Persistency of lactation yields: A review

Persistency

oflactation

yields: a

review

N.

Gengler

IJB.

&

btechnie,

Frctltt

Untvcrstlatrc iles $icnccs Agrononiquca 8-5030 Genblors

Abrtnct

FcdEcocy

d

yiclds dudlg thc laaation is

s!

imported

ldadm

q|'vc _Ftatnc&r.

It

can bc dcfncd as tDc

abilig to tDaintair lhr lerrcl dproduaion during thc ldation- Rlal pcrsisaascf, for a constant le\Gl of productioq c8tr

be disiaguisbcd &,om

apannt

_Frsins|gy.

P.rsi$cocy was

dcdr.d

i!

Eany difrct€ot xay5.

Thtlc

Eajot malhcodlical d.f,nitionc arc bascd: a) on mathdtrstical lacarion

orw

nodcls: b)

o!

ratioc of panial totrl or othcr yiclds rhuing thc laaation aod c) on tbc variation of thosc yiclds. Dcfinition of pcrsisrcocy can bc occndcd

o

ftt

and

prucin yiclds. Heritsbility valu6 for pcrsist€nsy arc found beturcco 0.01

ad

> 0.30, wilh ooct rnlucs surnd 0.10. lvioc rralucs rcportcd for mcasrcs bascd oo variatioo and on longcr pcriods wcrc bigb6 rhen _{tbo6a for ratio ncasucs.}

Ilcritebility for

ht

ard €sp.ciatly _Fotcin pcrsistcncics erc lower

tia!

thosa rqortcd for milk pcrsilrency. Inportanca

of pcrsistcDcy is double. It aficcts eccl|racy of yield evaluations bascd on hcompletr laclation rccords (c.&, tcslday nodcls)

ad

it

has

ar

cconomic importancc on its own; as

it

rcduc€s fccd and othcr managcm€nt cods. Genclic

aaluations for persistcncy can bc concicved as scpcfatc aahutioq combined (multiple-{rait) with or deduccd Arom

yicld araluation.

l.

Introduction

The main income

for

most dairy farmers is based on

millg

fat and protein yields

of

their

cows. Especially since the

introduaion

of

milk

quotas

in

the

European

Unioq

maximum

improvement

of

yields

is

_not

necessarily economically optimum. As a matter

of

fact,

if

profits

are a

funstion

of

returns minus costs,

reduction

of

costs must

be

considered

to

improve proEts when increases

in

retums are limited.

A

way

to

reduce costs is

to

distribute

the

same

total yield

more equally

over

the

whole lactation. The distribution

of

lactation

yield

is known

under the name

of

persistency

of

lactation

yields,

often

simply

called persistency. The precise definition is often not

well

worked

out, but in

general

we

say that

the lactation

ofa

cow is more persistent

if,

for

the

same

total

yield, the

animal peaks lower

and

the

lactation curve

is

flatter.

Better persistenry

is

considered advantageous

for

s

certain number

of

reasons.

The

two

most

important are the better use of cheap roughage (e.g., S6lkner and

Fuchs, 1987) and the reduction

of

stress due

to

high peak production (e.g.,

Zimmermann and Sommer, 1975).

The concept

of

persistency

is

also often related to the mathematical description

ofthe

laaation curve. Several different measures are classically used

to

describe persistency. We

will

describe

the

most important

persistency mea$rres

and

we

will

deal

with the

genetic aspects

of

persistency and

its

introduction

in

current and

future

selection schemes

for

vield

traits.

2.

Delinition

of persistency

2.

L

Persistency of milk

yields

Different

approaches

exist

to

define persistency and there is a certain ambiguity.

In

general persistency

can

be

defined

as

the

ability

to

maintain

a

more

or

less

constant

yield during the lactation.

If

we

consider that

the

shape

of

the

curve

is diferent

&om

one

differcncc

otists

even

8t a

constant level

of

produclion (Grossman et aL,

t986).

