Utilizing NUtrack to Access the Activity Levels in Pigs with Varying Degrees of Genetic Potential for Growth and Feed Intake

Abstract

Feed cost accounts for over two-thirds of the variable cost of production. In order to reduce feed costs without sacrificing production numbers, feed efficiency must be improved. Calorie expenditure has been difficult to quantify in the past but is understood to impact residual feed intake (RFI) greatly. The objective of this work was to utilize an advanced computer vision system to evaluate activity levels across sex and sire groups with different expected breeding value combinations for growth and feed intake. A total of 199 pigs from four different sire groups (DNA Genetics Line 600) High Feed Intake/High Growth (HIHG), Low Feed Intake/High Growth (LIHG), High Feed Intake/Low Growth (HILG), and Low Feed Intake/Low Growth (LILG) were utilized at the UNL ENREC farm over 127 days. The NUtrack system allowed for individual monitoring of pigs in group housing to track daily activity traits. In total, HIHG pigs travelled less (p < 0.05; 139 vs. 150 km), spent more time lying (p < 0.05; 2421 vs. 2391 h), and less time eating (p < 0.05; 235 vs. 243 h) when compared to LILG pigs across time. The results suggest variation in activity occurs across the progeny of the sire groups selected to differentiate in growth and feed intake.

Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigs

“…Intuitively, time spent lying and distance traveled are expected to have the strongest genetic correlations with ADG, as an animal that expends less energy is expected to grow faster. Obermier et al [ 16 ] also found that pigs that spent more time lying and that were less active had higher growth rates and greater body weight at a given age. Sitting time was the behavior trait that was estimated to be least phenotypically and genetically correlated with all production traits, while distance was estimated to be the most correlated.…”

“…Behavior traits that are genetically correlated to production traits can be used for genetic improvement, including activity levels [ 16 ], eating patterns [ 17 ], and management refinement [ 18 ]. The goal of this study was to create a data quality control procedure and investigate behavior traits that can be captured by digital phenotyping and their phenotypic and genetic correlations with production traits.…”