7 results on '"Geerinckx, Katleen"'
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2. Arbuscular mycorrhizal fungi affect both penetration and further life stage development of root-knot nematodes in tomato
- Author
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Vos, Christine, Geerinckx, Katleen, Mkandawire, Rachel, Panis, Bart, De Waele, Dirk, and Elsen, Annemie
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- 2012
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3. Short communication: Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows using cost-effective, on-farm measured data.
- Author
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Adriaens, Ines, Saeys, Wouter, Geerinckx, Katleen, De Ketelaere, Bart, and Aernouts, Ben
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CORPUS luteum , *COWS , *MATHEMATICAL functions , *LUTEOLYSIS , *PROGESTERONE , *DAIRY farms - Abstract
The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
4. Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge.
- Author
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Adriaens, Ines, Saeys, Wouter, Lamberigts, Chris, Berth, Mario, Geerinckx, Katleen, Leroy, Jo, De Ketelaere, Bart, and Aernouts, Ben
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ESTRUS , *LUTEINIZING hormone , *FARMERS , *MILK yield , *ENZYME-linked immunosorbent assay - Abstract
Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone–based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which modelbased indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
5. Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring.
- Author
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Adriaens, Ines, Huybrechts, Tjebbe, Aernouts, Ben, Geerinckx, Katleen, Piepers, Sofie, De Ketelaere, Bart, and Saeys, Wouter
- Subjects
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DAIRY farm management , *LIVESTOCK productivity , *HOLSTEIN-Friesian cattle , *MILK yield ,CATTLE diseases epidemiology - Abstract
Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
6. A novel system for on-farm fertility monitoring based on milk progesterone.
- Author
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Adriaens, Ines, Saeys, Wouter, Huybrechts, Tjebbe, Lamberigts, Chris, François, Liesbeth, Geerinckx, Katleen, Leroy, Jo, De Ketelaere, Bart, and Aernouts, Ben
- Subjects
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PROGESTERONE , *CATTLE fertility , *MILK analysis , *LUTEOLYSIS , *ESTRUS , *STATISTICAL sampling - Abstract
Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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7. Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows.
- Author
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Adriaens, Ines, Huybrechts, Tjebbe, De Ketelaere, Bart, Saeys, Wouter, Aernouts, Ben, Geerinckx, Katleen, Daems, Devin, and Lammertyn, Jeroen
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DAIRY cattle reproduction , *PROGESTERONE , *DAIRY farms , *ESTRUS , *DIAGNOSIS of pregnancy , *ECONOMICS - Abstract
Reproductive performance is an important factor affecting the profitability of dairy farms. Optimal fertility results are often confined by the time-consuming nature of classical heat detection, the fact that high-producing dairy cows show estrous symptoms shorter and less clearly, and the occurrence of ovarian problems. Today's commercially available solutions for automatic estrus detection include monitoring of activity, temperature and progesterone. The latter has the advantage that, besides estrus, it also allows to detect pregnancy and ovarian problems. Due to the large variation in progesterone profiles, even between cycles within the same cow, the use of general thresholds is suboptimal. To this end, an intelligent and individual interpretation of the progesterone measurements is required. Therefore, an alternative solution is proposed, which takes individual and complete cycle progesterone profiles into account for reproduction monitoring. In this way, profile characteristics can be translated into specific attentions for the farmers, based on individual rather than general guidelines. To enable the use of the profile and cycle characteristics, an appropriate model to describe the milk progesterone profile was developed. The proposed model describes the basal adrenal progesterone production and the growing and regressing cyclic corpus luteum. To identify the most appropriate way to describe the increasing and decreasing part of each cycle, three mathematical candidate functions were evaluated on the increasing and decreasing parts of the progesterone cycle separately: the Hill function, the logistic growth curve and the Gompertz growth curve. These functions differ in the way they describe the sigmoidal shape of each profile. The increasing and decreasing parts of the P4 cycles were described best by the model based on respectively the Hill and Gompertz function. Combining these two functions, a full mathematical model to characterize the progesterone cycle was obtained. It was shown that this approach retains the flexibility to deal with both varying baseline and luteal progesterone values, as well as prolonged or delayed cycles. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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