Livestock Research for Rural Development 25 (5) 2013 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Effect of environmental factors on lactation curves of milk production traits in Holstein-Friesian cows reared under North African conditions

M Bouallegue, B Haddad, M S Aschi and M Ben Hamouda*

Institut National Agronomique de Tunisie,
43 Av. Ch. Nicole, 1082 Tunis Mahragène, Tunisie.
cfpaa2006@yahoo.fr
* Institution de La Recherche et de l’Enseignement Supérieur Agricoles, (IRESA),
Ministère de l’Agriculture, Tunisie

Abstract

The Wilmink function was used to fit lactation curves of Holstein- Friesian cows in Tunisia using the Levenberg-Marquardt’s iterative method in the nonlinear regression procedure in SAS. The 305 days yields for all production traits were estimated with the Wilmink function. The influence of the non genetic factors on the 305-day milk, fat and protein yields in relation with the classical curve’s traits was investigated and a procedure for editing environmental factors in groups (Calving age-parity, herd-calving year and calving season) was derived. Test-day records of 259,776 observations of 5,649 Tunisian Holstein-Friesian cows were used in the analysis.

The results showed that the WIL function was a good fit for the lactation curves in different grouped effects and explained 98 % of variation in daily yield of milk, 96 % for fat and 97 to 98 % for protein yields. The average yields for milk, fat and protein shoed many fluctuations in different environment effect groups. Calving age-parity, herd-calving year and calving season was highly significant (P <0.01) for the 305-day milk, fat and  protein yields and for all curve traits, except for the persistency of fat yield which was obviously not affected by the herd-calving year factor (P>0.05). The highest significance of environmental effects on lactation curve parameters for milk traits allows having different shape lactation curves. The Shapes of the WIL lactation curve suggest that cow with a higher initial level of production attain faster the peak and thereafter decline at a slower rate than those with a lower initial level of production. Cows have lower peak milk but greater persistency in first than later calving age-parity groups. In all environmental factors Fat trend to be more persistent than milk and a little difference was observed between fat and protein. The 305 days yield of milk, fat and protein were positive and highly correlated with yield at the beginning of lactation (r=0.74, 0.50 and 0.57 respectively for milk, fat and protein yields) and peak yield (r=0.98).

Keywords: test day, lactation curve, environnemental effects


Introduction

The direct modelling of individual test-day (TD) records was proposed as a replacement for the traditional use of estimated accumulated 305 day yields. Some advantages of TD models include the ability to account for environmental effects of each TD, the ability to model the trajectory of the lactation for individual genotypes or groups of animals and a more precise adjustment for temporary environmental effects on TD (Jensen 2001).

Knowledge of the lactation curve of lactating dairy animals is required for feeding, breeding and economic management of a dairy herd. Lactation curves are especially important when making decisions. Dairy cows with a flat lactation curve are considered to have more persistent lactations and produce milk with a lower cost (Tekerli et al 2000).

Milk yield of individual cows is influenced by many factors (Van Tassell et al 1995), including genetic ability, parity, DIM (Days in Milk), age, physiological state, and management. In the lactation curve studies, calving age is generally used as a variation factor than the age of the animal; the parity was reported to be a significant source of variation and the lactation curves are usually developed using the average TD data on the milk yield within parity groups (1, 2 and ≥ 3) (Van Tassell et al 1995).

The combined exponential and linear model proposed by Wilmink (1987) is one among several models reported in the literature to describe the lactation curve of dairy cows. The Wilmink (WIL) model was developed in Holland and was the original function employed to describe the lactation curves in the official program of the genetic evaluation in Canada (Schaeffer et al 2000). In this form, it is considered the best three parameters of lactation curve (Olori et al 1999) and it is used in the random regression analysis. Moreover, WIL model is also used in some studies detecting the shape of the lactation curve (Macciotta et al 2005 and 2006, Roshanfekr et al 2010). This model is specifically conceived to model the lactation curve and WIL parameters can also be easily related to the characteristics of the lactation curve shape (Macciotta et al 2005).


