Aim of the present study was to evaluate the use of a walk-over-weighing (WoW) technology to remotely weighgrowing lambs in a pastoral sheep production system and then use these data to predict future liveweight (LW) atdifferent lead times. Thus, an experiment was carried out in a flock of 144 lambs that were grazing freely for atotal of 94 days while an automatic WoW system allowed to remotely estimate LW and growth rate of individuallambs daily under these grazing conditions. Data were recorded as each animal entered voluntarily into the WoWplatform and walked through it to access water. Daily LW of each animal was used to forecast LW (FW) at 20, 30,40, 50, and 60 days ahead of any actual day. The accuracy of the FW was assessed using a linear mixed-effectsmodel and Lin’s concordance correlation coefficient (LCCC) with FW as dependent variable and actual observedLW (OW) as independent for each target days, both animal and date were random effects. In total, data from 132lambs were included in the final dataset which had an average growth rate of 0.25 ± 0.11 kg/d throughout the93 days of the trial. The FW for the next 20 and 30 days showed substantial agreement with observed weight(LCCC > 0.90). However, FW beyond 40 days was less precise and accurate (LCCC < 0.75). In addition, the LCCCof FW was higher when estimated from the growth rate in the last 14 compared to the last 7 days and latecompared to early in the trial. The WoW technology is suitable to monitor LW and growth rate of lambs both inreal-time and to predict future LW in commercial farms. Hence, the WoW system can be recommended to helpwith on-farm decision making of individual sheep
Use of an automated walk-over-weighing system to monitor and forecast liveweight in grazing lambs
Alessio Cotticelli
;
2026-01-01
Abstract
Aim of the present study was to evaluate the use of a walk-over-weighing (WoW) technology to remotely weighgrowing lambs in a pastoral sheep production system and then use these data to predict future liveweight (LW) atdifferent lead times. Thus, an experiment was carried out in a flock of 144 lambs that were grazing freely for atotal of 94 days while an automatic WoW system allowed to remotely estimate LW and growth rate of individuallambs daily under these grazing conditions. Data were recorded as each animal entered voluntarily into the WoWplatform and walked through it to access water. Daily LW of each animal was used to forecast LW (FW) at 20, 30,40, 50, and 60 days ahead of any actual day. The accuracy of the FW was assessed using a linear mixed-effectsmodel and Lin’s concordance correlation coefficient (LCCC) with FW as dependent variable and actual observedLW (OW) as independent for each target days, both animal and date were random effects. In total, data from 132lambs were included in the final dataset which had an average growth rate of 0.25 ± 0.11 kg/d throughout the93 days of the trial. The FW for the next 20 and 30 days showed substantial agreement with observed weight(LCCC > 0.90). However, FW beyond 40 days was less precise and accurate (LCCC < 0.75). In addition, the LCCCof FW was higher when estimated from the growth rate in the last 14 compared to the last 7 days and latecompared to early in the trial. The WoW technology is suitable to monitor LW and growth rate of lambs both inreal-time and to predict future LW in commercial farms. Hence, the WoW system can be recommended to helpwith on-farm decision making of individual sheepI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