Therefore

we

can define pcrsistcncy as a

function of

the flatncss

of

the

lacation

qrrve.

For

an animal this means that

it

is more persistent as an othcr

ifthe

lacration

orve

has a

flatter

shape.

In

the

literahrc

this

conccpt

is

calld

persistency

of

the lactatioq

or

p€rsist€ocy

of

the

lactation

curve

or

persistency

of

milk

yields

or

persistency.

It

is

clcar that

the

shape

of

thc lactation curve depords also on thc

total

yiel4

represcnted

by the

sr&cc

below

Therefore

Gengler

(1995)

disinguished

bawccn

ryparcnt

or

obscnred pcrsistency

and

real

persistency.

_{Thc 6rst}

is

defined

without

cxonsidering total yields constant

2.2. Persistenqt

offat

and protein

yields

It

is

obvious

rhat

thc

same concepts

rs

for

milk

yields can be generalized

for fat

and

protei4 wen

if

such studies have been rather

seldom

in

the

literature (e.g., Kandzi

and

Glodeh

1990;

Bouloc

and

Boichard,

l99l;

Swalve, 1994).

2.

Lrctetion currcs

The milk

production

of

a

cow

can be

subdMded

into

three parts.

An

ascending phase between calving and peak

yield, a

near

constant production around

peak

and

a descending

part after

peak

(e.g.,

Gengler,

1990). Lactations

failing

to

show

the

first

phase,

or

showing steadily

increasing

production

are

called atypical

lactations. Shanks

et

d.

(1981)

summarized proportions reported

in diferent

papers.

The literature

is

not

very

consistent conceming

the

relative

importance

of

atypical curves as proportions

from

15%

for

Fenis et

d.

(1985)

to

45Vo

for

Schneeberger

(1978)

are

given.

These

diferences

are due

to

different

definitions

of

atypical,

differences

in

populations

studies,

management

of

animals

and

recording procedures (e.g., Belgium).

But

the

existence

of

atypical lactations is a fundamental problem

mathematical

models used

must deal

with.

Iactation

curves are atso important in the field

of incomplete lactation octension (e.g., and Van

Vlech

1973; Schaeffer and

1976) and

of test{ay

models (e.g.,

Ptak

Schaefer, 1993).

3.

Mrthcmeticd

lectetion

cunc

modelc

3.I.Inffirctiot

Difcrcnt

approach*

have been used

to

6nd a

function

to

ft

obs€rved

daily

yidds.

Difercnt

objectives

of

thesc

functions

also

orist. Thc

threc most important

are:

l)

the adjustment

for

individual

cows

to

describc a

givan curve and

wentually

the persistency, 2)

the adjustment

of

a curve

for

groups

of

cows,

which

is often

done

for

management reasons and

3)

the estimation

of

305 day yields using

the adjusted

ctwe,

the

totd

yield

being only the surface below the curve.

Several

difierent models exists

(e.g.,

Wood

1967;

Grossman

and Koops,

1988;

Cobby and

Le

Du,

1978) and

we

will

try to

group them

to

make the comprehension

ofthe

models easier.

3. 2. Eryonential

_functions

The use

of

exponential

functions

goes back to Gaines (1927).

But

Wood (e.g., 1967) popularized this approach.

It

was then used by

several

other

authors

(e.9., Kellogg

a

al.,

1977).

tfis

basic

function

is

often

called

Wood's

incomplete

gamma

function.

If

we define the daily yield at day

t

by y1, the model

ts:

lt

=

atb

e'd

(l)

where

4

b

and

c

are

parameters,

a

being linked

to

peak yield, b

to

the increasing phase

slope

and

c to

the

decreasing

phase.

Parameters can be estimated using the natural logarithmic transformation (e.g., Shanks et al.,

l98l;

Grossman

et

al.,

1986;

Batra

et

al.,

1987;

Congleton

and Everett, 1980a

and

1980b):

This

trandormation yields

a

multiple

regression

of

In(y1)

on

ln(l)

and

t

with

paramAcrs ln(a), b and c that can be estimded

by

ordinary least

squares.

