Diagnosis of Early Pregnancy in Cattle by Ovarian Analysis

Ludwick, T.M.; Rader, E.R.

doi:10.3168/jds.s0022-0302(68)86923-1

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…There is a long history of attempts to diagnose pregnancy in cattle at an early stage (Ghannam and Sorensen, 1967;Ludwick and Rader, 1968). Nevertheless, practical and reliable methods to generate a definitive diagnosis using ultrasonography at early stages of pregnancy (<25 d) remain of limited accuracy (ACC; based on predictive values) or may even be unavailable currently.…”

Section: Introductionmentioning

confidence: 99%

Color Doppler flow imaging for the early detection of nonpregnant cattle at 20 days after timed artificial insemination

Siqueira

Arêas

Ghetti

et al. 2013

Journal of Dairy Science

106

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The objective was to determine the accuracy of a pregnancy test for predicting nonpregnant cattle based on the evaluation of corpus luteum (CL) blood flow at 20 d (CLBF-d20) after timed artificial insemination (TAI). Crossbred Holstein-Gir dairy heifers (n=209) and lactating cows (n=317) were synchronized for TAI using the following protocol: intravaginal implant (1.0 g of progesterone) and 2mg of estradiol benzoate i.m. on d -10, implant removal and 0.526 mg of sodium cloprostenol i.m. on d -2, 1mg of estradiol benzoate i.m. on d -1, and TAI on d 0. On d 20, animals underwent grayscale ultrasonography (US) to locate the CL and color flow Doppler to evaluate CLBF-d20 using a portable ultrasound equipped with a 7.5-MHz rectal transducer. Based only on a visual, subjective CLBF evaluation, the animals were classified as pregnant or not pregnant. On d 30 to 35, blinded from results of the previous diagnosis, the same operator performed a final pregnancy diagnosis using US to visualize the fetal heartbeat (gold standard; US-d30). A second evaluator also analyzed the CLBF-d20 in the same animals by watching 7-s recorded videos. Blood samples were collected from a subset of 171 females to determine, by RIA, plasma progesterone (P4) concentrations, which indicate CL function. The final pregnancy outcome (US-d30) was retrospectively compared with the CLBF-d20 diagnoses and then classified either as correct or incorrect. The number of true positive, true negative, false positive, and false negative decisions were inserted into a 2 × 2 decision matrix. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CLBF-d20 test were calculated using specific equations. Binomial variables (pregnancy rate and proportions) were analyzed using Fisher's exact test for the effect of parity and to compare between evaluators and tests (CLBF-d20 vs. plasma P₄). The kappa values were calculated to quantify the agreement between CLBF-d20 and the gold standard (US-d30) and between evaluators. The performance parameters of CLBF-d20 test were as follows: sensitivity=99.0%, specificity=53.7%, positive predictive value=65.1%, negative predictive value=98.5%, and accuracy=74.8%. False negatives represented only 0.4% of the exams. No differences existed in these parameters between evaluators (no. 1 vs. no. 2) and tests (CLBF-d20 vs. plasma P4). Moreover, a high level of agreement was observed between evaluators (0.91). In conclusion, visual evaluation of CLBF-d20 represents a quick, reliable, and consistent diagnostic test that enables the early detection of nonpregnant cattle.

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Section: Introductionmentioning

confidence: 99%

Color Doppler flow imaging for the early detection of nonpregnant cattle at 20 days after timed artificial insemination

Siqueira

Arêas

Ghetti

et al. 2013

Journal of Dairy Science

106

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Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors

Shahinfar

Guenther

Page

et al. 2015

Journal of Dairy Science

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The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of individual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of individual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms.

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Diagnosis of Early Pregnancy in Cattle by Ovarian Analysis

Cited by 2 publications

References 3 publications

Color Doppler flow imaging for the early detection of nonpregnant cattle at 20 days after timed artificial insemination

Color Doppler flow imaging for the early detection of nonpregnant cattle at 20 days after timed artificial insemination

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors

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