On modern farms, the individual approach is to a great extent replaced by automatic data collection, radiotransmision of information and computer evaluation. Much of this received data can be used not only for production, reproduction and economy management but also as signalisation of possible health problems and thus for their early detection and treatment. It is important to state that this indirect individual care with the help of technical equipment for monitoring and evaluation does not rid the farmer of the obligation to secure at least one physical check of all animals daily (Doležal et al., 2004).

In the Czech Republic there are according to Kvapilík et al. (2011) 113 004 closed lactations for the Czech Fleckvieh dams and 157 634 closed lactations for Holstein dams in the production control procedure, where the milk productivity for these breeds is on average 6 472 kg and 8721 kg respectively. The Czech Fleckvieh cattle belongs to the world population of Fleckvieh Cattle with common phylogenetic origin, currently spread on all continents. The requirements for this breed are of dual performance with high milk production, medium to large body stature, excellent muscling and harmonic appearance (Bouška, 2006).

## **2. Results of the use of milking robots**

The aim of this study was to evaluate the productivity of breeding cows kept on farms that use the AMS according to breed, number of robots used and the type of company ownership. Part of this study also considered the behaviour of the cows during the milking procedure, where thorough analyses of lactation curves for different productivity levels, the content of milk components during lactation process, correlation between milk productivity and fertility and between milking frequency and reproduction results were conducted.

Seven agricultural companies mostly from the Southern Bohemia region that use the progressive technology of automated milking system (AMS - Dutch made model Astronaut A3 from producer Lely) were included in this study. The chosen companies were divided according to the number of milking robots used into following categories: small (one robot – company nr. 1, 2 and 3), medium (two and three robots – farm nr. 4 and 5) and large (seven and eight robots – company nr. 6 and 7). According to the ownership the companies were designated as private (1 to 3) and cooperative (4 through 7). In companies 2, 3 and 7 the breed of dairy cows was the Czech Fleckvieh cattle (C), while the Holstein breed was kept in farms 1, 6 and 7 (H). Both breeds were kept in companies 4 and 5. These parameters were followed in the breeding cows: reproduction parameters (insemination interval, service period and meantime), the average amount of milk per cow and day (in kg), the average number of milking times per cow and day, the average number of refused milking times per cow and day, numbers of problematic cows (refusing being milked by the robot).

Basic data background was acquired from each company from program T4C (Time for cows), the steering unit of AMS that serves as the communication device and enables the back check of the milking data. The data set was consequently analysed by statistical programs Statistica.cz, Statsoft Co. and Statsoft CR Ltd. 200. Information for the analysis of problematic dairy cows that needed being lead to the robot was acquired directly from the keepers. Data evaluated concerned the period between 1.1.2009 and 30.11.2009 (334 days).

#### **2.1. Results of herd reproduction and productivity**

Table 1 shows an overview of breeds kept in individual companies including the average number of dairy cows, average number of cows per milking robot and chosen reproduction parameters. The Holstein breed was kept in companies 1 and 6. In company nr. 7 both breeds C and H were kept separately, while in companies 4 and 5 herds consisted of both breeds mixed together which prevented the evaluating of the followed parameters separately. The lowest number of cows was 51 in company 2, the highest 377 in company 6. The number of robots varied from one in companies 1 and 3 to eight in company 7. The insemination interval was shown to vary greatly between 59 days (company 4) to 86 days (company 7), the service period varied from 89 (company 4) to 141 days (company 1). The length of the meantime was between 395 days (company 3 and 6) and 415 days (company 7). As a comparison Kvapilík et al. (2011) reported republic averages for dairy cows at productivity check for year 2009 to be 83.6 days for the insemination interval, 122.9 days as the length of the service period and 411 days as the length of the meantime.

H - Holstein; C - Czech Fleckvieh

318 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

**2. Results of the use of milking robots**

production control procedure, where the milk productivity for these breeds is on average 6 472 kg and 8721 kg respectively. The Czech Fleckvieh cattle belongs to the world population of Fleckvieh Cattle with common phylogenetic origin, currently spread on all continents. The requirements for this breed are of dual performance with high milk production, medium to large body stature, excellent muscling and harmonic appearance (Bouška, 2006).

The aim of this study was to evaluate the productivity of breeding cows kept on farms that use the AMS according to breed, number of robots used and the type of company ownership. Part of this study also considered the behaviour of the cows during the milking procedure, where thorough analyses of lactation curves for different productivity levels, the content of milk components during lactation process, correlation between milk productivity and fertility and between milking frequency and reproduction results were conducted.

Seven agricultural companies mostly from the Southern Bohemia region that use the progressive technology of automated milking system (AMS - Dutch made model Astronaut A3 from producer Lely) were included in this study. The chosen companies were divided according to the number of milking robots used into following categories: small (one robot – company nr. 1, 2 and 3), medium (two and three robots – farm nr. 4 and 5) and large (seven and eight robots – company nr. 6 and 7). According to the ownership the companies were designated as private (1 to 3) and cooperative (4 through 7). In companies 2, 3 and 7 the breed of dairy cows was the Czech Fleckvieh cattle (C), while the Holstein breed was kept in farms 1, 6 and 7 (H). Both breeds were kept in companies 4 and 5. These parameters were followed in the breeding cows: reproduction parameters (insemination interval, service period and meantime), the average amount of milk per cow and day (in kg), the average number of milking times per cow and day, the average number of refused milking times per

cow and day, numbers of problematic cows (refusing being milked by the robot).