This

firnction

has

the

weakness

that

scasonal

effects have

a

strong influence

on

ys

and thereforc

the

following

modiEcation

was

proposed

(e.g., Grossman et

al.,

1986; Batrs et al., 1987):

yt

₌

ubed(l+usin(r)+vcodx))

(3)

where

u

and

v

are

additional

par.meters associated

with

seasonal

variation

other than

season

of

calving and

r

is the day

of

the year expressed as radians.

Wth

certain assumptions

we can

also

usc the

logarithmic transformation:

(yJ

_=ld")

+

b

ldl)

-

cr+udn(r)+vo(r)

(a)

The problem

with

this transformation is that the number ofparameters is augrnented so

therefore the

fit

should be better.

but

we lose residual degrees

of

freedorn, or

from

the more

practical

_{point of}

_vieq

_we

_need

_to

_know

_at

leas

6 test-days.

S chneeberger

(1981)

proposed another

modification

of

Wood's

gamma

function

to

take

the time

of

initial

production

(re

₎

into

account:

y,

₌

_{{1-10)b"{r'to)}

(s)

Schaeffer

et

d.

(19?7)

used

a

more

complex

exponential

functio4

called

a

one-compartment open model.

3.3.

Multiphesic

mdels

As the lactation can be easily subdivided

in

different phases, the idea was developed

to

use multiphasic models.

An

example

is

given by Grossman and Koops (1988):

r,

=i{",u,[r-t""n'z(u1(r-

c1))]]

(6)

where y1 is the production at day

q

a1,

b;

and

ci

are parameters associated

with

phase i; tanh

is the

hlpcrbolic

tangent and n the number

of

phases.

This

method

works

well

if

2

or

3 phases are used (Grossman and

Koops,

1988),

but 6 or 9 parametcrs must be estimated. This

fast limits

thc

use

of

this

approach

to

situations

wherc enough

obscn

ations

are known.

This

method

has

a

certain

number

of

advantlge

but

has

bccn

scldom used.

Thc

main

reason

is

that

despite

its

theoraical

rd*t"g.C

it

csn

not bc

linearizd

so

that

parameter cstim.tion is

dificult.

3.4.

Polynonial

od

iwvrse

polyonial

ndels

Ali

and Schaetrer (1987) used an inverse quadratic polynomial model

first

describd

by Nelder (1966) and used by Yadav et al.

(1977)

for

dairy cattle. This modet is written as:

ylr

₌

_Fr+

p6t-t

+

p2t

(7) where the parameter

p

represents peak yield,

p9

is associated

with

increasing and

p2

with

decreasing phase

and

ylt

is the

inversc

of

yield

at

day

t.

The

model

is

written as

a

multiple regression

ofy[l

on

I

and

t-1. It

has

the advantage that the number of parameters is

limited

to

3.

3. 5. Multip le rc gre ssion

Another interesting way

to

describe the

lactation curve

is

the

multiple

regression approach

first

described

by

Ali

and Schaeffer (1987). The regression model has the form:

yt

₌

po

+pr,'t

+p2yl

*yrr+parl

(8)

where: ₇₁

_=t|3oS

and

_,=tn13g57r1.

The

parameters

are

again

linked

to

the lactation

cuwe:

pq with thc peak yield, p1 and

p2

\,ith

decreasing

and

p3

and

pa

with

increasing phase.

This

model permits

a

good description

of

the

cune

according

to

results presented

by

Ali

and Schaeffer (1987),

but

it

F

obsenrations

to

cstimatc

thesc

parameten.

This model was uscd

in thc

modeling

of

test-day

_{CID) genaic}

waluation

as corrcction

for

days in

milk

at

tcstday

by Ptak and Schacffer

(1993).

In

this contod

corrections are done

for

all

TD

records

in

lactations

of

cows

grouped

in

givcn

agc.season

classcs

and

thercfore

the

mrmber

of

parameters

_is

not important.

