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.
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 ?

5. P4 monitoring: other ‘on farm’ tools
Mini labs for ‘on farm’ P4 assays:
Concordance rate between ELISA in-lab assay (UNCEIA) and eProCheck® : % in milk (Gatien et al., 2012) % 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
Secondary sexual signs
Mounting signs
Standing estrus
Agonistic social signs
Affinity social signs
+ time spent standing up

8. 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

9. 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 %

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
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)

12. 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)

13. 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)

14. 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)

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)
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)

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)
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…

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
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

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. 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

26. 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 d 39
% of intervals between AIs 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)

27. Sum-up of the situation
Detœstrus approach (3)
RESULTS
This file automatically shows all risk factors from files and the level of associated risk. Factors are not in order of importance.
Estimation of resumption of cyclicity and expression of heat
Note: /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

28. 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)

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

30. 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