The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Peter Wynn - One of the best experts on this subject based on the ideXlab platform.
-
assessing Ovulation Detection performance in commercial dairy herds using progesterone concentrations from limited numbers of strategically collected milk samples
Journal of Dairy Science, 2010Co-Authors: J M Morton, Peter WynnAbstract:It is important to assess Ovulation Detection performance in commercial dairy herds both to investigate low reproductive performance and to enable herd managers to monitor the effectiveness of their system for detecting Ovulations. A method was developed to assess Ovulation Detection performance that uses limited numbers of strategically collected milk samples, assesses performance over the period when herd managers are making maximal effort to detect Ovulations, and when assessing proportions of Ovulations detected, accounts for false positive diagnoses of estrus and for cows that have not recommenced postpartum ovulatory cycles. Milk was sampled from cows not diagnosed in estrus early in the breeding program (about d 26 in year-round calving herds and d 22 in seasonal calving herds); milk samples were also collected from cows on the day of insemination. Cows with high milk progesterone concentrations were assumed to have had undetected Ovulations and false positive diagnoses of estrus, respectively. The method was successfully implemented in 161 of 167 commercial dairy herds. Positive predictive values (PPV; the proportions of Ovulation diagnoses where Ovulation was, in fact, imminent) were generally high in both year-round and seasonal calving herds (median values were 0.96 and 0.97, respectively), but 25% of herds had PPV <0.95. Ovulation Detection sensitivities (ODS) were low in most year-round calving herds, but many seasonal calving herds had high ODS values; median ODS were 0.73 and 0.94, respectively. However, in 25% of seasonal calving herds, ODS was <0.91. These findings indicate that this method for assessing Ovulation Detection performance can be successfully implemented in commercial dairy herds with appropriate professional support. The wide range of ODS and the absence of correlation between ODS and PPV suggest that it is possible for managers of many commercial herds in Australia to achieve increased reproductive efficiency through increases in ODS and PPV.
-
Assessing Ovulation Detection performance in commercial dairy herds using progesterone concentrations from limited numbers of strategically collected milk samples.
Journal of dairy science, 2010Co-Authors: J M Morton, Peter WynnAbstract:It is important to assess Ovulation Detection performance in commercial dairy herds both to investigate low reproductive performance and to enable herd managers to monitor the effectiveness of their system for detecting Ovulations. A method was developed to assess Ovulation Detection performance that uses limited numbers of strategically collected milk samples, assesses performance over the period when herd managers are making maximal effort to detect Ovulations, and when assessing proportions of Ovulations detected, accounts for false positive diagnoses of estrus and for cows that have not recommenced postpartum ovulatory cycles. Milk was sampled from cows not diagnosed in estrus early in the breeding program (about d 26 in year-round calving herds and d 22 in seasonal calving herds); milk samples were also collected from cows on the day of insemination. Cows with high milk progesterone concentrations were assumed to have had undetected Ovulations and false positive diagnoses of estrus, respectively. The method was successfully implemented in 161 of 167 commercial dairy herds. Positive predictive values (PPV; the proportions of Ovulation diagnoses where Ovulation was, in fact, imminent) were generally high in both year-round and seasonal calving herds (median values were 0.96 and 0.97, respectively), but 25% of herds had PPV
Seagal Azaria - One of the best experts on this subject based on the ideXlab platform.
-
semi supervised Ovulation Detection based on multiple properties
International Conference on Tools with Artificial Intelligence, 2019Co-Authors: Amos Azaria, Seagal AzariaAbstract:Despite being a well-researched problem, Ovulation Detection in human female remains a difficult task. Most current methods for Ovulation Detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which Ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for Ovulation Detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know Ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their Ovulation period, especially when some measurements are missing.
-
ICTAI - Semi-Supervised Ovulation Detection Based on Multiple Properties
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Co-Authors: Amos Azaria, Seagal AzariaAbstract:Despite being a well-researched problem, Ovulation Detection in human female remains a difficult task. Most current methods for Ovulation Detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which Ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for Ovulation Detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know Ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their Ovulation period, especially when some measurements are missing.
J M Morton - One of the best experts on this subject based on the ideXlab platform.