Objectives

The main objective of this study was to analyze some environmental factors affecting lactation curve’s traits and the 305-day yields, for Holstein-Friesian cows raised under the Tunisian conditions by fitting Wilmink model to test day records.


Materials and Methods

Data

Data were 259,776 TD records of milk trait yields recorded on 5,649 Holstein-Friesian cows were collected by the National Centre of Genetic Improvement of Tunisia (CNAG: Sidi Thabet, Tunis) on 188 herds during the years 1994-2002. Lactation records with less, than 10 consecutive test-days were not included in the analysis. Likewise, test-day yields recoded up to 5 days following calving, biologically unacceptable fat or protein yields and a very low milk yield (< 3 Kg) were omitted. The lactation period was limited to 5-305 day after calving. Table 1 summarized the average milk yield (MY), fat yield (FY) and protein yield (PY) and their standard deviations for six calving age-parity groups.
 

Lactation curve Model

The WIL model (Wilmink 1987) was used to fit individual lactation curves for milk, fat and protein was used:
                                               
Where: e is the Neper number, a, b and c are the parameters that determinate the curve’s shape.

Curves were fitted using the Levenberg-Marquardt’s iterative method via the NLIN procedure of SAS [SAS 2001]. The parameter K is connected to the time of peak lactation and usually assumes a fixed value, derived from a preliminary analysis made on average production The peak yield (Peak) and the time to which one observes this maximum (DIMP) was calculated as:



Persistency for milk traits was calculated as the yield at one test expressed as a percentage of yield at an earlier test. The main criteria used to evaluate the goodness of fit of WIL model were, the coefficient of determination (1- the ratio of the residual sum of squares to the total sum of squares) and the residual (RES), defined as the absolute values of the difference between the predicted and real yields. The 305-days yields () were estimated by the WIL model to the following formula:
.

Factors affecting lactation curves

To evaluate the significant effect of age at calving, parity, month of calving, year of calving and herd on the lactation curve parameters and the 305-days yields, data used in this work (Table 1) were grouped to six groups (G1 to G6) of calving age connected with the parity of cow at TD  control. Three groups (HCY1 to HCY3) of herd-calving year were formed according to the level of production in herds on calving years. Season groups were defined as for 3-month intervals: winter (December-February), spring (March-May), summer (June-August), and fall (September-November).

The effects of calving age-parity, herd-calving year, calving season and first test-day date on milk, fat and protein yields and lactation curve parameters were analyzed using a general linear model:

                                               

Table 1: Means and standard deviation for milk, fat and protein yields by calving age-parity groups

G

N

Age

parity

* P (%)

 

Mean

 

Milk (Kg)

Fat (Kg)

Protein (Kg)

G1

78459

16-36

1

97.09

18.6±6.62

0.62±0.25

0.56±0.20

2298

 

2

2.84

18.4±6.83

0.63±0.27

0.58±0.22

52

 

3

0.06

17±5.05

0.56±0.19

0.56±0.17

G2

3728

37-49

1

6.27

18.2±6.83

0.61±0.25

0.56±0.22

52456

 

2

88.24

20.5±7.62

0.7±0.29

0.65±0.24

3264

 

3

5.49

20.2±7.93

0.7±0.31

0.54±0.25

 

G3

216

50-62

1

0.49

19.1 ±7.29

0.74±0.33

0.63±0.24

6518

 

2

14.74

21.1±8.24

0.72±0.33

0.66±0.27

33942

 

3

76.76

21.1±8.04

0.73±0.32

0.67±0.25

3531

 

4

7.99

19.1±7.79

0.68±0.31

0.63±0.24

9

 

5

0.02

17.1±8.71

0.57±0.22

0.44±0.14

 

G4

252

63-74

2

0.89

21.5±8.71

0.78±0.37

0.71±0.28

6062

 

