Refining growth monitoring in lambs: dynamic growth curve modeling approach for targeted selective treatment

This study addresses the challenge of accurately monitoring lamb growth to optimize health interventions, particularly targeted selective treatment (TST) against gastrointestinal nematodes. Traditional growth models often rely on flock averages, which may mask individual variation and delay identification of underperforming lambs. Based on Richard´s model, the original growth curve was fitted. According to this herd-level curve which classified lambs based on weight deviations within ± 20% of predicted values, we proposed a dynamic growth curve adjusted to each lamb's previous body weight, preserving the herd-specific sigmoidal growth pattern. Therefore, we aimed to compare the effectiveness of this novel individualized dynamic growth with the original curve for guiding TST in lambs. Measurements of 296 live body weights from 18 lambs aged 26 to 221 days were analyzed. In addition, fecal egg counts (FEC) were conducted on 130 samples collected from both anthelmintic-treated and non-treated lambs. Although based on a relatively small sample size, the dynamic growth curve accurately represented herd and individual growth patterns, reducing mean percentage residuals from - 22% (original curve) to - 1% (dynamic growth curve). When comparing the average FEC, lambs identified by the dynamic growth curve as requiring treatment exhibited higher FEC values (mean: 1,613.1 ± standard deviation: 1,790.22, median: 100, ranging from 0 to 6,950) compared to those not needing treatment (mean: 564.8 ± standard deviation: 11.15, median: 0, ranging from 0 to 7,050) (p = 0.00028). In conclusion, the dynamic growth curve improved early detection of growth impairment related to parasitic infection, enabling more targeted and effective treatments. Adopting dynamic growth modeling holds potential to improve both productivity and animal health in modern livestock systems.