Decision trees as a tool for selecting sows in commercial herds

ABSTRACT

This study aimed to evaluate the use of decision trees to select sows based on the production parameters of parity order (PO) 1 sows from a commercial herd. Data were collected at a piglet production unit with a capacity of housing 5,500 sows in collective pens. Piglet production and sow culling information was collected from PO1 and PO2 sows. The period from January 2017 to March 2020 was analyzed. The correlation analysis was used to identify the influence of the production parameters on sow culling after exploring the database using the graphical analysis and descriptive statistics. The ANOVA was applied to evaluate differences in the response variables between culled and unculled sows. Two models were proposed using the decision tree method: model 1 referred to sow culling, and model 2 comprised the total number of liveborn piglets (TBA). The calculated value was close to 0, although the correlations of the production parameters with culling were statistically significant. The mean number of weaned piglets was higher for unculled sows in PO1 (p < 0.05). The number of weaned piglets, total number of liveborn piglets, and weaning-service interval did not differ in the unculled and culled sows in PO2 (p > 0.05). Using a confusion matrix as a metric tool, the decision tree method used in this study provided consistent results for this database, indicating its possible use for decision-making in sow selection.

culling; data; machine learning; piglets; pig production