Pregnancy scanning of sheep in southern Australia. 2. Accuracy of pregnancy scanning in field data
Year: 2025
Type: article
Abstract: Context Scanning for fetal count enables producers to alter pre-lambing management of ewes according to litter size, for improving lamb and ewe survival outcomes. However, accurate scanning in industry flocks is important to achieve this goal. Aims This study aims to characterise accuracy of scanning in current field data and relate this to lambs recorded at both the ewe and flock levels. Methods Four experimental flock data sets and two large industry data sets derived from Sheep Genetics were used to demonstrate the repeatability of scanning and investigate how data quality influences the assessment of accuracy of scanning to predict lambing outcomes. Key results Scanning for pregnancy determination and fetal count is highly repeatable for experienced scanners, but at higher litter size (> 2 lambs) error rates in fetal count increase. The accuracy of distinguishing singles from multiples can be higher than for fetal counts. However, accuracy in predicting lambs born from scanning results is more strongly influenced by poor quality of recording lambing outcomes against individual ewes than to scanning errors. Scanning for fetal count does not have as high accuracy for predicting lambs reared due to lamb losses, which are also influenced by litter size. Conclusions Technical improvements and slower scanning speed might be required to increase accuracy of fetal counts at high litter size, but the overall impact is relatively low at current mean litter sizes, in flocks where triplet litters are relatively scarce. Implications Scanning accuracy is facilitated by using experienced scan practitioners requested to distinguish fetal counts, with appropriate pre-scanning preparation, and with all ewes scanned within correct ranges for fetal age at the time of scanning.
Source: Animal Production Science
DOI: 10.1071/an25233
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4416676687