Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Abstract

In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet's own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.

Keywords: boosted trees; growth; machine learning; pig production; pneumonia.

Figure 1.

Mean pneumonia prevalence per year. Bar width represents the number of nonmissing values for pneumonia status. Total number of records is 35,125.

Figure 2.

Median and interquartile range of lifetime growth rate (kg/d) per year. Box width represents the number of nonmissing values for lifetime growth rate. Total number of records is 61,041.

Figure 3.

Median and interquartile range of meat percentage per year. Box width represents the number of nonmissing values for meat percentage. Total number of records is 60,889.

Figure 4.

Aggregated receiver operating characteristic curves, based on all predicted probabilities of the observations in the 98 Test_New datasets, for pneumonia [dotted line, area under the curve (AUC) = 0.70, sensitivity at 90% specificity = 28%], low lifetime growth rate (solid line, AUC = 0.72, sensitivity at 90% specificity = 34%), and low meat percentage (dashed line, AUC = 0.58, sensitivity at 90% specificity = 15%).