A

new

dwelopment

is

the

random

regression

model

(c.g.,

Jamrozik

and

Schacfrer, 1995) whcre

the

lactation

qrrve

is

describd

phenotlpicaly

and geneticaly using

Ali

and Schaeffer

(1987) multiple

regression.

Fixcd regrcssion coef6cients are extimated

for

give,n region-age'season classes

and

random

(genaic)

regression coefficients

for

an animal

using

the

gcnetic

covariances

among

coefficients

and

the

numerator

relationship

matrix among rnimals.

3.6. Other

mdels

Several

other

models can

be

used and

have been

described

in

the

literature.

An

example

is

the

vibration model reported by

Hayashi

et

at

_{(1986). Cobby and}

_Le

_Du

(1978)

described

a

certain

number

of

alternative

models,

essentially

modified

exponential

function bascd

on

Wood's tunction (e.g., 1967).

4.

Pcnistency mcrsurcs

1,1.

Measres

fuyd

on

Iacwion

atrvv mdels

Thc most oornmon mathernatical model is based on

lVood

(1967). This model has thc

partiailuity

that

it

is rather easy

to

dwelop

a

simple formula

of

parameteis associated

with

penistency. This simple formula is:

P ₌

c-G+D

Another way persistency can be deduced

out of such an mathematical model is

g"en

by Grossman and Koops (1988). They associated

with their

multiphasic

model,

persistency

to

the duration of their second phase:

P=2bi

(10)

For

multiple

regression

models

persistenry

can

be

linked

to

slope

of

the decrcasing phasc of lactation.

Random

regression

models

(e.g.,

Jamrozik

and

Schaeffer,

1995) can

provide breeding values

for

individual

day yields and

for

partial,

total

or

_{other ladarion ields.}

Persistency measures can than be developped

out ofthese values.

Oher

formulas

exist

to

describe

persistency

out

of

lactation curve

models (Rowlands

et

al.,

1982),

but their

importance

for

the

description

of

persistency

is

rather limited. Another problem is that one should be

able

to

describe

all

types

of

lactations, including atypical ones.

4.2. Measures based

on

ralios

between total,

partial, mcimum,

or

other

yields

The

idea

to

use

ratios

as

measures

of

persistenry

is

old,

it

goes

at

least back to

(e)

Sandcrs (1930).

Hc

dcdncd pcrsistcncy

rs

the

ratio

benvecn mean and pcak

yicld.

This idea

wrs

uscd

by

S6lkner and Fuchs (1987). They

defned

two

pcrsistency measunes,

Prourxz

utd

PTgttA)G,

trs:

Ploulxz

₌

Max.

yield6.p

r.ro

r

₁₀₀

_(l

_l)

(12)

Mean yield _3r,

_r.ru

Higher

values

for

Ff€ntAJA

and

rycM$o,

arre

associued

with

lower

persistency. This not very logical fact must bc

remembered

when

comparing thern

to

other

mahods.

Keown

et al.

(1986) used

an

modification of this mahod:

r=

MT;{tjldo-''''.r*

(13)

Y I el _{O.r d.y 3OO}

This measure is also linked negatively

to

persistenry

and

shares

therefore

the

same

intuitive

weakness

as

PfUt4ArO

and

Prs,tcx3

Others suoh as fohansson and Hansson

(1940)

introduced

ratios

berween

partial yields. These methods were popular and many

variants

of

their

two

measures, P2.g and P3. _,

exist:

Dandl (1982) also modifed P2,,

but

with

thc

intention

of

crcating

a

meanrre

rssociucd

with

asc,cnding

and

descending plrasas

of

laciation called

Pl

and P2.

