Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Acta Vet Scand. 2012 Jan 30;54(1):5. doi: 10.1186/1751-0147-54-5.
Overall reproductive performance of dairy herds is monitored by various indicators. Most of them do not consider all eligible animals and do not consider different management strategies at farm level. This problem can be alleviated by measuring the proportion of pregnant cows by specific intervals after their calving date or after a fixed time period, such as the voluntary waiting period. The aim of this study was to evaluate two reproductive performance indicators that consider the voluntary waiting period at the herd. The two indicators were: percentage of pregnant cows in the herd after the voluntary waiting period plus 30 days (PV30) and percentage of inseminated cows in the herd after the voluntary waiting period plus 30 days (IV30). We wanted to assess how PV30 and IV30 perform in a simulation of herds with different reproductive management and physiology and to compare them to indicators of reproductive performance that do not consider the herd voluntary waiting period.
To evaluate the reproductive indicators we used the SimHerd-program, a stochastic simulation model, and 18 scenarios were simulated. The scenarios were designed by altering the reproductive management efficiency and the status of reproductive physiology of the herd. Logistic regression models, together with receiver operating characteristics (ROC), were used to examine how well the reproductive performance indicators could discriminate between herds of different levels of reproductive management efficiency or reproductive physiology.
The logistic regression models with the ROC analysis showed that IV30 was the indicator that best discriminated between different levels of management efficiency followed by PV30, calving interval, 200-days not-in calf-rate (NotIC200), in calf rate at100-days (IC100) and a fertility index. For reproductive physiology the ROC analysis showed that the fertility index was the indicator that best discriminated between different levels, followed by PV30, NotIC200, IC100 and the calving interval. IV30 could not discriminate between the two levels.
PV30 is the single best performance indicator for estimating the level of both herd management efficiency and reproductive physiology followed by NotIC200 and IC100. This indicates that PV30 could be a potential candidate for inclusion in dairy herd improvement schemes.
奶牛群的整体繁殖性能通过各种指标进行监测。其中大多数指标都没有考虑到所有合格的动物，也没有考虑到农场层面的不同管理策略。通过在产后特定时间间隔或固定时间段（如自愿等待期）测量怀孕牛的比例，可以缓解这个问题。本研究的目的是评估两个考虑牛群自愿等待期的繁殖性能指标。这两个指标是：自愿等待期后 30 天加上怀孕牛在牛群中的比例（PV30）和自愿等待期后 30 天加上配种牛在牛群中的比例（IV30）。我们想评估这两个指标在不同繁殖管理和生理条件下的模拟牛群中的表现，并将其与不考虑牛群自愿等待期的繁殖性能指标进行比较。
为了评估繁殖指标，我们使用了 SimHerd 程序，这是一个随机模拟模型，模拟了 18 种情况。通过改变繁殖管理效率和牛群繁殖生理状态来设计这些场景。我们使用逻辑回归模型和接收者操作特征（ROC）来评估繁殖性能指标在不同繁殖管理效率或繁殖生理水平的牛群之间的区分能力。
逻辑回归模型和 ROC 分析表明，IV30 是区分不同管理效率水平的最佳指标，其次是 PV30、产犊间隔、200 天未怀孕率（NotIC200）、100 天怀孕率（IC100）和生育指数。对于繁殖生理，ROC 分析表明，生育指数是区分不同水平的最佳指标，其次是 PV30、NotIC200、IC100 和产犊间隔。IV30 无法区分这两个水平。
PV30 是评估牛群管理效率和繁殖生理水平的单一最佳性能指标，其次是 NotIC200 和 IC100。这表明 PV30 可能是纳入奶牛群改良计划的潜在候选指标。
