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Advances in natural heat detection

Advances in natural heat detection. Claire Ponsart, Pascal Salvetti. Physiological background. 1 oocyte ± 21 days Viability: 6 hours only . When to inseminate ?. Kölle (AETE, 2010). Kölle (AETE, 2010). 6 to 10 hours to reach the oocyte Viability: 24 hours. How to detect ovulations ?.

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Advances in natural heat detection

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  1. Advances in natural heat detection Claire Ponsart, Pascal Salvetti

  2. Physiological background 1 oocyte ± 21 days Viability: 6 hours only When to inseminate ? Kölle (AETE, 2010) Kölle (AETE, 2010) 6 to 10 hours to reach the oocyte Viability: 24 hours

  3. How to detect ovulations ? Estrous Oestrous • P4 concentrations monitoring • “Estrus” monitoring

  4. P4 monitoring: Herd Navigator® On field measurements in milk (automatic sampling according to animal status): LDH, BHB, Urea and Progesterone. • 93.3% Se and 93.7% Sp (over passing the problem of silent ovulations), • early alerts (12h before estrus), • no manipulation needed… • …What about costs ? Friggens et al. (2008) cited by Martin et al. (in press)

  5. P4 monitoring: other ‘on farm’ tools • Mini labs for ‘on farm’ P4 assays: • Concordance rate between ELISA in-lab assay (UNCEIA) and eProCheck® : 76.7% in milk (Gatien et al., 2012) 87.5% in serum • Cost, time-consuming • Individual P4 assays: LFIA, colorimetric • Efficient ? • Time-consuming ++

  6. Heat detection Det♀estrus (2008-2010) • 1 aim : to improve heat detection practices in cattle • 3 workpackages: • Description of behavioural changes during estrus in beef cattle • On field interviews of farmers and technicians about estrus detection • Development of a predictive model to assess heat detection quality

  7. Behavioural changes during estrus • 118 estrus analyzed • 83 in Charolais (CH) • 15 in Limousine (LI) • 20 in Blonde d’Aquitaine (BA) • Continous video recording, • P4 monitoring (blood) • For each estrus • 36h estrus video • versus • 36h control video Standing estrus Agonistic social signs Secondary sexual signs Affinity social signs Mounting signs + time spent standing up

  8. Behavioural changes: which signs to detect ? Not specific Repetition of SS signs is specific Rare Specific

  9. Behavioural changes: less lying time periods + 30 %

  10. Heat detection difficulties: highly variable expression « Easy » cow • 8 to 15 % of silent ovulations ! (disenhaus, 2004; Ranasinghe et al., 2010) « Discreet » cow

  11. Heat detection difficulties and milk production All sexual signs Mounting signs only (except StE) Standing estrus only (StE) Probability of detection (ovulation) Milk production (Kg/day) Logistic regressions using 587 ovulations in Normande & Holstein cows (including effects of breed, other cows in heat and milk production) Cutullic et al. (2010)

  12. Heat detection difficulties: a decreased estrus duration • In beef cattle • In dairy cattle • 4 to 8 h (StE) • 14 h (SSS) Cutullic et al. (2010) Estrus duration (StE-StE) Year of publication

  13. Heat detection difficulties: frequent cyclicity abnormalities Disenhaus et al. (2008) Cyclicity profiles of 63 holstein cows (Trinottières 2012, in press): Normal profiles  60.3 % PLP profiles  17.5 % Inactivity profiles  6.4 % Chanvallon et al. (2012)

  14. Heat detection difficulties: Changes in estrus cycle length Disenhaus et al. (2008)

  15. Visual detection: what is expected ? • Field study in French dairy farms: % of insemination during the luteal phase is varying according to the estrus signs used by breeders to inseminate cows • Higher % when “unspecific signs” (mucus discharge, nervosity, …) are used • Lower % when standing /mounting signs are used Salvetti et al. (2012)