She defined:

D--

-.---

_-

Max'

Yiclda:n

r-rs

,r*

rrordAx3 =

_{ffit;e--d.,'tln,}

-

Partialyieldar.16l_26

'2r=lE;;@;

^

Partial

yield*,rouro

",r=

lfrti'yidl;_-,,=Pffig',*

,,,

₌ Partial

ytcld,r-

rzr-zro

.

roo

-'

_Psrtial

_{yielddry,3l.l,}

(17)

(18)

All

thc

r*io

mahods

based

on

P21 and

P3

sssociste

higher values

to

bctter

persistency.

Other

ratio

methods exist,

but

all

share

the

samc basic

definitions

of

ratios

between

ccrtain partial,

peah

daily,

totd

or

other

yields.

1.3.

Measres

based on

vwiation of

yields

The

definition

of

persistency as flatness

of

the

ctrve

can

be

easily

described

by

measures of dispersion. This rather simple idea

seems

to

have been seldom used

in

the

past.

Only

since

is

use

by

S6lkner

and Fuchs in

1987,

it

has been

reconsidered

by

other scientists (Gengler, 1990; Swalve, 1994).

Sdlkner and Fuchs

(1987)

defined

two

persistency mea$lres based

on the

standard deviations of tes-day yields:

Some

scientist

(e.g., Ericson

et

at.,

1988) used the same ratios but expressed them

as perc€ntages.

Others modified

therq

as the persistency measure defined by Mahadwan ( I 95 _{I )} :

-

Pfitial idddD. r.re-Partial yiddd$ r.D

t=@rro,

Psoz

₌

(le)

where

TD;

is the ith test-day

_feld,

pp

is the

mean test-day yield and

p

is the number

ofthe

last test-day before 200 days in milk.

Pso:

₌

(20)

where

TD;

is the ith test-day

yield,

pp

is the

mean test-day yield and p is the number

ofthe

last

test-day

before

305

days

in

milk.

This measure has a weak point, the lactation needs

to

be finished,

or

at least considered finished.

Both

methods are affected

by

the fact that

a (14)

(15)

1i(-,-r*)'

gircn

_tcstday

_{do€s not neccssary take place at}

the samc stage of tactation.

An

Lnportant

rdrnntagc

of

these

measres

is

that their

distribution

_can

_be

_cmpirically

considered

ncarcr

to

a

normal one tlran

foi

ratio measurcs (S6lkner and Fuchs,

l9g7). But

at

the

same

time

all

pcrsistency

measures

based

on

v?riation

mahods

sharc

the

disadvutsge that their

_{vatues become nearer}

to zero

with

higher persisency.

_

But

according

_to

_Gengler

(1990)

and

Swalvc

_{(1994), the}

_mcasures

_based

_on

_thc

valation

of

tcst{ays

arc in0uenced by erratic

variations

that

rrc

obscrved

from

test-dav

to

testday.

Gengler

et al.,

1995 used thereforc the _{persistency measure called}

pyv

_measured as yield variation and defined as:

*=1{,S.(H'.(g-,*'f

(2t)

where

M

is the partial

milk yield from

day

I

to

day

100,

M2

is the partial

milk

yield from

day

l0l

to

day

200,

M3

is

the partial milk

yield from day 201

to

305 and

M1

is the total

milk yield from day

I

to

day

305.

Gengler

(1995)

_{modified slighrly}

_the

_definition

replacing

_{the 0.5}

_by

_0.25.

_He

_used

_a suggestion _{made earlier (Gengler, 1990) and} expressed _{persistency on a relative scale} intra-lactation.

5.

_{Influencc of}

_milk

yield

on pcrsistency

The

problem

_of

_links

_between

persistency

_of

_{lactation and}

_milk

_yields

_has

interested scientists

for

many years

(e.g., Gaines, 1927; Mahadevaq

l95l).

There are at least

two

reasons

for

this

link. Total

vield

is

the

area

_{below the cuwe,}

_and

_the

_leld

_at

every moment

of

the lactation is a function

of

the

qlrve.

On the other hand

it

is clear that an

animal

with

very high production at peak have

likely

_{a steeper slope than another that is}

_low

producing (Genglea _{1990). Therefore we can}

assume

that the milk yield

has

an

important inlluence on persistency.