**2.1. Results of herd reproduction and productivity**

Basic data background was acquired from each company from program T4C (Time for cows), the steering unit of AMS that serves as the communication device and enables the back check of the milking data. The data set was consequently analysed by statistical programs Statistica.cz, Statsoft Co. and Statsoft CR Ltd. 200. Information for the analysis of problematic dairy cows that needed being lead to the robot was acquired directly from the keepers. Data evaluated concerned the period between 1.1.2009 and 30.11.2009 (334 days).

Table 1 shows an overview of breeds kept in individual companies including the average number of dairy cows, average number of cows per milking robot and chosen reproduction parameters. The Holstein breed was kept in companies 1 and 6. In company nr. 7 both breeds C and H were kept separately, while in companies 4 and 5 herds consisted of both breeds mixed together which prevented the evaluating of the followed parameters separately. The lowest number of cows was 51 in company 2, the highest 377 in company 6. **Table 1.** Chosen reproduction factors in followed herds

The average daily production of milk achieved in individual farms is shown in Table 2. The highest productivity (40.43 kg) was measured for company 1. The second largest (30.16 kg) was found in company 6. On the other hand was the lowest productivity (21.04 kg of milk) measured for company 3 with the highest number of animals per robot (73 cows). Differences between the companies were highly statistically significant (P≤0.001). The obtained results father emphasise the premises of Doležal (2000) that higher milk production induces the higher effect of milking times. Compared to the average daily milk production for year 2009 in the Czech Republic (18.82 kg of milk - Kvapilík et al. 2011), the companies followed were able to show higher values.

Dividing the farms according to size corresponds well with the division according to ownership (Table 3). The highest daily productivity was achieved in small privately owned companies with one robot (28.79 kg of milk) as compared to medium and large cooperate companies. The data were despite large variation (sx =8.54) and differences 3.57 resp. 1.45 kg of milk found statistically significant (P≤0.001). These results correspond to the conclusions made by Rotz et al. (2003) who recommend installation of milking robots on small family

farms. According to Rodenburg (2002) is automated milking great advantage for dairy cattle herds with 60 to 200 heads, as the milking parlour technology is too expensive and automated milking represents more economical solution.

**Table 2.** Average milk production in individual companies (in kg of milk)

Table 4 shows the average productability with regard to the breeding type of the dairy cows. Highest values were obtained for the H breed, reaching 29.97 kg of milk. The difference compared to the C cattle was l 7.17 kg (P≤0.001). Herds with both breeds have reached an average value of both (25.22 kg of milk). The number of cows per robot was identical for H breed and H and C mixed breed (53 heads), but lower for the C breed (47 heads).

**Table 4.** Average milk production according to breed (kg of milk)

#### **2.2. Milking frequency using AMS**

320 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

automated milking represents more economical solution.

Number of cows per 1 robot

Company number

Company size

Breed

Czech Fleckvieh Cattle

farms. According to Rodenburg (2002) is automated milking great advantage for dairy cattle herds with 60 to 200 heads, as the milking parlour technology is too expensive and

1. 59 334 40.43 1.65 2745.0\*\*

Small (1) private 61 1002 28.79 8.54 206.2\*\*

Table 4 shows the average productability with regard to the breeding type of the dairy cows. Highest values were obtained for the H breed, reaching 29.97 kg of milk. The difference compared to the C cattle was l 7.17 kg (P≤0.001). Herds with both breeds have reached an average value of both (25.22 kg of milk). The number of cows per robot was identical for H

Holstein Cattle (1) 53 4008 29.97 4.33 2903.0\*\*

(2) 47 2004 22.80 2.26

Both breeds (3) 53 1670 25.22 2.99

2. 51 334 24.83 1.71 3. 73 334 21.04 1.40 4. 49 668 22.92 3.00 5. 55 1002 26.75 1.74 6. 54 2338 30.16 2.97 7. 45 2672 24.87 2.97

**Table 2.** Average milk production in individual companies (in kg of milk)

Number of cows per 1 robot

Medium (2) cooperative 53 1670 25.22 2.99 Large (3) cooperative 49 5010 27.34 4.00

**Table 3.** Average milk production according to size and ownership of the company (kg of milk)

breed and H and C mixed breed (53 heads), but lower for the C breed (47 heads).