-
assessing Ovulation Detection performance in commercial dairy herds using progesterone concentrations from limited numbers of strategically collected milk samples
Journal of Dairy Science, 2010Co-Authors: J M Morton, Peter WynnAbstract:It is important to assess Ovulation Detection performance in commercial dairy herds both to investigate low reproductive performance and to enable herd managers to monitor the effectiveness of their system for detecting Ovulations. A method was developed to assess Ovulation Detection performance that uses limited numbers of strategically collected milk samples, assesses performance over the period when herd managers are making maximal effort to detect Ovulations, and when assessing proportions of Ovulations detected, accounts for false positive diagnoses of estrus and for cows that have not recommenced postpartum ovulatory cycles. Milk was sampled from cows not diagnosed in estrus early in the breeding program (about d 26 in year-round calving herds and d 22 in seasonal calving herds); milk samples were also collected from cows on the day of insemination. Cows with high milk progesterone concentrations were assumed to have had undetected Ovulations and false positive diagnoses of estrus, respectively. The method was successfully implemented in 161 of 167 commercial dairy herds. Positive predictive values (PPV; the proportions of Ovulation diagnoses where Ovulation was, in fact, imminent) were generally high in both year-round and seasonal calving herds (median values were 0.96 and 0.97, respectively), but 25% of herds had PPV <0.95. Ovulation Detection sensitivities (ODS) were low in most year-round calving herds, but many seasonal calving herds had high ODS values; median ODS were 0.73 and 0.94, respectively. However, in 25% of seasonal calving herds, ODS was <0.91. These findings indicate that this method for assessing Ovulation Detection performance can be successfully implemented in commercial dairy herds with appropriate professional support. The wide range of ODS and the absence of correlation between ODS and PPV suggest that it is possible for managers of many commercial herds in Australia to achieve increased reproductive efficiency through increases in ODS and PPV.
-
Assessing Ovulation Detection performance in commercial dairy herds using progesterone concentrations from limited numbers of strategically collected milk samples.
Journal of dairy science, 2010Co-Authors: J M Morton, Peter WynnAbstract:It is important to assess Ovulation Detection performance in commercial dairy herds both to investigate low reproductive performance and to enable herd managers to monitor the effectiveness of their system for detecting Ovulations. A method was developed to assess Ovulation Detection performance that uses limited numbers of strategically collected milk samples, assesses performance over the period when herd managers are making maximal effort to detect Ovulations, and when assessing proportions of Ovulations detected, accounts for false positive diagnoses of estrus and for cows that have not recommenced postpartum ovulatory cycles. Milk was sampled from cows not diagnosed in estrus early in the breeding program (about d 26 in year-round calving herds and d 22 in seasonal calving herds); milk samples were also collected from cows on the day of insemination. Cows with high milk progesterone concentrations were assumed to have had undetected Ovulations and false positive diagnoses of estrus, respectively. The method was successfully implemented in 161 of 167 commercial dairy herds. Positive predictive values (PPV; the proportions of Ovulation diagnoses where Ovulation was, in fact, imminent) were generally high in both year-round and seasonal calving herds (median values were 0.96 and 0.97, respectively), but 25% of herds had PPV
Dean L Moyer - One of the best experts on this subject based on the ideXlab platform.
-
single luteal phase serum progesterone assay as an indicator of Ovulation
American Journal of Obstetrics and Gynecology, 1997Co-Authors: Robert Israel, Daniel R Mishell, Sergio C Stone, Ian H Thorneycroft, Dean L MoyerAbstract:Abstract The availability of a rapid, accurate serum progesterone assay would alleviate many of the problems of patient inconvenience and discomfort encountered with the classical methods of Ovulation Detection. With the use of a competitive protein binding technique, by which a single technician can assay 30 or more samples for serum progesterone in one day, a range of daily follicular and luteal phase values was ascertained in a group of normally menstruating women. After a normal luteal phase range was established, single luteal phase serum progesterone sampling was performed in 51 infertile women with regular menses and 35 oligomenorrheic women undergoing clomiphene therapy. In the follicular phase of the cycle, progesterone levels were consistently less than 2 ng. per milliliter. Between 11 and 4 days prior to the onset of menses, in presumptively ovulatory cycles, serum progesterone levels were always 3 ng. per milliliter or greater. Progesterone values in this range were always accompanied by a secretory endometrium and, thus, can be considered presumptive evidence of Ovulation.
Catherine Disenhaus - One of the best experts on this subject based on the ideXlab platform.