Many

scientists

have

andlzcd

rclationships berween persistency, measured through

diferent

param€tcrs, and

initial,

partia[

_total,

_ac.

_milk

yields.

The

rcslltiii

found

does

not

show

a

consistcnt

pattern.

Early

studies

found

a

negative

relationship bctween persistency and

total yield

(Gaheq

1927).

l-atu

lvlahadevan

(1951) found

a

positivc relationship. This result is

verified

by

Schnecberger (1981).

_Now

_it

_is

_{clear that the}

rdationship

betcreen

total

_lcld

and

pcrsistenq

depecrds

asscntially

on

the pcrsisrency

mea!

re used

(solkner

and

Fuc\

1987). Persistency measures based

on

ratios

showed

a

positive

rclationstrip,

but

the

methods based

on

variation

a

negative

.1

relationship. Gengler

(1990)

showed

that

this later relationship is close

to

a linear one.

If

we

_,

take

the definition

given

by

Grossman

et

al.

_:

(1986),

persistency

_should

_{be independentty I}

measured,

or

oorrected

after,

for milk

yields. Therefore a

good

_{persistency measure should}

be independent

from

yields,

or

corrected

for

the influence

ofyields.

As

a

conclusion

we

_can

state

that

persistency

is

dependent

on

yields,

especially

total

yields,

but

the

direction

of

the

relationship depends

on the

measures used.

The

ratio

meaiures

show

a

positive

one,

whereas

the

variation

measures

show

a

negative relationship.

The

reason

for

this

could be

that the

first

are

higNy

atrected by

the level

of

production

_{and the}

_{second are}

inlluenced by variation in production,

with

this

variation

more

important

for

high

producing cows.

The

influence

of

total

yields

has two

components,

a

genetic

and

a

non

genetic, therefore

if

phenotypic relationships are forced

to

zero

through

the

definition

of

real

t

persistency, genetic relationships exists. Table

l.

gives results obtained

by

Gengler

(1995)

:

using

a

persistency measure

based

on

a

i

function

ofthe

variation

ofyields

6.

Environmental

inlluences on

pcnistenqr

6.1. Parity and age at

caling

t

I

,:i

The

effect

of

rgc

8nd

parity

on

p€rsittency

_hrs

been

*udied

by

several

scicntists.

Certain

scientists separste

thesc

effects

or

worked

with

separate lactations

(e.g.,

Sdlkner and Fuclrst 1987;

Gengler, 1990),

while

others considered them together

(Suders,

1930).

All

authors ageed

tlut

the

first

lactation

is more persistent than thc others (e.g., Shanls ct al.,

l98l;

Dancll,

1982; Keown et al., 1985;

S6lkncr and Fuchsi 1987). The most conunon

ocplanation

is

based

on

I

lowcr

level

of

devclopment

of

the

mammary gland

for

first

puity

cows (e.g., S6lkner and Fuchs, 1987).

The

results concerning

the

influence

of

age within parity

are

not so

similar.

Mahadwan (1951)

for

orample did not find an influence,

Danell (1982)

and Grossman

et

al.

(1986) found

a

very

mdl

influence

that

is

more important

for

younger cows. Grossman

et al. (1986) did

not find

it

sigrificant.

Others such as Smith and Legates (1962) described a reduction

in

persistenc,

with

age

during

the

first lactation and an augmentation during later

ones. Gengler

(1990) found

results

that supported

the

later

hypothesis.

All

authors

agre€

that

it

would be

important

to

study

effects

on

persistency

of

_{parity and}

age

separately.

6.2. Season oJ

calving

Most

research

that

has

been

done

confrms the influence

of

season

of

calving on persistency (e.g., Mahadevar\

l95l;

Cady and

Mc Dowell,

1980; Danell, 1982;

Fenis

et al.,

1985;

Grossman

et

at.,

1986; S6lkner

and

Fuchs, 1987;

Gengler,

1990).