Number of cows per 1 robot

**Table 4.** Average milk production according to breed (kg of milk)

Type of ownership n x sx F test

n x sx F test

n x six F test

1:2,3,4,5,6,7\*\*\* 2:3,4,5,6,7\*\*\* 3:4,5,6,7\*\*\* 4:5,6,7\*\*\* 5:6,7\*\*\* 6:7\*\*\*

> 1:2\*\*\* 1:3\*\*\* 2:3\*\*\*

1:2\*\*\* 1:3\*\*\* 2:3\*\*\*

The average number of milking times per one dairy cow per day is an important parameter for the automated milking system. In Table 5 significant differences between the companies can be seen (P≤0.001), where company 1 achieved the highest frequency of 2.67. The productability of this company is thus markedly higher which corresponds to literature (Doležal, 2000, 2002, Stelwagen, 2001) regarding the positive influence of multiple milking times for high performance dairy cows. The lowest number of milking times, namely only 1.97, was found for company nr. 3, where the number of cows per robot was the highest, 73 heads. Lowering this number by 14 resulted in an increased number of milking times, up to 2.67. It is important to state here that both companies hold different breeds of cattle.

**Table 5.** Average number of milking per one dairy cow per day in individual companies

Significant differences (P≤0.001) in the number of milking times per head and day were also found for the companies when devided according to their size and ownership (Table 6) even though the differences were not large. The highest number of milking times was exhibited by medium and large companies (2.47 resp. 2.43), while the small companies put together obtained an average value of 2.34. Similar conclusions can be drawn when the ownership situation is considered simultaniously.

**Table 6.** Average number of milking times per one dairy cow per day according to size and ownership of the company

Significant difference in milking times was also found when considering the different breeds of cattle (Table 7). The lower average of 2.32 milking times per head and day was found for the dual productivity cattle of breed C, while the breeding cows of the dairy cattle H were milked on average 2.45 times (P≤0.001). The correlation to the productivity average of the individual breeds is therefore clear.

**Table 7.** Average number of milking times per cow and day according to breed

When a cow enters the robot before the right milking time has come, it is refused and released, which is defined as a refusal. Ideally the number of such refusals should be around 1.5 per day, which would mean one refusal for two concluded milking times (Šťastný, 2010). The differences found for the number of refusals per head and day were very significant (P≤0.001) for the different companies (Table 8). Clearly the highest number of refusals (3.66) was found for company 4, the second highest (2.63) for number 7. Both these companies have the lowest number of cows per milking robot (49 resp. 45). Extremely low values were then calculated for farms 2 (0.72) and 3 (0.88).

**Table 8.** Average number of rejections per dairy cow per day in individual companies

The average number of refusals according to size and company ownership is shown in Table 9. Small privately owned companies with one robot showed low refusal count at about 1.11, as compared to medium and large cooperative companies with values around 2.18 resp. 2.30. Small and privately owned farms seem to place more emphasis on working relief supplied by the AMS than on milk production results. The differences are statistically highly significant for differences between companies 1 and 2 and 1 and 3 (P≤0.001). Milking robots are used more frequently in small private companies as there usually are more cows per robot (61) as compared to larger cooperatively owned companies with several AMS (53 resp. 49 heads/robot).

Table 10 shows that breeding cows of the Czech Fleckvieh cattle were refused by the robot 2.55 times per day on average, while the breeding cows of the Holstein breed only 1.88 times per day. These differences are highly statistically significant (P≤0.001). In herds with both breeds kept together the average number of refusals was calculated to 2.18.

**Table 10.** Average number of rejections per one dairy cow per day according to breed

### **2.3. Problematic dairy cows**

322 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

Number of cows per 1 robot

Czech Fleckvieh Cattle (2) 47 2004 2.32 0.26

**Table 7.** Average number of milking times per cow and day according to breed

Both breeds (3) 53 1670 2.47 0.20

individual breeds is therefore clear.

then calculated for farms 2 (0.72) and 3 (0.88).

Average number of cows per 1 robot

Company number

Breed

Significant difference in milking times was also found when considering the different breeds of cattle (Table 7). The lower average of 2.32 milking times per head and day was found for the dual productivity cattle of breed C, while the breeding cows of the dairy cattle H were milked on average 2.45 times (P≤0.001). The correlation to the productivity average of the

Holstein Cattle (1) 53 4008 2.45 0.23 245.2\*\*

When a cow enters the robot before the right milking time has come, it is refused and released, which is defined as a refusal. Ideally the number of such refusals should be around 1.5 per day, which would mean one refusal for two concluded milking times (Šťastný, 2010). The differences found for the number of refusals per head and day were very significant (P≤0.001) for the different companies (Table 8). Clearly the highest number of refusals (3.66) was found for company 4, the second highest (2.63) for number 7. Both these companies have the lowest number of cows per milking robot (49 resp. 45). Extremely low values were

1. 59 334 1.74 0.51 524.1\*\*

The average number of refusals according to size and company ownership is shown in Table 9. Small privately owned companies with one robot showed low refusal count at about 1.11, as compared to medium and large cooperative companies with values around 2.18 resp. 2.30. Small and privately owned farms seem to place more emphasis on working relief

2. 51 334 0.72 0.27 3. 73 334 0.88 0.28 4. 49 668 3.66 2.07 5. 55 1002 1.20 0.32 6. 54 2338 1.93 1.15 7. 45 2672 2.63 1.31