3

21.31

21.2±8.34

0.73±0.33

0.66±0.26

19841

 

4

69.73

20.5±7.98

0.71±0.31

0.65±0.25

2268

 

5

7.97

19.9±7.70

0.68±0.29

0.6±0.23

29

 

6

0.10

27.2±7.04

0.75±0.26

0.84±0.25

 

G5

398

75-87

3

1.80

20.3±7.77

0.72±0.32

0.65±0.24

5242

 

4

23.77

21.6±8.51

0.74 ±0.33

0.67±0.26

14493

 

5

65.72

20.3±7.82

0.70±0.30

0.62±0.24

1911

 

6

8.67

19.7±7.76

0.67±0.29

0.57±0.23

9

 

7

0.04

27±6.43

0.81±0.30

0.71±0.17

 

G6

302

88-164

4

1.22

22.3±8.30

0.71±0.31

0.65±0.24

3704

 

5

14.99

21.5±8.53

0.74±0.32

0.65±0.26

10745

 

6

43.49

20±7.97

0.7±0.31

0.62±0.24

6376

 

7

25.81

19.4±7.87

0.67±0.30

0.60±0.24

2545

 

8

10.30

18.9±7.40

0.64±0.28

0.57±0.22

781

 

9

3.16

18±6.91

0.61±0.24

0.56±0.20

236

 

10

0.96

16.2±6.26

0.57±0.25

0.51±0.19

19

 

11

0.08

15±4.71

0.49±0.19

0.46±0.15

± (Standard deviation), * P= percentage following the parity, G=groups.


Results and Discussion

Goodness of fit for WIL model

In a preliminary analysis, parameter K was estimated at 0.65 for MY and 0.10 for FY and PY. The value found for milk is agrees with that used by Silvestre et al (2006) and close to that (k=061) found by Olori et al (1999), whereas Wilmink (1987) proposed a value of 0.50 for Dutch- Friesians cattle. The WIL was a good fit for average lactation curves across cows (Table 2) in different grouped effects. The coefficient of determination from non-linear regression is 98 % for MY, 96 % for FY and ranged from 97 to 98 % for PY. The WIL also predicted better the yields; RES ranged from 1.70-2 Kg, 0.08-0.10 and 0.05-0.07 Kg, respectively for MY, FY and PY. Standard deviations of residual were uniformly smaller for protein than fat yield, similar results were noted by Schutz et al (1990). Thus, PY predicted yield with lower residual than FY in all environmental factors.

 

Table2: Goodness of fit for Wilmink model according to various environmental factors.

 

Goodness of fit

 

MY

FY

PY

Factors

R2

RES

R2

RES

R2

RES

Calving age-parity group

G1

0.98±0.01

1.70±1.62

0.96±0.03

0.09±0.09

0.97±0.01

0.05±0.05

G2

0.98±0.01

1.80±1.70

0.96±0.03±

0.10±0.10

0.98±0.01

0.06±0.06

G3

0.98±0.01

1.90±1.80

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

G4

0.98±0.01

1.90±1.80

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

G5

0.98±0.01

1.90±1.80

0.96±0.03

0.10±0.10

0.97±0.01

0.06±0.06

G6

0.98±0.01

2±1.80

0.96±0.03

0.10±0.10)

0.98±0.02

0.06±0.06

Herd-calving year (HCY) group

HCY1

0.98±0.01

1.70±1.60

0.96±0.03

0.08±0.09

0.98±0.01

0.05±0.06

HCY2

0.98±0.01

1.80±1.70

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

HCY3

0.98±0.01

1.85±1.80

0.96±0.03

0.10±0.10

0.97±0.01

0.07±0.06

Calving season

Winter

0.98±0.01

1.80±1.70

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

Spring

0.98±0.01

1.85±1.80

0.96±0.03

0.10±0.10

0.97±0.01

0.06±0.06

Summer

0.98±0.01

1.80±1.70

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

Fall

0.98±0.01

1.80±1.70

0.96±0.03

0.10±0.10

0.98±0.01

0.06±0.06

± (Standard deviation)