Only

Schneeberger (1981) did not

confrm

this.

But

the different

studies

did not find

the

same

influence. Gengler

(1990)

explained

that obviously such differences are due

to

climatic

and

management

differences

among

the

population studied. Some papers

described studies done in

North

America (e.g.,

Fenis

et

al., 1985;

Keown

et

d.,

1986) who found that

the

most

persistent lactations begins

at

end

sunrmer.

But

European studies as Mahadevan (1951), Danell (1982) and S6lkner and Fuchs

(1987)

found

that

the

most

persistent lactations begin

in

fall-winter. Gengler (1990)

crplained

that

these results

are also afhcted

by

the length

of

the

measured

poiod.

As

a

matter of fact the way persiitency is measured also atrecrs the time of the apparent modmum

persistcncy

(Solkner

and

Fuchs,

1987). Longer intervals tend

to

shift this mo<imum

to

eadier dates in the year.

6,3, InJluence

of

gesaion

The new c8lf

thst

a

cow

is

carrying during the last

part

of its

lactation is

able

to

influencc

the

potential

milk

production

of

a

cow. Thc influence on daily yields during this

period

is

direct and

creates

an

indirect influence

on

persistency.

Sweral

ways

have

been

described

to

measure

this

influence. Some scientists used the days open approach

(e.g.,

Schneeberger,

l98l;

Danell,

1982;

Grossman

et al.,

1986; Sdlkner and

Fuchs,

1987), others used days

carried

calf

(e.g.,

Keown

et

d.,

1986). Gengler

(1990)

took

calving

interval

as

the

parameter.

Animals

were

often

grouped

in

classes

(e.g., Schneeberger, 198

l;

Danell,

1982;

Kmwn

et

al.,

1986; Gengler, 1990). Sdlkner and Fuchs

(1987) used a linear and quadratic regression on days open.

The

results found

were

not

similar. Some people found no influence (e.g., Danell, 1982; Grossman

et al.,

1986), others such as

Sdlkner

and

Fuchs

(1987),

Schneeberger (1981), and Smith and Legates (1962) showed

that gestation influenced persistency. S6lkner

and

Fuchs (1987)

observed

a

linear

relationship.

They also

showed

that

the persistency measures

covering

a

long

period are

more

afec-ted than

those covering

short

periods. Gengler (1990)

found a

relationship

of

gestation

with

persistency

only in the first

lactation and

it

had a quadratic component.

7.

Seritrbility

of persistency

The

literature

on

persistency measures shows

that the

different

measures have also

diflerent levels

of

heritability.

This

additional source

of

variation

for the

heritability

makes

difficult.

_{Genglcr (1990) gave a review}

_of

_the

Utcrature.

Results

werc

bctween

undcr

0.01

(Shanls _et

_al.,

_l98l)

_{and over 0.30 (Smith and}

I*gatesi

^1962).

Another

importani point

is

that

holding

thc milk yicld

constant reduced

the.heritability @anell, l9B2).

According

to

Sdlkner and Fuchs

(1987),

heritabilities

iere

rather constsnt

fiom

lactation

_to

lactatio4

but

rathcr diferent

betwecn

_thc

pcrsistency measures

thcy

snrdied. _{Ttrcy found that longer} mcalnrres

including

thc

whole

lactation

and

rrariation

_mahods

_{had the}

_highest

heritabilitieg

p5p3

_had

_a

_{heritability of}

-O.Zt,

0.22 and 0.22

in

_{the three first i""t"tions.}

Gcnglcr (1995) found that vatues

for

apparent

Tg

r""l

persistency arc close

if

persijcncy

is

defined

as

variuion

_of

partiat

yieias. Heritab.rlity

of

reat persistency

_{for milk}

_yields

*"t yt

0.14

higher than

fat

(O.O6j and

protein (0.04)

(table

_I

and Tabte

2).

Similar

lfuls

were reported

by

Swalve

(199a)

and

Kandzi and Glodek (1990).

t.