**Table 8.** Average number of rejections per dairy cow per day in individual companies

n x sx F test

n x sx F test

1:2\*\*\* 1:3\*\* 2:3\*\*\*

1:2,3,4,5,7\*\*\* 2:3,4,5,6,7\*\*\* 3:4,5,6,7\*\*\* 4:5,6,7\*\*\* 5:6,7\*\*\* 6:7\*\*\* 1:6\*\*

Figure 1 to 3 show percentage counts of problematic cows needed to be lead to the milking robot for a certain amount of time. The lowest number was found for company number 3 (8.2%), the highest for numbers 7 (25.2%) and 6 (15.1%). For the rest of the companies the values fluctuated around 10 % (Figure 1). When focussing on the companies according to size and ownership (Figure 2) the percentage of problematic animals obtained tends to be quite high (20.1%) in large companies as compared to medium large (even cooperative) and small farms where the numbers were kept just above 9%. Differences found between the breeds (Figure 3) point to least problematic individuals belonging to the Fleckvieh cattle (8.8%) as compared to the Holstein breed (14.4%). Veselovský (2005) explains that it is characteristic for classic motivation that the originally neutral stimulus (robot visit) eventually activates a certain behavioural pattern (milking) when induced by a reward here in the form of extra feed. The main target is to create association or connection between a certain activity and the reward. Šťastný (2010) says that the normal state is that 5 to 10% of the cows need being led to the robot box. According to Tančin and Tančinová (2008) thorough attention must here be turned to the quality of the person – keeper, as animals strongly feel the presence of an aggressive or unkind person and his presence tends to induce changes in behaviour, and consequently due to increased stress lead to lower productivity by as much as 10%. Weis et al. (2005) found based on own studies excellent capability of the cows to learn as after three days the majority were capable of entering the robot box without physical assistance from the keepers.

**Figure 1.** Number of leaded dairy cows to the milking robot in individual companies (in %**)**

**Figure 2.** Number of leaded dairy cows to the milking robot according to company size (in %)

```
H – Holstein Cattle, C – Czech Fleckvieh Cattle
```
**Figure 1.** Number of leaded dairy cows to the milking robot in individual companies (in %**)**

**9,3 9,7**

**10,1 9,8 8,2 9,2 10,3**

**1234567 farm**

**15,1**

**20,1**

**25,3**

**Figure 2.** Number of leaded dairy cows to the milking robot according to company size (in %)

**small middle large farm**

robot box without physical assistance from the keepers.

**%**

**%**

in the form of extra feed. The main target is to create association or connection between a certain activity and the reward. Šťastný (2010) says that the normal state is that 5 to 10% of the cows need being led to the robot box. According to Tančin and Tančinová (2008) thorough attention must here be turned to the quality of the person – keeper, as animals strongly feel the presence of an aggressive or unkind person and his presence tends to induce changes in behaviour, and consequently due to increased stress lead to lower productivity by as much as 10%. Weis et al. (2005) found based on own studies excellent capability of the cows to learn as after three days the majority were capable of entering the

**Figure 3.** Number of leaded dairy cows to the milking robot according to breed (in %)

## **3. Analysis of milk productivity**

An agricultural company with milking productivity corresponding to the average values found in the Czech Republic for the Czech Fleckvieh cattle was chosen for a thorough analysis of the milk productivity and the lactation process. Placed at an elevation of 500 to 550 m above sea-level and spread on 750 ha of agricultural land, with 600 ha being used as field lands, its production herd consists of 120 cows.

#### **3.1. Progression of lactation curves**

Lactation curves for first, second, third and following lactations are shown in Figure 4. Major differences in milking levels between the first and the remaining lactations are easily seen from the progression of the curves. The highest average milk production was found for the duration of the second lactation, about 10 kg of milk in its beginning as compared to the first lactation curve. During the third and the following lactations there is a slight decrease in milk production, although the progress of the curves resembles that of the second.

Figure 5 shows lactation curves with progression of lactation for cows with starting milk production up to 25 kg, between 25 and 30 kg and above 30 kg of milk. From the differences in the progression of the lactation curves it is possible to state that the initial milk production up to 25 kg has lactation curve very close to the optimal progression, the initial milking is higher by almost 4 kg of milk and this elevated value holds for four whole months. The lactation curve is very steep for high initial milk production where the initial milk increase is only 2.4 kg and keeps for two months which implies insufficient nutrition levels supplied by the company that does not cover the milk production in the initial lactation stage. For the group of cows with initial average milk production over 30 kg the lactation curve suggests no initial milk production stage (no production increase) and there is a continuous decrease of milk production from the beginning of lactation. This decrease is significant enough to level the milk production of this group with group 2 after just one month, thus implying that in given conditions of nutrition and care the cows with higher genetic potential towards milk production cannot make use of this potential and their production abilities are not fully used.