 
Factors affecting the shape of lactation curve for milk, fat and protein yields

The ANOVA mean squares of model effects are in Table 3. The coefficient of determination (R2) ranged from 0.05 for b to 0.25 for DIMP (days in milk at peak) for milk yield, these results are similar to previous studies (Tekerli et al 2000, Atashi et al 2009). For fat and protein yields, the R2, ranged respectively from 0.01 (persistency) to 0.57 (DIMP) and from 0.01 (b) to 0.64 (DIMP). All grouped effects were highly significant (P<0.01), but only the herd-calving year was not significant (P>0.05) for fat persistency. The high significance of calving age parity is explained mainly by the effect of milk secretory tissue that requires more time for peak activity in primiparous cows than in multiparous cows Rao and Sundaresan (1979). The high level of significance of the herd-calving year effect can be explained by the difference in management between herds and also the diverse feeding level which changes according to the annual climate frequently has observed under the Tunisian conditions. The effect of calving season on lactation curve traits is explained by temperature and rain variations that affect fodder production, especially in summer under the North African climate when feeding resources are limited and heat stress effect is important. The highest significance of all grouped environmental effects on lactation curve parameters a, b and c for milk traits allows having different shapes of the curves. In the North of Africa conditions similar results were indicated in the literature but were limited only for milk yield. Boujenane and Hilal (2012) studied the genetic and non genetic effects on lactation curve traits determined by the incomplete gamma function for Holstein-Friesian cows in Morocco and reported that lactation curve traits (a, b, c, DIM, peak, persistency and Y305) were affected by herd, parity, age at calving, season of calving and year of calving. Rekik et al (2003) fitted lactation curves of Holstein-Friesian cows in Tunisia in four types of herds and reported also some similar finding for milk yield.

 

Table 3: Mean squares of variables from ANOVA of individual lactation curves for milk, fat and protein yields

 

Trait

 

Variable

DF

Lactation curve parameters

a (×104)

b (×106)

c (×10-4)

Peak

(×104)

DIMP

(×104)

Per

(×103)

Y305

(×108)

 

Calving age-parity

5

26**

0.08**

47001**

12.3**

36**

102.3**

30.6**

 

Herd-calving year

2

12.5**

0.87**

1900**

6.92**

4.19**

6.7**

55**

MY

Calving season

3

8.40**

1.81**

21200**

1.94**

14.6**

83.8**

3.21**

 

DIM at first test day

1

1.57**

29.3**

6600**

4.36**

1199**

24.5**

37.6**

 

R2

_

0.10

0.05

1500

0.11

0.25

0.10

0.13

 

Calving age-parity

5

0.000003**

0.0004**

48.3**

0.016**

4.74**

140**

0.04**

 

Herd-calving year

2

0.000001**

0.005**

2.7**

0.0085**

0.44**

70.9

0.06**

FY

Calving season

3

0.000001**

0.03**

45.6**

0.0077**

8.33**

533**

0.01**

 

DIM at first test day

1

0.0000002**

0.01**

2.3**

0.0053**

979.8**

224**

0.01**

 

R2

 

0.10

0.02

1.2

0.0015

0.57

0.01

0.10

 

Calving age-parity

5

0.023**

0.0002**

34.6**

0.012**

7.63**

102**

0.039**

 

Herd-calving year

2

0.008**

0.003**

0.64**

0.0057**

0.55**

9.22**

0.042**

PY

Calving season

3

0.008**

0.009**

22.9**

0.0035**

6.66**

98.5**

0.0026**

 

DIM at first test day

1

0.0002**

0.003**

1.46**

0.0025**

802**

11.9**

0.014**

 

R2

 