Rcpcetability

_of

pcrsistency

Another important question

is

what

is

the repeatability

of

persistency

_from

_lactation

to

lactation.

Most

research

_{has been}

_done

l11iol

by

tactation (S6lkner and

Fuchs,

1987; Gengler, 1990),

but

it

is

interesting

_to

assess _{how similar are}

_two

_persistency

_rec;rds

are

for

the _{same animal. Resutts}

_from

_Gengler

(1995)

show

that

real pcnistencies

_{for milk}

fat

and protein yields

had

slightly

higher repeatabilirics

(Iable

2)

compared

to

apparent persistencies _Cfable

_l).

_{Thc rather}

_low

_iesults

for

repeatability

_indicate

_that

_at

_least persistency

for first

and later _{lactations can be}

considered

diferent

traits.

9.

Corrclrtions

bctwccn

_{mi[q fat}

_and

protein

persistcncics

Very

few

results are known showing the

correlations between

_mil(

_fat

_and

_protein

persistencies.

_Table

I

_and

_Table

₂

_{show the} values obtained by Gengler (1995). _{They show}

that

genetic correlations

are

higher

than

phenotypic _{correlations. Correlations are}

higher betwecn

protein

_and

persistency between milk and fat or protein.

10. Economic

importrncc

of

pcrsistcncy

_-

Economic

importanc€

of

persistency

linked

to

the

reduction

of

costs obtained

bater

pcnistcncy.

_{Two tlpes}

_of

odsts. Frst bctter

persistency reduces costs 8s shovm

by

Sdlkner and Fuchs

(l

and Genglcr (1995)

who

linked persistency

to

the replaccmcnt

of

conc€Nrtratcs

by

rough4gc.

Gengla

(1995) found that this reduction gives

persistency

_a

_{relativc weight}

_compared

_to

leld

of

around

3

/o.

_B*ter

persistency reduces

also health

and

reproductive

costs.

Here he found

around

7

Yo

of

the

relative economic value of yield.

ll.

Conclusion

In

the

literature

persistency

_is

_mostty

defined as lactation

or milk

yield

persistency, and persistencies

of fat

and

protein yield

are

seldom

considered.

Different

persistency measures

were

described

in

_the

literature. Three great

tpes

of

measures can be defined:

l)

me^cures based

on ratios

between total,

partial

_or

other

_{felds; 2)}

_{measures based on}

variation

of

testday

_lelds

and

3)

measures based _{on mathematical modets. Environmental}

infuences on persistency are

well

described in

the literaturg

even

if

_{there are}

diferences

between persistenry measures and population studied.

The

_{most important effects are milk}

yields and seasonal effects. The heritabilities

of

persistency measures _{reported in the literature}

range from under 0.05

to

over 0.30.

Acknowledgemcnt

The author is

Chargi

de Rccherches

of

the

Fonds

National

_{Belge de}

_la

_Recherche

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Tablc

I

Gcnaic corrclalions (aborr), hcritabilitics and rEFatabilities (on rcpcatabilitics b€twcen brcakels) and phenotypic correlations (bclow thc diagonal) among apparcnt milh fat and pmrein _Frsincncies

Traits

Milk

Dcrsistcncr

Fat

ocrsistencr

Protcin Dcrsistcncv

Milk pcrsistency 0.t4

(0.24)

0.E3

Fatpc6istcncy

0.52 Protcin

persistency

0-52 0.06 (0.14) 0.70 0.E9 0.8E 0.05 (0. _r0) Tablc 2

Gcnctic correlations (above), beritabilitics and npcatabilitiG (oq repealabilitics betrvccn breakcts) and phenogpic corrclatiors (below lhc diagonal) among rEal milh faa and protcin pcrsistcncics

Mi Milk p€rsistcncy Fat _Frsistens, Protein persistcnsy 0. t4

(0.26)

0. 0.51 0.52 0.06 (0.15) 0.72 0.m 0.E6 0.04 (0.10)