**Figure 4.** Progression of lactation curves according to lactation order

**Figure 5.** Progression of lactation curve for different milk production levels

Progression of lactation curves for the individual groups of cows (divided according to the initial milk production of up to 25, 25 to 30 and above 30 kg of milk) is shown individually in Figures 6 to 8. Expressing the progression of the curves linearly clarifies the differences in the slope of the individual lines and also in the fraction of the explained variability expressed by the coefficient of determination (R2). While the fraction of variability for the lactation curve of cows with production up to 25 kg of milk in Figure 6 is 75.11%, the group depicted in Figure 7 with production between 25 and 30 kg exerts 92% and the cows with initial productivity over 30 kg reach 98.5%. This would then imply that we practically have not a lactation curve but lactation straight line. The straight-line parameters shown in Figure 8 (y = -1.7735x + 35.451) also show the relatively steep decrease of milk production for this group.

326 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

production abilities are not fully used.

10

10,00

15,00

20,00

25,00

**kg**

30,00

35,00

15

20

25

**milk production (kg)**

30

35

**Figure 4.** Progression of lactation curves according to lactation order

**Figure 5.** Progression of lactation curve for different milk production levels

lactation stage. For the group of cows with initial average milk production over 30 kg the lactation curve suggests no initial milk production stage (no production increase) and there is a continuous decrease of milk production from the beginning of lactation. This decrease is significant enough to level the milk production of this group with group 2 after just one month, thus implying that in given conditions of nutrition and care the cows with higher genetic potential towards milk production cannot make use of this potential and their

**Lactation curves**

1. lactation 2.lactation 3.lactation >3. lactation

to 25 kg 25-30 kg over 30 kg

1 2 3 4 5 6 7 8 9 10 **month of lactation**

**Milk production**

1 2 3 4 5 6 7 8 9 10 **month of lactation**

**Figure 6.** Progression of lactation curve for dairy cows with productivity up to 25 kg of milk

**Figure 7.** Progression of lactation curve for dairy cows with productivity from 25 to 30 kg of milk

**Figure 8.** Progression of lactation curve for dairy cows with productivity over 30 kg of milk

#### **3.2. Content of milk components during lactation process**

Changes in milk content during the lactation process were judged based on analysis of milk samples taken during performance testing. The followed group of cows was divided according to their milk production during initial lactation into three groups with production up to 25 kg, from 25 to 30 kg and over 30 kg of milk and for each cow fat, protein and lactose content in the milk was established for a period of 10 months from initial lactation.

Values of fat content in milk samples show that there is no significant difference between the fat content for the three groups. The interesting feature are the maximum and minimum values measured for individual cows where individuals with very low fat content (2%) or quite high fat content (over 5% or even over 6%) appear in each of the three groups. These values suggest a possible use of the different fat content in milk as selection criteria for changed requirements on the quality of milk from the buyer or for changed financial evaluation for the individual milk ingredients.

The changes in fat content in milk for the individual groups are graphically represented in Figures 9 to 11. Coefficient of determination is similar for all three groups (R = 0.7623 to 0.7852) although the changes of the fat content during the lactation process resemble a parabolic curve where a continuous increase of the fat content at the end of the lactation process is clearly seen for all three groups. The progress of the theoretical curve for the individual groups changes and shows a continuous increase in fat content from the group with the lowest initial productivity (y = 0.0196x2 – 0.1464x + 3.9892) to the group with the highest one (y = 0.0085x2 – 0.0571x + 3.8103). The inflexion points that are characterized by the top of a parabola and represent the point when an increase in fat content appears after an initial decrease are shown to differ slightly for the three groups. The group with lowest productivity (up to 25 kg) exhibited the lowest fat content 3.71% after 3 months and 24 days of lactation, the second group showed a minimum of 3.70% after 3 months and 6 days and the third group with highest milk production (over 30 kg) reached the minimum after 3 months of lactation with 3.73% of fat.

**Figure 9.** Milk fat during lactation in milk production up to 25 kg

328 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

over 30 kg y = -1,7735x + 35,451

R2

= 0,9906

**Figure 8.** Progression of lactation curve for dairy cows with productivity over 30 kg of milk

content in the milk was established for a period of 10 months from initial lactation.

Changes in milk content during the lactation process were judged based on analysis of milk samples taken during performance testing. The followed group of cows was divided according to their milk production during initial lactation into three groups with production up to 25 kg, from 25 to 30 kg and over 30 kg of milk and for each cow fat, protein and lactose

1 2 3 4 5 6 7 8 9 10 **month of lactation**

Values of fat content in milk samples show that there is no significant difference between the fat content for the three groups. The interesting feature are the maximum and minimum values measured for individual cows where individuals with very low fat content (2%) or quite high fat content (over 5% or even over 6%) appear in each of the three groups. These values suggest a possible use of the different fat content in milk as selection criteria for changed requirements on the quality of milk from the buyer or for changed financial

The changes in fat content in milk for the individual groups are graphically represented in Figures 9 to 11. Coefficient of determination is similar for all three groups (R = 0.7623 to 0.7852) although the changes of the fat content during the lactation process resemble a parabolic curve where a continuous increase of the fat content at the end of the lactation process is clearly seen for all three groups. The progress of the theoretical curve for the individual groups changes and shows a continuous increase in fat content from the group with the lowest initial productivity (y = 0.0196x2 – 0.1464x + 3.9892) to the group with the highest one (y = 0.0085x2 – 0.0571x + 3.8103). The inflexion points that are characterized by the top of a parabola and represent the point when an increase in fat content appears after an initial decrease are shown to differ slightly for the three groups. The group with lowest productivity (up to 25 kg) exhibited the lowest fat content 3.71% after 3 months and 24 days

**3.2. Content of milk components during lactation process**

evaluation for the individual milk ingredients.

