Predicting ewe body condition score using adjusted liveweight for conceptus and fleece weight, height at withers, and previous body condition score record

Abstract

The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change and previous BCS records. It was hypothesized that if fleece weight- and conceptus- free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 months to approximately 42 months of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e. pre-breeding, at pregnancy diagnosis, pre-lambing and weaning). Additionally, individual lambing dates, ewe fleece weight and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Further, another two models based on a combination of LW, LW-change, previous BCS and height at withers (combined models) namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2) were fitted. Combined models gave more accurate (with lower Root Mean Square Error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R 2) (P > 0.05). Further, in all models, both LW-alone and combined models, a great proportion of variability in BCS could not be accounted for (0.25 ≥ R 2 ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offers no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed.

Study on Pig Body Condition Scoring Based on Deep Learning Model EfficientNet-B0

“…Inaccurate scoring can lead to overfeeding or underfeeding, resulting in health problems such as obesity, reduced reproductive capacity, and even death. Therefore, an efficient and accurate body condition scoring method is crucial to ensure pig health [1].…”