0.19

0.01

0.21

0.0027

0.64

0.01

0.11

*P<0.05, **P<0.01, DIMP=DIM at peak , Per=persistency , Y305 =Total lactation yield through 305 DIM

The least squares means for the lactation curve traits are presented in Table 4 for MY and in Table 5 for FY and PY. In this investigation, the parameter a (represents a yield at the beginning of lactation), peak and 305-days yields were the lowest in the first calving age-parity group (G1) than other groups, but cows are more persistent for all yields traits. The highest persistency may be caused by the relatively lower peak yield resulting in a flatter lactation curve. The 305-day yields (Tables 4 and 5) show that the first group cows (G1) produce the lowest yields than the other groups, for example a difference of 635 Kg MY and 27 Kg FY and PY was noted between G1 and G3. Indeed, 97 % of the cows classed in G1 (Table 1) are in the first lactation (primiparous) and their calving age ranged from 16-36 months. Stanton et al (1992) observed the same effect in dairy cows and suggested that this pattern could be due to the fact that the body and mammary gland of young animals are still developing during the first part of lactation. Total yields increase with the calving age-parity groups and the yields peaked in the third group (G3) whose animals are mainly in the third lactation (77 %) and their calving age ranged from 50 to 62 months. Then, yields decline from the fourth lactation (G4), this decline in yields according to the advance of calving age is explained by the physiological activities of all body systems start to decrease and the secretary tissues of mammary gland is partially degenerated leading to gradual decrease in milk production with advancing age, in agreement with Keowen et al (1986).

 

Table 4: Least squares means of main grouped environmental effects included in the analysis of individual lactation curves

 

Variable

 

N

Curves parameters and predicted milk yield.

a

b

c (×10-2)

Peak

DIMP

per

Y305

calving age-parity

G1

80809

23.4a

-1.91a

-3.14a

22.6a

52.8a

94.3a

5537a

G2

59448

27b

-0.66b

-4.47b

25.7b

47b

92.4b

6001b

G3

44216

29c

-2.79c

-5.37ce

27.4c

46.6c

91.6c

6172c

G4

28452

28.6d

-0.93d

-5.34c

26.8d

45.7d

90.6d

6076d

G5

22608

28.6d

-2.64c

-5.4de

27.2e

45.6d

90.7df

6024b

G6

24708

28e

-2.66c

-5.41d

26.4f

45.8d

91ef

5844e

Herd-calving year

HCY1

55484

25.8a

-4.80a

-4.55a

23.5a

43.5a

92.4a

5623a

HCY2

82920

27.7b

-6.14b

-4.82b

25.5b

45b

92.5a

6040b

HCY3

121837

28.4c

-7.23c

-4.90c

26c

45.25b

92.6a

6164c

Calving season

Winter

67773

28.6a

-8.09a

-5.45a

26a

44a

91.4a

5990a

Spring

54319

27.3b

-4.01b

-4.80b

25.3b

43.8ac

92.4b

5908b

Summer

74697

26c

-4.88c

-4.16c

24.2c

45bd

93.4c

5842c

Fall

63452

27.3b

-7.24d

-4.61d

25d

45.3b

92.8d

5970b

a,b,c,d,e,f Means of factors levels with different superscripts for each lactation curve parameter are significantly different (P<0.05)

For the herd-calving year groups, cows calved during 1993, 1995, 2000 and 2001 recorded more production of MY, FY and PY during their lactations. For calving season, the lowest level of production occurred for cows were calved in summer, but these cows are more persistent. While, the highest level happened for cows were calved in winter and fall for MY. Similar reports were obtained by (Tekerli et al 2000 and Atashi et al 2009). And there are a little variation between FY and PY in spring, fall and winter. However, summer months depress total milk, fat and protein yields (Keown et al 1986).