12

17

22

**kg of milk**

27

32

37

**Figure 10.** Milk fat during lactation in milk production from 25 to 30 kg

Protein content was analysed for the same group of cows as above. Were founded, that not only lower average values of protein content as compared to fat, but also smaller variations of the values characterised by the spread and determinant deviation. Although the maximum values of protein content found in individual cows (in some cases high above 4%) suggest a possibility of selection towards attempts to increase protein content in milk or to increase the usability of selected dairy products.

**Figure 11.** Milk fat during lactation in milk production over 30 kg

Graphic expression of the protein content in milk during the lactation process in Figures 12 to 14 shows a continuous increase in protein content in milk (of course under the presumption that the high protein percentage in colostrum during the first few days after calving is disregarded) with the tendency towards linearity. With increasing initial milk production after calving the real progression of the changes in protein content in milk approaches linearity also inferred by the determinant coefficient that was estimated to 0.8457 for the first group with up to 25 kg of milk, 0.884 for the second group and 0.9951 for the third group with milk production over 30 kg of milk. The slopes of the individual graph lines also indicate differing levels of protein as an inverse relation between protein content and milk production.

**Figure 12.** Protein content during lactation in milk production up to 25 kg

**Figure 13.** Protein content during lactation in milk production from 25 to 30 kg

group 3 y = 0,0085x<sup>2</sup>

R2

group 1 y = 0,0668x + 3,1879 R2

= 0,8457

= 0,7623

**Figure 11.** Milk fat during lactation in milk production over 30 kg

**Figure 12.** Protein content during lactation in milk production up to 25 kg

and milk production.

3,2

3,4

3,6

3,8

**milk protein [%]**

4,0

4,2

3,6 3,8 4,0 4,2 4,4 4,6 4,8

**milk fat [%]**

Graphic expression of the protein content in milk during the lactation process in Figures 12 to 14 shows a continuous increase in protein content in milk (of course under the presumption that the high protein percentage in colostrum during the first few days after calving is disregarded) with the tendency towards linearity. With increasing initial milk production after calving the real progression of the changes in protein content in milk approaches linearity also inferred by the determinant coefficient that was estimated to 0.8457 for the first group with up to 25 kg of milk, 0.884 for the second group and 0.9951 for the third group with milk production over 30 kg of milk. The slopes of the individual graph lines also indicate differing levels of protein as an inverse relation between protein content

1 2 3 4 5 6 7 8 9 10 **month of lactation**

1 2 3 4 5 6 7 8 9 10 **month of lactation**

**Figure 14.** Protein content during lactation in milk production over 30 kg

Lactose content during lactation progress for the individual groups is shown in Figures 15 to 17. Average values of lactose content in the individual lactation months vary between 3.67 and 4.93 % and no trends among the groups or durations can be seen. The graphical expression especially shows that the lactation progression corresponds more to polynomic functions, although by different degrees and precisions (for example the group with milk production to 25 kg has determinant coefficient 0.8401). There is thus no correlation between lactose content and milk production and causes to changes in lactose levels need to be sought elsewhere.

**Figure 15.** Lactose content during lactation in milk production up to 25 kg

**Figure 16.** Lactose content during lactation in milk production from 25 to 30 kg

#### **3.3. Correlation between reproduction parameters and milk content**

Mutual dependencies between lactation order and parameters of milk productivity and fertility are shown in Table 11 expressed in terms of correlation coefficients. Statistically significant values are given in bold letters. Values in the first column show that satisfactory reproducibility was found for the experimental set (meantime of 375 days, insemination interval 64.4 days and service period of 111.94 days) and also that the milk content corresponds to the expected values. The average number of milking times per day is 2.62. It is clear from the obtained correlation coefficients that there is no connection between lactation order and the other followed parameters. Statistically significant is a positive but

