通过随机回归测试日模型预测整个哺乳期的体况评分。

IF 1.9 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
H Atashi, Y Chen, J Chelotti, P Lemal, N Gengler
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引用次数: 0

摘要

定期监测泌乳期体况评分(BCS)的变化是奶牛的一项重要管理工具;然而,目前的BCS测量通常不连续,时间间隔也不均匀。本研究旨在调查随机回归测试日模型(RR-TDM)预测奶牛整个泌乳期体况评分的能力,即使实际评分仅限于一次体况评分记录。数据包括2014年至2022年期间在比利时瓦隆大区128个牧场收集的产奶量(MY)、脂肪率(FP)、蛋白质率(PP)和BCS(基于单位增量的9分制;1-9)的测试日记录。在MY、FP和PP方面,共有2166头头等荷斯坦奶牛(2-12头,平均每头奶牛9.42个测试日)的20698个测试日记录;在BCS方面,共有7985头相同奶牛(2-12头,平均每头奶牛3.68个测试日)的记录。为了估算解决方案,每头奶牛只随机选取一条 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(1-11,平均每只动物 2.86 个 BCS 记录)用于评估预测的准确性(验证集)。通过平均信息限制最大似然(AI-REML)算法,使用多性状 RR-TDM 估算(共)方差成分。预测的 BCS 被分为九类,与用于比较的原始观测 BCS 相同。计算了预测 BCS 与观测 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、校准和验证数据集的 BCS 平均值(标准偏差;SD)分别为 4.97 (1.01)、4.95 (1.07) 和 4.98 (1.00) BCS 单位。观测和预测 BCS 之间的皮尔逊相关性为 0.71,平均(标清)PE 为 0.04 (0.52) BCS 单位,平均(标清)APE 为 0.48 (0.53) BCS 单位,RMSE 为 0.72 BCS 单位。这些研究结果表明,RR-TDM 能够利用单一的 BCS 记录以及现有的荷斯坦奶牛产奶量和成分测试日记录预测整个泌乳期的 BCS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of body condition score throughout lactation by random regression test-day models.

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.

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来源期刊
Journal of Animal Breeding and Genetics
Journal of Animal Breeding and Genetics 农林科学-奶制品与动物科学
CiteScore
5.20
自引率
3.80%
发文量
58
审稿时长
12-24 weeks
期刊介绍: The Journal of Animal Breeding and Genetics publishes original articles by international scientists on genomic selection, and any other topic related to breeding programmes, selection, quantitative genetic, genomics, diversity and evolution of domestic animals. Researchers, teachers, and the animal breeding industry will find the reports of interest. Book reviews appear in many issues.
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