For DIMP, there is a tendency to decrease as calving age-parity groups increase with a little difference starting from the fourth group for MY and the third group for FY and PY. Peak yield occurs between 6th at the 8th week of lactation in all groups and traits. The great variation (3 to 6 days) was observed between the first and other groups according to the calving age-parity and between summer and the other calving season. Moreover, there is little variation in DIMP by herd-calving year effect. This was in accordance with the finding of Keown et al (1986).

For all production traits and all environmental effects, fat trend to be more persistent than milk and little difference was observed between fat and protein. Keown et al (1986) reported similar persistencies of fat and protein yields, protein tending to be more persistent than fat. Schutz et al (1990) indicate that protein was more persistent than fat yield for Holstein cows. Cows in high herd-calving year production groups have the highest peak production for all milk traits and more persistent for milk yield, but little difference in persistency with the three groups for fat and protein yields.

 

Table 5: Least squares means of main grouped environmental effects included in the analysis of individual lactation curves parameters and for fat and protein yields

 

Variable

N

Lactation curve traits

a

b

c(×10-5)

Peak

DIMP

Per

Y305

Fat yield

Age-parity

G1

80809

0.72a

0.27a

-68.3a

0.7a

50.6a

96.6a

187a

G2

59448

0.85b

0.17b

-115b

0.82b

48.4b

93.3b

206b

G3

44216

0.93c

0.07c

-145c

0.9c

47.6c

95.1ac

214c

G4

28452

0.92d

0.32a

-146ce

0.87d

47.6c

95.1a

210d

G5

22608

0.92d

-0.0007d

-151d

0.88d

47.4ce

92.6bc

206b

G6

24708

0.89e

0.29a

-149de

0.86e

47.2de

92.7bc

203e

Herd-calving year

HCY1

55484

0.8a

0.43a

-120a

0.77a

48a

95.8a

190a

HCY2

82920

0.91b

0.23b

-134b

0.87b

47.9a

94.9ac

213b

HCY3

121837

0.9b

-0.01c

-133b

0.87c

48.5b

93.6bc

210c

Calving season

Winter

67773

0.9a

0.6a

-150a

0.86a

47a

92.4a

204a

Spring

54319

0.8b

1.02b

-99b

0.77b

49.5b

95.6b

205a

Summer

74697

0.85c

-0.55c

-108c

0.82c

49.5b

98.8c

202b

Fall

63452

0.93d

-0.3d

-159d

0.89d

46.5c

92.3a

206c

Protein yield

Age-parity

G1

80809

0.66a

0.02a

-67a

0.65a

49a

96.1a

168a

G2

59448

0.81b

0.10be

-117b

0.78b

46b

94.1b

191b

G3

44216

0.86c

0.08c

-141c

0.84c

44.6c

92.1c

195c

G4

28452

0.84d

0.04ae

-140c

0.81d

44.7c

92.1c

191b

G5

22608

0.82d

0.21d

-136d

0.79e

45c

92.1c

185d

G6

24708

0.81b

0.6ae

-140c

0.78b

44.8c

92c

181e

Herd-calving year

HCY1

55484

0.76a

0.21a

-119a

0.73a

45.2a

92.5a

175a

HCY2

82920

0.8b

0.03b

-124b

0.78b

45.9b

93.4b

186b

HCY3

121837

0.84c

-0.82c

-127c

0.82c

46.1c

93.3b

194c

Calving season

Winter

67773

0.85a

-0.02a

-149a

0.81a

43.1a

91.5a

186ad

Spring

54319

0.77b

0.69b

-110b

0.75b

45.5b

93.5b

186ad

Summer

74697

0.76c

-0.3c

-101c

0.74c

47.5c

94.9c

181b

Fall

63452

0.83d

-0.4d

-134d

0.80d

45.9c

92.6d

186cd

a,b,c,d,e Means of variable levels with different superscripts for each lactation curve trait are significantly different (P<0.05)