**Figure 17.** Lactose content during lactation in milk production over 30 kg

group 1 y = -0,0006x4 + 0,0147x3 - 0,116x2 + 0,3492x + 4,5216

1 2 3 4 5 6 7 8 9 10 **month of lactation**

R2 = 0,6096

group 2 y = 0,0004x5 - 0,0116x4 + 0,1222x3 - 0,5783x2 + 1,1502x + 4,1569

R2 = 0,8401

**Figure 15.** Lactose content during lactation in milk production up to 25 kg

4,75

4,65 4,70 4,75 4,80 4,85 4,90 4,95 5,00

**lactose [%]**

4,80

4,85 4,90

**lactose [%]**

4,95

5,00

**Figure 16.** Lactose content during lactation in milk production from 25 to 30 kg

**3.3. Correlation between reproduction parameters and milk content**

Mutual dependencies between lactation order and parameters of milk productivity and fertility are shown in Table 11 expressed in terms of correlation coefficients. Statistically significant values are given in bold letters. Values in the first column show that satisfactory reproducibility was found for the experimental set (meantime of 375 days, insemination interval 64.4 days and service period of 111.94 days) and also that the milk content corresponds to the expected values. The average number of milking times per day is 2.62. It is clear from the obtained correlation coefficients that there is no connection between lactation order and the other followed parameters. Statistically significant is a positive but

1 2 3 4 5 6 7 8 9 10 **month of lactation**

slight correlation between the length of insemination interval and the service period (rxy = 0.309) which is quite logical. There is on the other hand a slightly negative statistically significant dependence between protein content in the milk and the length of the insemination interval (rxy = -0.353) that would imply a connection to a deficit in cow nutrition after calving that is expressed by a lower protein content (also by less pronounced rutting period of the cows, their deteriorated health and therefore also in later release of the cows after calving). Low although statistically significant dependence was found between fat and protein content in milk (rxy = 0.290). Increasing number of milking times is shown to have a negative impact on the fat content in milk (rxy = -0.230).

**Table 11.** Correlation between milking and reproduction parameters

## **4. Conclusion**

Studies involving evaluation of results on milking with the help of automated milking systems (AMS) of the type Lely Astronaut A3 and finding differences that depend on size, levels of production and ownership were conducted in seven agricultural companies in the Czech Republic. The results were analysed in companies keeping dairy cows of the Czech Fleckvieh cattle (n = 2004), Holstein cattle (n= 4008) and both these breeds together (n = 1670). In a chosen company with productivity results corresponding to the average value of the Czech Republic (n = 120) a thorough analysis of relations between milk production, milk content and reproducibility was conducted. These conclusions can be drawn from the obtained results:

14. For high productivity cows (with over 30 kg as initial lactation) the progression of lactation resembled a straight line with a steep decreasing slope (R = 0.9875).

334 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

1. The number of cows milked by one robot should not by much exceed 60.

compared to large cooperative companies.

Studies involving evaluation of results on milking with the help of automated milking systems (AMS) of the type Lely Astronaut A3 and finding differences that depend on size, levels of production and ownership were conducted in seven agricultural companies in the Czech Republic. The results were analysed in companies keeping dairy cows of the Czech Fleckvieh cattle (n = 2004), Holstein cattle (n= 4008) and both these breeds together (n = 1670). In a chosen company with productivity results corresponding to the average value of the Czech Republic (n = 120) a thorough analysis of relations between milk production, milk content and reproducibility was conducted. These conclusions can be drawn from the

2. Significantly higher milk production was found in small privately owned companies as

3. Disregarding the type of company where the cows are kept, the Holstein cattle breed shows significantly much higher milk production than the cows of the Czech Fleckvieh

4. Statistically significant differences in the average number of milking times per day were found between the individual farms. The frequency of milking time varied between 1.97

5. Differences in the number of milking times according to the ownership are significant,

6. The Holstein cattle were statistically more often milked than the Czech Fleckvieh cattle

7. The number of robot refusals is very individual for each stable and varies between 0.72

8. The refusals are much less frequent in small privately owned farms (1.11) as compared

9. Cows of the Czech Fleckvieh breed are significantly more often refused by the robot

10. The numbers of cows that need to be taken to the robot by a keeper vary between 8.2 to 25.3% in the different companies. It is much more frequent in large companies (20.1%)

11. The need to lead the cows to the robot is lower for the Czech Fleckvieh (8.8%) as

12. A significant difference was found in the progression height of the first lactation curve

13. Only cows with initial milk production below 25 kg of milk exhibited an optimal progression of the lactation curve for an average productivity of the herd at 6450 kg of milk. Cows with milk production of 25 to 30 kg showed a steep lactation curve while the cows with milk production above 30 kg did not show any increasing milk flow and

to medium (2.18) and large (2.30) cooperative companies.

than in small (9.3%) and medium sized ones (9.7%).

their lactation curve fell rapidly already from the beginning.

**4. Conclusion**

obtained results:

cattle.

and 2.67 times per day.

(2.45 compared to 2.32).

to 3.66 times per cow and day.

(2.55) in comparison to the Holstein dams.

compared to the Holstein breed (14.4%).

and the curves of the following lactations.

though much smaller (2.34 to 2.47).