  16. Visual detection: what is expected ? • Field study in French dairy farms: Conception rate depending on estrus signs used by breeders to inseminate cows • Decreased when only one “unspecific sign” is used to inseminate • Lowered when standing/mounting signs are used Salvetti et al. (2012)

  17. Visual detection: Timing of AI • Field study in French dairy farms: Time interval between estrus detection and insemination should be shorter than 24 hours Salvetti et al. (2012)

  18. Visual detection: expected efficiency • Key figures : • - 50 % of sensitivity (Se) • - 95 % of accuracy (Ac) Ducrot et al.(1999) Lacerte (2003)

  19. Estrus detection aids • Different tools, automated or not • Cameras • Standing estrus detector • Podometer • Neck collar activimeter • … • For review see Saint Dizier and Chastant-Maillard (RDA, 2012)

  20. Estrus detection by cameras: Results from one single farm • Good performances but time-consuming…

  21. Automated activity monitoring • Our experience in dairy cattle: • 85 Holstein cows (Derval, 2008, not published) • Heatime neck collar: 65.8% Se and 81.2% Ac • 41 Holstein cows (Philipot et al., 2010) • Heatime neck collar: 76.0% « Se »* and 93.0% Ac • Visual detection: 86.0% « Se »* and 96.0% Ac * P4 assays only when a detection occurred  not a real Se • 62 Holstein cows (Trinottières, 2012, not published) • Heatime neck collar: 62.6% Se and 84.2% Ac • Afimilk pedometer: 73.0% Se and 71.6% Ac

  22. Automated activity monitoring • Few study, great variability in results... • Effects of breeding system ? Breed ? Health?... • Comparison of 4 methods of detection Holman et al. (2011) 67 Holstein cows Optimal combination

  23. Monitored heat detection aids: what can we expect? • Further studies are needed to improve heat detection algorithms in relation with the breeding / management system (race, housing, health, calving dates,…) • Necessity to cross observations and to take into account animal history

  24. How to help farmers? • Assessment of heat detection quality Det♀estrus tool Simple informatic software (under Excel®) allowing to assess the quality of heat detection in the herd, using basic reproduction results

  25. Detœstrus approach (1) Characteristics of the farm and breeding management Level and penalties associated Risk factors Evaluation of heat expression level Score (/100) with green/orange/red code

  26. Detœstrus approach (2) Basic reproduction results including heat expression level  Characteristics of the farm and breeding management Evaluation of heat expression level by cows Evaluation of heat detection quality Estimation of heat detection efficiency at 1st AI and on returns + Estimation of heat detection accuracy (green/orange/red code)

  27. Detœstrus approach (3) Characteristics of the farm and breeding management Evaluation of heat expression level by cows Evaluation of heat detection quality Risk factors analysis Efficiency Accuracy

  28. Detœstrus approach (3) Characteristics of the farm and breeding management Evaluation of heat expression level by cows Evaluation of heat detection quality Risk factors analysis Efficiency Accuracy

  29. Detœstrus approach (3) Characteristics of the farm and breeding management Sum-up of the situation Risk factors list Evaluation of heat expression level by cows Evaluation of heat detection quality Risk factors analysis Efficiency Accuracy

  30. Detœstrus approach (3) Characteristics of the farm and breeding management Evaluation of heat expression level by cows Evaluation of heat detection quality Risk factors analysis Efficiency Accuracy Summary and advices to farmer Actions plan

  31. How to help breeders ? • Increasing breeder’s awareness regarding economic losses involved by a default of heat detection Simulation of economic losses involved by a decrease in heat detection performances compared with a reference situation (50 cows producing 9500 Kg of milk per year, 70% of Se, 99% of Ac) with low (25%) or high (50%) fertility Seegers et al.(2010)

  32. Important costs related to estrus detection deficiency Inchaisri et al.(2010)

  33. Futures • Improvement of automated detection aids • Promising genomic selection: towards identification of estrus expression QTLs Kommadath et al. (2011)  OXT and AVP genes and estrus behaviour expression

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