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 ? Estrous Oestrous P4 concentrations monitoring “Estrus” monitoring

P4 monitoring: Herd Navigator® On field measurements in milk (automatic sampling according to animal status): LDH, BHB, Urea and Progesterone. Friggens et al. (2008) cited by Martin et al. (in press) 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 ?

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 ++

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

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 Secondary sexual signs Mounting signs Standing estrus Agonistic social signs Affinity social signs + time spent standing up

Behavioural changes: which signs to detect ? Not specific Behaviours type Race Estrous phase luteal phase Social signs (%) CHL 59 ± 11 92 ± 9 CHB 47 ± 1 1 90 ± 10 LI 37 ± 11 90 ± 11 BA 47 ± 8 84 ± 10 Secondary sexual signs (%) 30 ± 10 8 ± 9 33 ± 7 10 ± 10 45 ± 8 9 ± 12 40 ± 7 16 ± 10 Mounting signs (without StE) (%) 9 ± 5 0 ± 0 15 ± 7 14 ± 4 11 ± 3 Standing Estrus(%) 2 ± 2 5 ± 5 4 ± 3 2 ± 1 Repetition of SS signs is specific Rare Specific

Behavioural changes: less lying time periods Race % of time spent « standing-up » Œstral phase Luteal phase CHL 88 ± 11 % 48 ± 25 % CHB 82 ± 12 % 53 ± 11 % LI 84 ± 11 % 61 ± 20 % BA 91 ± 8 % 59 ± 23 % + 30 %

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

Heat detection difficulties and milk production All sexual signs Mounting signs only (except StE) Probability of detection (ovulation) Standing estrus only (StE) 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)

Heat detection difficulties: a decreased estrus duration In beef cattle In dairy cattle 4 to 8 h (StE) 14 h (SSS) Race Standing estrus (StE) Secondary sexual signs (SSS) CHA 7,6 ± 4,6 h 12,4 ± 3,9 h CHB 9,9 ± 3,7 h 12,1 ± 4,1 h LI 8,2 ± 6,3 h 11,1 ± 4,0 h BA 6,2 ± 3,4 h 11,0 ± 2,4 h Cutullic et al. (2010) Year of publication Estrus duration (StE-StE)

Heat detection difficulties: frequent cyclicity abnormalities Race nb Normal Inactivity Prolonged Luteal Phase Abondance 26 22 (80 %) 1 (4 %) Charolaise 96 54 (56 %) 42 (44 %) Motbéliarde 36 24 (67 %) 9 (25 %) Normande 105 85 (81 %) 8 (8 %) Prim Holstein 138 76 (55 %) 26 (19 %) 32 (23 %) 400 261 (65 %) 86 (12 %) 41 (10 %) 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)

Heat detection difficulties: Changes in estrus cycle length Race nb Mean Median S.D. Abondance 35 20.8 21 1.9 Charolaise 77 20.2 2.2 Montbéliarde 37 21.0 2.5 Normande 155 21.4 2.1 Prim Holstein 136 22.6 23 2.3 Disenhaus et al. (2008)

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)

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)

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)

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

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)

Estrus detection by cameras: Results from one single farm Study Protocols Sensibility (Se) Accuracy (Ac) Method Frequency/duration Signs Hetreau et al. (2010) Visual detection 4 x 10 min StE 76 / Camera in continue 60 min  86 « Camera-icons » 20 min  77 Bruyère et al. (2011) 69a 94  20 min 80ab 93 « Camera-icons » + visual detection 20 min + 4 x 10 min 89b Good performances but time-consuming…

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

Automated activity monitoring Few study, great variability in results... Effects of breeding system ? Breed ? Health?... Comparison of 4 methods of detection Methods Se (%) Ac (%) Scrathcard 35.9 63.9 Kamar 56.7 61.3 Farmer 56.5 92.9 Neck collar 58.9 93.5 Pedometer 63.3 73.5 Neck collar + farmer 75.0 91.7 Holman et al. (2011) 67 Holstein cows Optimal combination

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

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

Estimation of heat expression level (score/100) Detœstrus approach (1) 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 day 2 % of cows with low protein ratio at the start of lactation 2 HEALTH STATUS % of cows with placenta retention and/or chronic metritis % of cows showing lameness between 15 & 30% % of cows having other acute pathologies 3 ANIMAL HOUSING (main type of housing at time of reproduction) Estimation of heat expression level (score/100) 55 Characteristics of the farm and breeding management Risk factors Level and penalties associated Evaluation of heat expression level Score (/100) with green/orange/red code

Detœstrus approach (2) Basic reproduction results including heat expression level  Characteristics of the farm and breeding management Level of production by cow and year (kg) 7,800 Level of heat expression High 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 d % of intervals between AIs 18-26 d 39 % of intervals between AIs 27-35 d 16 % of heat detection up to the 1st AI included 2 48-58 % of recurrent heats detected 2 29-39 % of inseminations outside of heat period 3 2-9 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)

Sum-up of the situation Detœstrus approach (3) 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 heat Note: 78 /100 Risk: High Medium Low MILK PRODUCTION AND ENERGY DEFICIT   % high producing cows X Number of milkings per day % of cows with low protein ratio at the start of lactation HEALTH STATUS % of cows with placenta retention and/or chronic metritis % of cows showing lameness % of cows having other acute pathologies 3 ANIMAL HOUSING (main type of housing at time of reproduction) Type of housing Type of building 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 Summary and advices to farmer Actions plan Risk factors analysis Efficiency Accuracy

Costs (€) per cow and per year 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 Heat detection quality Costs (€) per cow and per year High fertility Low 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)

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

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