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

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

3. How to detect ovulations ? Estrous Oestrous P 4 concentrations monitoring “Estrus” monitoring

4. P 4 monitoring: Herd Navigator ® Friggens et al. (2008) cited by Martin et al. (in press) 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 ?

5. P 4 monitoring: other ‘on farm’ tools Mini labs for ‘on farm’ P 4 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 Standing estrus Secondary sexual signs Mounting signs Agonistic social signs Affinity social signs 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 + time spent standing up

8. Behavioural changes: which signs to detect ? Behaviours typeRaceEstrous phaseluteal phase Social signs (%) CH L 59 ± 1192 ± 9 CH B 47 ± 1 190 ± 10 LI 37 ± 1190 ± 11 BA 47 ± 884 ± 10 Secondary sexual signs (%) CH L 30 ± 108 ± 9 CH B 33 ± 710 ± 10 LI 45 ± 89 ± 12 BA 40 ± 716 ± 10 Mounting signs (without StE) (%) CH L 9 ± 50 ± 0 CH B 15 ± 70 ± 0 LI 14 ± 40 ± 0 BA 11 ± 30 ± 0 Standing Estrus(%) CH L 2 ± 20 ± 0 CH B 5 ± 50 ± 0 LI 4 ± 30 ± 0 BA2 ± 10 ± 0 Specific Not specific Rare Repetition of SS signs is specific

9. Behavioural changes: less lying time periods Race % of time spent « standing-up » Œstral phaseLuteal phase CH L 88 ± 11 %48 ± 25 % CH B 82 ± 12 %53 ± 11 % LI84 ± 11 %61 ± 20 % BA91 ± 8 %59 ± 23 % + 30 %

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

11. Heat detection difficulties and milk production Milk production (Kg/day) Probability of detection (ovulation) All sexual signs Mounting signs only (except StE) Standing estrus only (StE) 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) RaceStanding estrus (StE)Secondary sexual signs (SSS) CH A 7,6 ± 4,6 h12,4 ± 3,9 h CH B 9,9 ± 3,7 h12,1 ± 4,1 h LI 8,2 ± 6,3 h11,1 ± 4,0 h BA6,2 ± 3,4 h11,0 ± 2,4 h Cutullic et al. (2010) Year of publication Estrus duration (StE-StE)

13. Heat detection difficulties: frequent cyclicity abnormalities RacenbNormalInactivity Prolonged Luteal Phase Abondance2622 (80 %)1 (4 %) Charolaise9654 (56 %)42 (44 %)0 Motbéliarde3624 (67 %)9 (25 %)0 Normande10585 (81 %)8 (8 %) Prim Holstein13876 (55 %)26 (19 %)32 (23 %) 400261 (65 %)86 (12 %)41 (10 %) Disenhaus et al. (2008) Chanvallon et al. (2012) Cyclicity profiles of 63 holstein cows (Trinottières 2012, in press): Normal profiles  60.3 % PLP profiles  17.5 % Inactivity profiles  6.4 %

14. Heat detection difficulties: Changes in estrus cycle length RacenbMeanMedianS.D. Abondance3520.8211.9 Charolaise7720.2212.2 Montbéliarde3721.0212.5 Normande15521.4212.1 Prim Holstein13622.6232.3 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 Observation (15 min per seq.) % of cows detected 1 time (midday, Mi)24 1 time (afternoon, A)42 1 time (morning, Mo)50 2 times (Mo & A)81 3 times (Mo, Mi & A)86 Lacerte (2003) Ducrot et al.(1999) Key figures : - 50 % of sensitivity (Se) - 95 % of accuracy (Ac)

