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Linear regression coefficient estimates (standard error within parentheses) of inbreeding for weight at birth, weight at weaning, and average pre-weaning daily weight gain (ADG)
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This study is aimed at estimating genetic parameters, effective population size, inbreeding, and inbreeding depression for birth weight, weaning weight, and average pre-weaning daily weight gain (ADG) in Piau pigs. We used information from 3841 Piau pigs, and four linear models were fitted in single-trait analyses, including or excluding maternal g...
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... inbreeding effects on the evaluated traits are presented in Table 6. The regression coefficient was significant only for ADG (p < 0.10); for this trait, the regression shows that for each 1% increase in the inbreeding coefficient, there is a decrease of 0.0005 g in ADG (Table 6). ...Similar publications
The aim of study was to evaluate morphometric traits, growth and reproduction performance of indigenous Agonda Goan pigs reared under coastal climate which supports livelihood development of resource poor farmers. Population size in breeding tract varied depending on farmers’ preference for breeding and consumers’ demand for pork. Data (n=74) on di...
The aim of study was to evaluate morphometric traits, growth and reproduction performance of indigenous Agonda Goan pigs reared under coastal climate which supports livelihood development of resource poor farmers. Population size in breeding tract varied depending on farmers’ preference for breeding and consumers’ demand for pork. Data (n=74) on di...
Citations
... Regarding live weights, the purebred PP exhibited the lowest values across all growth stages, as expected, due to their smaller size. The average weaning weight in this population of the PP breed is 6.60 kg with a standard deviation of 1.84 kg (Oliveira et al., 2023). In our study, the average weight Figure 6 -Importance of the image features back area, back perimeter, back width, and body depth in the prediction model for body weight in pigs using the random forest regression approach. ...
This research aimed to compare statistical methods (random forest, RIDGE, LASSO, and elastic net regression) for the prediction of body weight in purebred and crossbred pigs reared in Brazil. This prediction was based on dorsal-view images obtained from video image processing. The study involved 69 animals belonging to breeds such as Large White, Piau, Duroc × Large White, and Piau × Large White. The data collection spanned 144 days, with measurements taken at approximately 20-day intervals, totaling eight measurements for each animal throughout their growth stages. Image acquisition was carried out in individual pens using an Intel RealSense Depth D435 digital camera. The features back area, back perimeter, back width, and body depth were extracted from the images. Pearson’s correlation analysis was conducted to assess the relationship between live weight and these features. The dataset was randomly divided into a training dataset (65%) and a test dataset (35%), and model training was performed by five-fold cross-validation balanced according to the growth stage, which was divided into three groups. This procedure was repeated 100 times, and the resulting metrics were taken as the average of the 100 repetitions. Although with a slight difference, the random forest method outperformed the others with the highest average R² value (0.87), as well as the lowest average RMSE (14.32) and average MAE (10.13) values. Consequently, the random forest algorithm proved to be the most effective in predicting body weight. The back area, back width, and back perimeter were the most important variables in the model.
2D image; back area; crossbred pig; penalized regression; precision livestock farming; random forest