-
Compact-calving systems are better suited to dual-purpose than dairy cow breeds, particularly when nutrient supply is limited
2018Co-Authors: Nicolas Bedere, Catherine Disenhaus, Ségolène Leurent-colette, Luc DelabyAbstract:This study aimed to explore adaptive trajectories of dairy and dual purpose breeds in contrasting grazing-based feeding systems (FS). About 500 lactations were recorded at the INRA farm of Le Pinau- Haras, equally distributed among breeds (Holstein: HO or Normande: NO) and FS (High or Low). It was possible to study the different steps of the reproductive process by combining milk progesterone information (three times a week) with intensive oestrous behaviour recording and pregnancy diagnosis (ultrasonography). Holstein produced more milk (+2294 kg in the High FS, +1280 kg in the Low FS) and lost more body condition than NO. Cows in the Low FS produced less and lost more body condition than in the High FS. NO resumed ovarian activity earlier (-5 d) and showed a higher proportion of normal cyclicity patterns (+22%) than HO. There was no difference in Ovulation Detection rates between breeds or FS. The NO had a higher re-calving rate than HO (+19%). Feeding system was not associated with cyclicity and re-calving rate. By limiting their milk yield, NO did not experience a severe negative energy balance, unlike HO. This resulted in better reproductive performance for NO, suggestive of greater suitability to a compact calving system.
-
Is selecting dairy cows for fat and protein contents an opportunity to maintain yearly compact-calving systems?
2018Co-Authors: Nicolas Bedere, Catherine Disenhaus, Ségolène Leurent-colette, Vincent Ducrocq, Luc DelabyAbstract:This study aimed to explore the effect of alternative selection strategies based on milk fat and protein contents instead of milk yield on reproduction of dairy cows. About 500 lactations were recorded, equally distributed among breeds (Holstein: HO or Normande: NO) and genetic groups with similar genetic merit for fat and protein yields and either high breeding values for milk yield (MILK) or fat and protein contents (CONT). Milk progesterone monitoring enabled the study of the reproductive performance. In both breeds, cows in CONT produced less milk (-763 kg in HO, -649 kg in NO), with higher fat content (+4.1 g kg-1 in HO and +3.9 g kg-1 in NO) and higher protein content (+1.6 g kg-1 in HO, +2.0 g kg-1 in NO) than cows in MILK. Cows in CONT had an earlier resumption of luteal activity than cows in MILK (-6 d in HO, -4 d in NO). There was no difference in Ovulation Detection rates between genetic groups. No difference in fertility performance was observed between genetic groups in NO. However, HO in CONT had a lower re-calving rate than in MILK (48 vs 55%). Selecting dairy cows for fat and protein contents may not be a good opportunity to improve reproduction.
-
Consequence on reproduction of two feeding levels with opposite effects on milk yield and body condition loss in Holstein and Normande cows
Journal of Dairy Science, 2009Co-Authors: E. Cutullic, Luc Delaby, G. Michel, Catherine DisenhausAbstract:The objective of this study was to evaluate the respective effects of milk yield and body condition (BC) loss on cows' postpartum reproductive status taking breed differences into account. 105 Normande (dual-purpose) and 98 Holstein cows were assigned to a low or high feeding level (L-group: 50% grass silage and 50% haylage in winter, no concentrate at grazing; H-group: 55% maize silage, 15% alfalfa hay and 30% concentrate, 4kg concentrate at grazing). Milk progesterone assays led to determine commencement of luteal activity (CLA), postpartum ovarian activity profile, Ovulation Detection rate and late embryo mortality. Data were analysed by variance-covariance and logistic regression models. In both breeds, L-group cows had a lower 100-day average daily milk yield (MY) than the H-group cows but lost more BC (0-5 scale) (21.5 vs. 30.9 kg/day, -1.38 vs. -0.94 unit, N=102 and 101; P
-
Consequence on reproduction of two feeding levels with opposite effects on milk yield and body condition loss in Holstein and Normande cows
Journal of Dairy Science, 2008Co-Authors: E. Cutullic, Luc Delaby, G. Michel, Catherine DisenhausAbstract:The objective of this study was to evaluate the respective effects of milk yield and body condition (BC) loss on cows' postpartum reproductive status taking breed differences into account. 105 Normande (dual-purpose) and 98 Holstein cows were assigned to a low or high feeding level (L-group: 50% grass silage and 50% haylage in winter, no concentrate at grazing; H-group: 55% maize silage, 15% alfalfa hay and 30% concentrate, 4kg concentrate at grazing). Milk progesterone assays led to determine commencement of luteal activity (CLA), postpartum ovarian activity profile, Ovulation Detection rate and late embryo mortality. Data were analysed by variance-covariance and logistic regression models. In both breeds, L-group cows had a lower 100-day average daily milk yield (MY) than the H-group cows but lost more BC (0-5 scale) (21.5 vs. 30.9 kg/day, -1.38 vs. -0.94 unit, N=102 and 101; P 0.25). Owing to feeding level effects, MY and BC loss could affect reproduction at different stages. Consistent with the literature, a greater BC loss coincides with reduced conception rate. High milk yield coincides with depressed Ovulation Detection and embryo survival.
