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大型商业奶牛场荷斯坦泌乳牛受胎率和配种率相关管理实践。

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds.

机构信息

Department of Dairy Science, University of Wisconsin, Madison 53706, USA.

出版信息

J Dairy Sci. 2010 Apr;93(4):1459-67. doi: 10.3168/jds.2009-2015.

DOI:10.3168/jds.2009-2015
PMID:20338423
Abstract

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance.

摘要

对参与商业后裔测试计划的泌乳荷斯坦奶牛的数据进行了分析,以解释与大型商业奶牛场的群体平均受胎率和配种率相关的管理因素。利用牧场管理软件,对 108 个牛群的生产、繁殖、淘汰和牛奶质量等相关数据进行了分析。此外,还对 86 个农场的管理、设施、营养和劳动力进行了调查。本研究共考虑了 41 个与管理因素和可能影响受胎率和配种率的条件有关的解释变量。使用构建模型树的机器学习算法来开发解释受胎率和配种率的模型。与受胎率相关的最重要的解释变量是人工授精后 4 至 17 天内重复授精的百分比、配种栏的饲养密度、自愿等待期的长度、妊娠检查天数和体细胞评分。与配种率相关的最重要的解释变量是每个配种技术员的泌乳牛数量、使用同步化程序、在夏季使用吸水器在蓄栏区、以及配种栏中每头牛的卧床空间。上述模型分别解释了观察到的受胎率和配种率变化的 35%和 40%,强调了与繁殖无关的群体管理因素与群体繁殖性能之间的关联。

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