Lactation curves fitted for milk, fat and protein yields according to environmental factors are in Figures 1 to 6. Lactation curves for all yield traits and for all environmental factors were depressed at approximately 230 days in milk. This is explained by an increase in the influence of pregnancy depression at 7 to 8 months in gestation. The shape of the lactation curve differed between fat and protein yields in each environmental group. Figure 1 shows that the flattest curve was detected in the first calving age-parity group (G1) that started at a low level (18.4 kg), has a very slow increase (0.18 Kg/day) to the peak, which was not very high (23 kg) and was reached late in lactation (54 DIM), then the milk yield decreased slowly until the end of lactation. These trends in the shape result that cows’ group was more persistent. The most typical lactation curves were observed in the G3 and G4 cows’ group, with the majority of the animals are in third lactation (77%, for G3) and in the fourth lactation (70%, for G4). These cows started the lactation with a high level (23-24 kg) with a moderate rate of increase up to the peak production that was reached between 45-46 DIM at a high level (28 kg), they followed by a moderate rate of decrease of daily production. The shape of the lactation curves for cows in the fifth group were different to G3 and G4 only at the initial phase of lactation but from the peak the same trends were observed. This difference results in the fact that these animals produce 123 kg of milk less than those which have the maximum of Y305 (G3).

Figure 1: lactation curves for different calving age-parity effects for MY

The lactation curves for FY and PY according to the calving age-parity effect are presented In Figure 2, cows in the first group have a flatter curve for MY (Figure 1) and PY (Figure 2-b) than for FY (Figure 2-a). Keown et al (1986) found differences in lactation curves for fat and protein yields. Primiparous cows (G1) reach their peak faster for FY than for PY but present almost a similar trend in the decrease of yields. The highest curves for FY and PY (Figures 2-a, and 2-b) were obtained with cows which calving age are between 50 to 74 months classed in G3 and G4 groups. Although, those of G3 produce more than G4 in the second part of lactation and the curves of G3 and G4 were closer for FY than for PY. The highest level of production at the beginning of lactation was recorded in the third and the fourth parity of animals (G3 and G4) for all milk traits. While, the highest level of production at the end of lactation were obtained with cows in the first and in second lactation (G1 and G3).

Figure 2: lactation curves for different calving age-parity effects for FY (a) and PY (b)

The patterns of lactation curves in different herds classified by calving year are presented in Figures 3 and 4. The shape of the lactation curve for MY, was different between the three herd groups. Cows in HCY3 and HCY2 had the highest rate of increase to peak, but those in HCY3 produced the highest peak (Figure 3). Thus, in HCY1 the daily milk yield increased and decreased more rapidly with the lowest peak yield. However, cows in HCY3 presented a more typical lactation curve than HCY 1 and 2 so, this group produced the highest level of total milk yield (for example 6124 kg for HCY 3 and 5554 Kg for HCY 1).

Figure 3: lactation curves for Herd - calving year effects for MY

The cows of the second and the third Herd-calving year groups present exactly the same shape of the lactation curve for FY, whereas they are different for PY. In all groups, FY increases rapidly after partition than PY, but with higher level for FY at DIMP. Curves for PY were very flattered than FY and closer to MY in general shape aspect.

Figure 4: lactation curves for Herd - calving year effects for FY (a) and PY (b)

The lactation curves for the different calving seasons are presented in Figure 5 and 6. Results show that the calving season affected the shape of fat and protein curves more than milk. The biggest fluctuation of MY between seasons was observed around the peak and until about 200 DIM. Whereas, season effect influenced FY and PY curves in the majority parts of lactation. For MY cows calved in fall, winter and summer started their lactation with the same level of production (Figure5) and this aspect was probably attributed to the body condition of the animal at calving and feeding level of the cow before and around partition than of the seasonality effect. While, those calving in summer increase fast to DIMP and recorded a lower peak yield. The higher peak yield with slower rate of decline was obtained from cows calving in the fall and winter. And curves for fall season were highly persistent until the end of lactation.

