Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles

Tenghe, Amabel; Bouwman, A.C.; Berglund, B.; Strandberg, E.; Blom, J.Y.; Veerkamp, R.F.

doi:10.3168/jds.2014-8732

Cited by 29 publications

(60 citation statements)

References 26 publications

(56 reference statements)

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“…The luteal phase length (mean ± SD) preceding AI was 13.7 ± 6.2 d, similar to previous reports of 12.9 ± 5.0 (Lamming and Darwash, 1998) and 13.0 ± 11.5 d (Tenghe et al, 2015). The inter-luteal phase length averaged 12.0 ± 3.7 d and was greater than the 7.8 ± 4.3 and 9.3 ± 8.5 d reported by Lamming and Darwash (1998) and Tenghe et al (2015), respectively. Repeat-ability estimates for luteal and inter-luteal lengths were 0.16 and 0.09, respectively, which were greater than that of Tenghe et al (2015), who reported estimates close to zero for both variables.…”

Section: Characterization Of In-line Milk Progesterone Profilessupporting

confidence: 86%

“…The inter-luteal phase length averaged 12.0 ± 3.7 d and was greater than the 7.8 ± 4.3 and 9.3 ± 8.5 d reported by Lamming and Darwash (1998) and Tenghe et al (2015), respectively. Repeat-ability estimates for luteal and inter-luteal lengths were 0.16 and 0.09, respectively, which were greater than that of Tenghe et al (2015), who reported estimates close to zero for both variables. The variation in inter- Variables were categorized based on quartiles and receiver operating characteristic (ROC) curve analysis cut-points (below vs. above).…”

Section: Characterization Of In-line Milk Progesterone Profilescontrasting

confidence: 54%

“…In herd D, milk P4c measurement started at 42.3 ± 2.8 DIM because a management decision to adopt a prolonged elective waiting period was followed. Following first milk P4c measurement, sampling repeated at algorithm-driven intervals on average every 2.2 ± 1.9 d based on a biomodel described in detail by Friggens and Chagunda (2005) and reported elsewhere (Friggens et al, 2008;Tenghe et al, 2015;Bruinjé et al, 2017a,b). In brief, the biomodel sampling frequency aims to estimate the day of estrus based on the decline in milk P4c below a default threshold, indicating the cessation of the previous luteal phase.…”

Section: In-line Milk Progesterone Analysis System and Records Descrimentioning

confidence: 99%

“…The total milk P4c records initially obtained included every record available from the IMAS software during the study period. Similar to previous studies evaluating milk P4c data (Friggens et al, 2008;Tenghe et al, 2015), sets of filtering criteria were applied to the data both at a lactation level and at a variable level. The filtering aimed to exclude lactations that had prolonged periods of no P4c measurements (i.e., gaps in sampling) that could occur if a cow had temporarily not been assigned to be sampled by the IMAS for management reasons, or if the system had a temporary breakdown during any time of a lactation period.…”

Section: Filtering Criteriamentioning

confidence: 99%

“…However, such an assumption does not consider cows that could have had a delayed ovulation, which would result in increased inter-luteal phase length, as demonstrated by Sartori et al (2004). Consequently, the late increase in milk P4c in relation to day of ovulation would explain the overall short luteal phase length observed in studies evaluating milk P4c profiles (Lamming and Darwash, 1998;Tenghe et al, 2015), when compared with the expected corpus luteum (CL) lifespan of approximately 18 d (Sartori et al, 2004).…”

mentioning

confidence: 99%

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Using in-line milk progesterone data to characterize parameters of luteal activity and their association with fertility in Holstein cows

Bruinjé

Colazo

Ribeiro

et al. 2019

Journal of Dairy Science

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Our objectives were to characterize parameters of luteal activity based on milk progesterone concentration (P4c) data from before and after artificial insemination (AI) and to evaluate their potential association with fertility in Holstein cows. Records of AI events (n = 4,353) and of milk P4c (n = 158,961) obtained through an in-line milk analysis system (Herd Navigator, DeLaval International, Tumba, Sweden) from 1,891 lactations of 1,423 Holstein cows were evaluated. Milk P4c (ng/mL) were measured every 2.2 ± 1.9 d (mean ± standard deviation) between 23.6 ± 7.3 and 185.3 ± 56.7 d in milk. Variations in milk P4c of consecutive records were used to determine onset of luteal phase (increase in P4c from <5.0 to ≥5.0 ng/mL), luteal phase length (period, in days, of P4c ≥5.0 ng/mL), cessation of luteal phase (decline from ≥5.0 to <5.0 ng/mL, designated as P4cdecline), and pregnancy (AI followed by a luteal phase that remained uninterrupted until 50 d post-AI). The length of the luteal phase preceding AI, the highest P4c (P4c peak) during the luteal phase preceding AI, the lowest P4c preceding AI (P4c pre-AI) that followed a P4c-decline, and the interval between P4c-decline and AI were evaluated, as well as the interval between AI and onset of luteal phase, and P4c at early diestrus (4.5 ± 0.6 d post-AI), mid diestrus (10.0 ± 0.6 d post-AI), and late diestrus (14.1 ± 0.6 d post-AI). Data were analyzed using logistic regressions, and comparisons made based on quartiles and cut-points established by receiver operating characteristic curve analysis. Overall probability of pregnancy was 32.0%. Parameters associated with reduced probability of pregnancy (represented as percentage points decrease in the probability of pregnancy) were (1) luteal phase length >14.4 d (7.6% decrease), (2) P4c peak ≤24.7 ng/mL (4.5% decrease), (3) P4c pre-AI >0.5 ng/mL (5.5% decrease), (4) interval between P4c-decline and AI of >1.6 d (4.0% decrease), (5) interval between AI and onset of luteal phase of <7 or >11 d (9.3 and 12.1% decrease, respectively), and (6) P4c at early diestrus ≤0.7 or >3.5 ng/ mL (15.2 and 6.7% decrease, respectively), (7) P4c at mid diestrus ≤12.4 ng/mL (12.5% decrease), and (8) P4c at late diestrus ≤22.7 ng/mL (9.7% decrease). The parameters of luteal activity associated with reduced probability of pregnancy established here could be used as benchmarks while developing recommendations to improve reproductive performance in herds using inline milk progesterone monitoring.

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