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 Study Protocols Sensibility (Se) Accuracy (Ac) MethodFrequency/durationSigns Hetreau et al. (2010) Visual detection4 x 10 minStE76/ Camera in continue60 min StE86/ « Camera-icons »20 min StE77/ Bruyère et al. (2011) Visual detection4 x 10 minStE69 a 94 « Camera-icons » 20 minStE80 ab 93 « Camera-icons » + visual detection 20 min + 4 x 10 minStE89 b 93 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 * P 4 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 MethodsSe (%)Ac (%) Scrathcard35.963.9 Kamar56.761.3 Farmer56.592.9 Neck collar58.993.5 Pedometer63.373.5 Neck collar + farmer75.091.7 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. Characteristics of the farm and breeding management Evaluation of heat expression level Assessment of risk factors associated with low cyclicity rates and discrete estrus behavioural signs --> estimation of heat expression level MILK PRODUCTION AND ENERGY DEFICIT % of high producing cows 1 <15% Number of milkings per day2 % of cows with low protein ratio at the start of lactation 2 <15% HEALTH STATUS % of cows with placenta retention and/or chronic metritis<15% % of cows showing lamenessbetween 15 & 30% % of cows having other acute pathologies 3 <15% ANIMAL HOUSING (main type of housing at time of reproduction) Estimation of heat expression level (score/100)55 Detœstrus approach (1) Risk factors Level and penalties associated Score (/100) with green/orange/red code

26. Evaluation of heat detection quality Evaluation of heat expression level by cows Characteristics of the farm and breeding management Level of production by cow and year (kg)7,800 Level of heat expressionHigh Time indicator between calvings (d)95 Average interval calving – AI 1 (d)85 Minimal postpartum delay for AI 1 (d)50 Rate of success for AI 1 36 Rate of success for all AIs 1 38 % of intervals between AIs < 18 d0 % of intervals between AIs 18-26 d39 % of intervals between AIs 27-35 d16 % of heat detection up to the 1 st AI included 2 48-58 % of recurrent heats detected 2 29-39 % of inseminations outside of heat period 3 2-9 Detœstrus approach (2) Basic reproduction results including heat expression level  Estimation of heat detection efficiency at 1 st AI and on returns + Estimation of heat detection accuracy (green/orange/red code)

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

28. Efficiency Accuracy Risk factors analysis Evaluation of heat expression level by cows Evaluation of heat detection quality Characteristics of the farm and breeding management RESULTS This file automatically shows all risk factors from files 2-4-5 and the level of associated risk. Factors are not in order of importance. Estimation of resumption of cyclicity and expression of heatNote: 78 /100 Risk:HighMediumLow MILK PRODUCTION AND ENERGY DEFICIT % high producing cows X Number of milkings per day X % of cows with low protein ratio at the start of lactation X HEALTH STATUS % of cows with placenta retention and/or chronic metritis X % of cows showing lameness X % of cows having other acute pathologies 3 X ANIMAL HOUSING (main type of housing at time of reproduction) Type of housingX Type of building X Detœstrus approach (3)

29. Efficiency Accuracy Risk factors analysis Evaluation of heat expression level by cows Evaluation of heat detection quality Characteristics of the farm and breeding management RESULTS This file automatically shows all risk factors from files 2-4-5 and the level of associated risk. Factors are not in order of importance. Estimation of resumption of cyclicity and expression of heatNote: 78 /100 Risk:HighMediumLow MILK PRODUCTION AND ENERGY DEFICIT % high producing cows X Number of milkings per day X % of cows with low protein ratio at the start of lactation X HEALTH STATUS % of cows with placenta retention and/or chronic metritis X % of cows showing lameness X % of cows having other acute pathologies 3 X ANIMAL HOUSING (main type of housing at time of reproduction) Type of housingX Type of building X Detœstrus approach (3) Risk factors list Sum-up of the situation

30. Efficiency Accuracy Risk factors analysis Evaluation of heat expression level by cows Evaluation of heat detection quality Characteristics of the farm and breeding management Detœstrus approach (3) 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 Heat detection qualityCosts (€) per cow and per year High fertilityLow Fertility Se1 reduced by 33%-37-30 Se2 reduced by 33%-10-32 Ac reduced by 12%-4-14 Sum of the 3 problems-49-58 Seegers et al.(2010) 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

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