Atashi H, Chen Y, Chelotti J, Lemal P, Gengler N
TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.
Department of Animal Science, Shiraz University, Shiraz, Iran.
J Anim Breed Genet. 2025 Mar;142(2):214-222. doi: 10.1111/jbg.12890. Epub 2024 Aug 31.
Regular monitoring of body condition score (BCS) changes during lactation is a crucial management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The aim of this study was to investigate the ability of random regression test-day models (RR-TDM) to predict BCS for the entire lactation in dairy cows even if the actual scoring is limited to one BCS record. The data consisted of test-day records of milk yield (MY), fat percentage (FP), protein percentage (PP) and BCS (based on a 9-point scale with unit increments; 1-9) collected from 2014 to 2022 in 128 herds in the Walloon Region of Belgium. In total, 20,698 test-day records on 2166 first-parity Holstein cows (2-12 with an average of 9.42 test-day records per cow) were available for MY, FP and PP; and 7985 records on the same animals (2-12 with an average of 3.68 records per cow) were available for BCS. To estimate the solutions, only one randomly selected BCS record per animal along with all her MY, FP and PP records were used, which were then used to predict BCS data (calibration set). The remaining BCS (1-11 with an average 2.86 BCS records per animal) were used to evaluate the goodness of the predictions (validation set). Multiple-trait RR-TDM was used to estimate (co)variance components through the average information restricted maximum likelihood (AI-REML) algorithm. Predicted BCS were grouped into nine classes as the original observed BCS used for comparison. Pearson correlation between the predicted and observed BCS, prediction error (PE), absolute prediction error (APE) and root mean squared prediction error (RMSE) were calculated. Mean (standard deviation; SD) BCS was 4.97 (1.01), 4.95 (1.07) and 4.98 (1.00) BCS units in the full, calibration and validation datasets, respectively. Pearson correlation between the observed and predicted BCS was 0.71, mean (SD) PE was 0.04 (0.52) BCS units, mean (SD) APE was 0.48 (0.53) BCS units and RMSE was 0.72 BCS units. These findings demonstrate the ability of RR-TDM to predict BCS for the entire lactation using a single BCS record along with available test-day records of milk yield and composition in Holstein dairy cows.
在奶牛养殖中，定期监测泌乳期的体况评分（BCS）变化是一项至关重要的管理工具；然而，目前的BCS测量往往是不连续的，且在时间上间隔不均。本研究的目的是调查随机回归测定日模型（RR-TDM）预测奶牛整个泌乳期BCS的能力，即使实际评分仅限于一条BCS记录。数据包括2014年至2022年在比利时瓦隆地区128个牛群中收集的测定日产奶量（MY）、乳脂率（FP）、乳蛋白率（PP）和BCS（基于9分制，单位增量为1；1 - 9）的记录。总共获得了2166头头胎荷斯坦奶牛的20698条测定日记录（每头牛2 - 12条记录，平均每头牛9.42条测定日记录）用于MY、FP和PP；以及同一批奶牛的7985条记录（每头牛2 - 12条记录，平均每头牛3.68条记录）用于BCS。为了估计参数，每头动物仅随机选择一条BCS记录以及其所有的MY、FP和PP记录，然后用于预测BCS数据（校准集）。其余的BCS记录（每头动物1 - 11条记录，平均每头牛2.86条BCS记录）用于评估预测的准确性（验证集）。多性状RR-TDM通过平均信息约束最大似然（AI-REML）算法估计（协）方差分量。预测的BCS被分为九个类别，与原始观察到的BCS进行比较。计算预测BCS与观察到的BCS之间的皮尔逊相关性、预测误差（PE）、绝对预测误差（APE）和均方根预测误差（RMSE）。完整数据集、校准数据集和验证数据集中的平均（标准差；SD）BCS分别为4.97（1.01）、4.95（1.07）和4.98（1.00）个BCS单位。观察到的和预测的BCS之间的皮尔逊相关性为0.71，平均（SD）PE为0.04（0.52）个BCS单位，平均（SD）APE为0.48（0.53）个BCS单位，RMSE为0.72个BCS单位。这些结果表明，RR-TDM能够利用一条BCS记录以及荷斯坦奶牛测定日产奶量和成分的可用记录来预测整个泌乳期的BCS。
