Semakula Jimmy, Corner-Thomas Rene A, Morris Steve T, Blair Hugh T, Kenyon Paul R
Department of Animal Science, School of Agriculture and Environment, Massey University, Palmerston North, New Zealand.
National Agricultural Research Organization, Entebbe, Uganda.
Transl Anim Sci. 2021 Aug 3;5(3):txab130. doi: 10.1093/tas/txab130. eCollection 2021 Jul.
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 ( = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, 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. Furthermore, 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 ( ) ( 0.05). Furthermore, in all models, both LW-alone and combined models, a great proportion of variability in BCS, could not be accounted for (0.25 ≥ ≥ 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 offer 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.
母羊体况评分(BCS)与活重(LW)之间的关系此前已被用于根据LW、LW变化及之前的BCS记录来预测前者。研究假设,如果使用羊毛重量、无孕体活重和LW变化,以及肩高,那么当前预测BCS方法的准确性将会提高。在新西兰,对2017年出生的母羊(n = 429)从8月龄到约42月龄进行跟踪。在年度生产周期的不同阶段(即配种前、妊娠诊断时、产羔前和断奶时)收集每只母羊的LW和BCS数据。此外,还收集了每只母羊的产羔日期、羊毛重量和肩高数据。使用两个基于LW的模型来拟合线性回归模型,以预测每年生产周期中每个母羊年龄和阶段的当前BCS,这两个模型分别是未根据孕体重量和羊毛重量进行调整的(仅LW1)和经过调整的(仅LW2)模型。此外,还拟合了另外两个基于LW、LW变化、之前的BCS和肩高组合的模型(组合模型),即未调整的(组合1)和根据孕体和羊毛重量进行调整的(组合2)。与仅基于LW记录的模型相比,组合模型给出的BCS预测更准确(均方根误差更低:RMSE)。然而,应用调整后的模型并没有提高BCS预测准确性(或降低RMSE),也没有改善模型的拟合优度(P > 0.05)。此外,在所有模型中,无论是仅LW模型还是组合模型,BCS中很大一部分变异性无法得到解释(0.25 ≤ R² ≤ 0.83),并且在年度生产周期的不同年龄组和阶段以及不同时间(年份)都存在较大的预测误差(0.33 BCS ≤ RMSE ≤ 0.49 BCS)。因此,使用额外的母羊数据来校正LW以考虑羊毛和孕体重量,并将肩高作为额外的预测因子,并没有提高模型准确性。事实上,研究结果表明,针对孕体和羊毛重量对LW数据进行调整,对于基于LW的BCS预测模型没有额外价值。因此,仍需要开展更多研究来确定其他方法以解释个体动物的变异性。