Abstract

BackgroundOverall 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.MethodsTo 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.ResultsThe 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.ConclusionPV30 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.

Highlights

  • IntroductionMost of them do not consider all eligible animals and do not consider different management strategies at farm level

  • Overall reproductive performance of dairy herds is monitored by various indicators

  • The animal and its organs are influenced by both internal and external factors. All these multiple factors can be integrated in mathematical modelling to characterize an animal’s reproductive state efficiently [12]. The aim of this present study was to assess how two reproductive performance indicators, which are adjusted for the herd voluntary waiting period (VWP) and based on survival analysis, perform in a simulation of herds with different reproductive status and how they compare to traditionally used reproductive performance indicators that do not consider the VWP

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Summary

Introduction

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. Cows that have not been inseminated or cows that fail to conceive or to calve again are never included in those kinds of indicators and they are not completely representative of a herd’s reproductive status 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 and using survival analysis on the time-to-event data, where all information, on animals without the event, is used. The 100-days in calfrate (IC100) is an increasingly popular indicator that uses this methodology [1,2]

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