Figure 5: lactation curves for calving season effect on MY

According to Figures 6-a, and 6-b fall and winter calving seasons were more favourable for fat and protein production. Indeed, initial yield and peak for fat and protein yields were greater for fall and winter. Cows freshening in the summer and spring have the lowest peak production for FY and PY but as lactation length increases; they augment their production and become more persistent. On the opposite, fat and protein production in winter and fall were higher and more persistent until about 200 DIM but after that production decreased fast and become inferior to other seasons. Indeed, cows calving in the spring and summer finish their lactation during cooler months, whereas those calving in winter and fall complete their lactations during warmer months and decline of more sharply (Keown et al 1986). This aspect explains the change of production levels between lactation stage and season effects.

Figure 6: lactation curves for calving season effect for FY (a) and PY (b)

Phenotypic correlations between the lactation curve traits are shown in Table 6. The highest correlation found was between a and peak yield (r=0.98), and the lowest correlation was between a and c for all milk production traits. The negative correlation between a and b and between b and peak suggest that cow with a higher initial level of production reached their peak faster and thereafter decline at a slower rate than those with a lower initial level of production. This aspect is observed with the third and fourth calving age-parity groups, in winter and fall for fat and protein yields and for second and third herd-calving year groups for all traits. The positive correlations between b and c indicate that cows with a higher rate of increase until the peak and also have a quicker decline after peak. The initial yield for MY, FY and PY (parameter a) was positively higher correlated with Y305 and peak, but negatively associated with DIMP suggest that cows with bigger initial yields tend to have higher and peak yields and recorded the biggest total yields (Y305). This suggestion is very clear with the result obtained by the third calving age-parity group, in winter and HCY3 group (Table 4 and 5). The correlation between c and persistency is higher and positive for MY and PY than for FY indicate that cows with a lower rate of decline have higher persistency. Finally, the results show no relation of persistency (P>0.05) with a total fat yield in 305 DIM. The correlation between peak yield and lactation yield (r=0.84, 0.57 and 0.65 respectively for MY, FY and PY) was higher than that observed between persistency and lactation yield .Thus peak yield seems to be more important in determining the total lactation yield than persistency.

 

Table 6: Phenotypic correlation between individual lactation curves traits for milk, fat and protein yields

Variable

Lactation curve traits

Milk yield

 

B

c

Y305

Peak

DIMP

Per

a

-0.26**

-0.78**

0.74**

0.98**

-0.21**

-0.01**

b

 

0.27**

0.22**

-0.23**

-0.10**

0.02**

c

 

 

-0.25**

-0.70**

0.36**

0.18**

Y305

 

 

 

0.84**

-0.05**

0.02**

Peak

 

 

 

 

-0.25**

-0.19**

DIMP

 

 

 

 

 

0.27**

Fat yield

a

-0.33**

-0.80**

0.50**

0.98**

-0.21**

-0.03**

b

 

0.32**

0.44**

-0.38**

0.006*

0.01**

c

 

 

-0.04**

-0.75**

0.28**

0.04**

Y305

 

 

 

0.57**

-0.03**

0.003

Peak

 

 

 

 

-0.22**

-0.02**

DIMP

 

 

 

 

 

0.02**

Protein yield

a

-0.29**

-0.78**

0.57**

0.98**

-0.19**

-0.01**

b

 

0.27**

0.42**

-0.32**

-0.05**

0.03**

c

 

 

-0.09**

-0.71**

0.31**

0.15**

Y305

 

 

 

0.65**

-0.03**

0.02**

Peak

 

 

 

 

-0.20**

-0.08**

DIMP

 

 

 

 

 

0.06**

* P<0.05, ** P<0.01


Conclusion


Acknowledgements

The National Centre of Genetic Improvement of Tunisia (CNAG: Sidi Thabet, Tunis) is greatly acknowledged for providing the data used in this study.


References

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Received 31 December 2012; Accepted 28 April 2013; Published 1 May 2013

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