Effect of within-litter birth weight variation on piglet survival and pre-weaning weight gain in a commercial herd

There are several factors that affect piglet survival and this has a bearing on sow productivity. Ten variables that influence pre-weaning vitality were analysed using records from the Pig Industry Board, Zimbabwe. These included individual piglet birth weight, piglet origin (nursed in original litter or fostered), sex, relative birth weight expressed as standard deviation units, sow parity, total number of piglets born, year and month of farrowing, within-litter variability and the presence of stillborn or mummified littermates. The main factors that influenced piglet mortality were fostering, parity and within-litter variability especially the weight of the individual piglet relative to the average of the litter (P<0.05). Presence of a mummified or stillborn littermate, which could be a proxy for unfavourable uterine environment or trauma during the birth process, did not influence pre-weaning mortality. Variability within a litter and the deviation of the weight of an individual piglet from the litter mean, influenced survival to weaning. It is, therefore, advisable for breeders to include uniformity within the litter as a selection criterion. The recording of various variables by farmers seems to be a useful management practice to identify piglets at risk so as to establish palliative measures. Further, farmers should know which litters and which piglets within a litter are at risk and require more attention.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.