Advances in natural heat detection

Claire Ponsart, Pascal Salvetti

Physiological background

6 to 10 hours to reach the oocyte

Viability: 24 hours

1 oocyte ± 21 daysViability: 6 hours only

Kölle (AETE, 2010)

Kölle (AETE, 2010)

When to inseminate ?

How to detect ovulations ?

Estr

ous

Oes

trou

s

• P4 concentrations monitoring• “Estrus” monitoring

P4 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 ?

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

-5-4.5

-4-3.5

-3-2.5

-2-1.5

-1-0.5

01.5

11.5

22.5

33.5

44.5

55.5

66.5

77.5

88.5

99.5

1010.5

1111.5

1212.5

1313.5

1414.5

1515.5

1616.5

1717.5

18

02468

10121416182022

Hours from the beginning of estrus

Fre

quency

Behavioural changes during estrus

Standing estrus Secondary sexual signsMounting 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

versus36h control video

+ time spent standing up

Behavioural changes: which signs to detect ?Behaviours type Race Estrous phase luteal phase

Social signs (%)

CHL 59 ± 11 92 ± 9CHB 47 ± 1 1 90 ± 10LI 37 ± 11 90 ± 11BA 47 ± 8 84 ± 10

Secondary sexual signs (%)

CHL 30 ± 10 8 ± 9CHB 33 ± 7 10 ± 10LI 45 ± 8 9 ± 12BA 40 ± 7 16 ± 10

Mounting signs (without StE) (%)

CHL 9 ± 5 0 ± 0CHB 15 ± 7 0 ± 0LI 14 ± 4 0 ± 0BA 11 ± 3 0 ± 0

Standing Estrus(%)

CHL 2 ± 2 0 ± 0CHB 5 ± 5 0 ± 0LI 4 ± 3 0 ± 0BA 2 ± 1 0 ± 0

Specific

Not specific

Rare

Repetition of SS signs is 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

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)

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)

CHA7,6 ± 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

Année de publication

Du

rée

AC

-AC

(h

)

1920 1940 1960 1980 2000 2020

0

5

10

15

20

?

Cutullic et al. (2010)

Year of publication

Est

rus

du

rati

on

(S

tE-S

tE)

Heat detection difficulties: frequent cyclicity abnormalities

Race nb Normal InactivityProlonged

Luteal Phase

Abondance 26 22 (80 %) 1 (4 %) 1 (4 %)

Charolaise 96 54 (56 %) 42 (44 %) 0

Motbéliarde 36 24 (67 %) 9 (25 %) 0

Normande 105 85 (81 %) 8 (8 %) 8 (8 %)

Prim Holstein

138 76 (55 %) 26 (19 %) 32 (23 %)

400 261 (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 %

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 21 2.2

Montbéliarde

37 21.0 21 2.5

Normande 155 21.4 21 2.1

Prim Holstein

136 22.6 23 2.3Disenhaus 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

Observation(15 min per seq.)

% of cows detected

1 time (midday, Mi) 241 time (afternoon, A) 421 time (morning, Mo) 50

2 times (Mo & A) 813 times (Mo, Mi & A) 86 0

20

40

60

80

100

0 1 2 3 4 5 6Months in milk

% o

f cow

s not

det

ecte

d in

es

trus

> 60 min/day30-60 min/day< 30 min/day

Lacerte (2003)

Ducrot et al.(1999)

Key figures : - 50 % of sensitivity (Se)- 95 % of accuracy (Ac)

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 farmStudy

ProtocolsSensibility

(Se) Accuracy (Ac)Method Frequency/duration Signs

Hetreau et al.

(2010)

Visual detection 4 x 10 min StE 76 /

Camera in continue 60 min StE 86 /

« Camera-icons » 20 min StE 77 /

Bruyère et al.

(2011)

Visual detection 4 x 10 min StE 69a 94

« Camera-icons » 20 min StE 80ab 93« Camera-icons » + visual

detection 20 min + 4 x 10 min StE 89b 93

• 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

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 day 2% 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 lameness between 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

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) 7,800Level of heat expression HighTime indicator between calvings (d) 95Average interval calving – AI 1 (d) 85Minimal postpartum delay for AI 1 (d) 50Rate of success for AI 1 36Rate of success for all AIs 1 38% of intervals between AIs < 18 d 0% 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

Detœstrus approach (2)Basic reproduction results including heat expression level

Estimation of heat detection efficiency at 1st AI and on returns +

Estimation of heat detection accuracy (green/orange/red code)

EfficiencyAccuracy

Risk factors analysis

Evaluation of heat expression level by

cows

Evaluation of heat detection quality

Characteristics of the farm and breeding management

RESULTSThis 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 /100Risk: High Medium Low

MILK PRODUCTION AND ENERGY DEFICIT % high producing cows XNumber of milkings per day X% of cows with low protein ratio at the start of lactation XHEALTH STATUS

% of cows with placenta retention and/or chronic metritis X% of cows showing lameness X % of cows having other acute pathologies 3 XANIMAL HOUSING (main type of housing at time of reproduction)Type of housing X Type of building X

Detœstrus approach (3)

Summary and advices to farmer

Actions plan

Risk factors list

Sum-up of the situation

How to help breeders ?

• Increasing breeder’s awareness regarding economic losses involved by a default of heat detection

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 -58Seegers 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

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