by weaning piglets at 32.26 days. Given the performance of piglets in the farrowing and nursery phases, the results of this meta-analysis indicated the best weaning age was between 26 and 32 days

Vocalization Data Mining For Estimating Swine Stress Conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. 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