**Figure 18.** Milking by AMS (photo A. Váchová)

**Figure 19.** Computer install into AMS allowed getting the data during lactation (photo A. Váchová)

**Figure 20.** Cow get concentration feed in robot (photo A. Váchová)

## **Author details**

336 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

**Figure 19.** Computer install into AMS allowed getting the data during lactation (photo A. Váchová)

**Figure 20.** Cow get concentration feed in robot (photo A. Váchová)

Miroslav Maršálek, Jarmila Voříšková and Jana Zedníková *University of South Bohemia in Ceske Budejovice, Faculty of Agriculture, Czech Republic*

## **Acknowledgement**

This study was supported by the Ministry of Agriculture of the Czech Republic, NAZV QH91260.

## **5. References**

Bouška, J. (2006*). Chov dojeného skotu.* Profi Press, ISBN 80-86726-16-9, Praha

	- Maršálek, M.; Kottová, M.; Zedníková, J. & Voříšková, J. (2009). Ekonomický efekt zavedení dojících robotů v zemědělském podniku. *Agritech Science,* No. 2 , pp. 1-3, ISSN 1802- 8942
	- Přikryl, M. (1997). *Technologická zařízení staveb živočišné výroby.* Tempo Press, ISBN 80- 901052-0-3 Praha
	- Rodenburg, J. (2002). Robotic Milkers: What, Where…and How Much!? Dairy Management Conference, pp. 1-18, Ohio, December 16 -17, 2002
	- Rotz, C. A.; Coiner, C. U. & Soder, K. J. (2003). Automatic Milking System, Farm Size and Milk Production. *Journal of Dairy Science,* Vol. 86, No. 12, pp. 4167-4177, ISSN 0022-0302
	- Stelwagen, K. (2001). Effect of Milking Freguency on Mammary Functioning and Shape of the Lactation Curve. *Journal of Dairy Science,* Vol. 84, E. Suppl., pp. 204-211E, ISSN 0022- 0302
	- Sychra, L. (2001).: *Nové trendy a poznatky v chovu skotu.* MZLU, 2001, ISBN 80-7157-541-0, Brno
	- Šťastný, V. (2010). Dojení robotem. 3.1.2010, Available from http://www.zootechnik.cz/zoodr3.php
	- Tančin, V. & Tančinová, D. (2008). *Strojové dojenie kráv a kvalita mlieka.* SPU, ISBN 978-80- 88872-80-1, Nitra
	- Vegricht, J. (2002). Study of Using Automatic Milking Systems on Large Dairy Farms. *Research In Agricultural Engineering*, Vol. 48, No. 1, pp.1-6, ISSN1212-9151
	- Veselovský, Z. (2005). *Etologie-Biologie chování zvířat.* Academia, ISBN 80-200-1331-8, Praha
	- Wagner-Storch, A.M. & Palmer, R.V. (2003). Feeding Behavior, Milking Behaviourand Milk Yields of Cows Milked in a Parlor versus an Automatic Milking Sysstem. *Journal of Dairy Science*, Vol.86, No. 4, pp.1494-1502 ISSN 0022-0302
	- Weiss, D.; Moestl, E. & Bruckmaier, R. M. (2005). Physiological and Behavioural Effects of Changeover from Conventional to Automatic Milking in Dairy Cows with and without Previous Experience. *Veterinary Medicine*, Vol. 50, No.6, pp. 253-261, ISSN 0375-8427

**Chapter 15**

## **Differential Characteristics of Milk Produced in Grazing Systems and Their Impact on Dairy Products**

A.M. Descalzo, L.Rossetti, R. Páez, G. Grigioni, P.T. García, L. Costabel, L. Negri, L. Antonacci, E. Salado, G. Bretschneider, G. Gagliostro, E. Comerón and M.A. Taverna

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/ 50760

## **1. Introduction**

338 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

Conference, pp. 1-18, Ohio, December 16 -17, 2002

Šťastný, V. (2010). Dojení robotem. 3.1.2010, Available from

*Dairy Science*, Vol.86, No. 4, pp.1494-1502 ISSN 0022-0302

http://www.zootechnik.cz/zoodr3.php

8942

0302

Brno

901052-0-3 Praha

88872-80-1, Nitra

Maršálek, M.; Kottová, M.; Zedníková, J. & Voříšková, J. (2009). Ekonomický efekt zavedení dojících robotů v zemědělském podniku. *Agritech Science,* No. 2 , pp. 1-3, ISSN 1802-

Přikryl, M. (1997). *Technologická zařízení staveb živočišné výroby.* Tempo Press, ISBN 80-

Rodenburg, J. (2002). Robotic Milkers: What, Where…and How Much!? Dairy Management

Rotz, C. A.; Coiner, C. U. & Soder, K. J. (2003). Automatic Milking System, Farm Size and Milk Production. *Journal of Dairy Science,* Vol. 86, No. 12, pp. 4167-4177, ISSN 0022-0302 Stelwagen, K. (2001). Effect of Milking Freguency on Mammary Functioning and Shape of the Lactation Curve. *Journal of Dairy Science,* Vol. 84, E. Suppl., pp. 204-211E, ISSN 0022-

Sychra, L. (2001).: *Nové trendy a poznatky v chovu skotu.* MZLU, 2001, ISBN 80-7157-541-0,

Tančin, V. & Tančinová, D. (2008). *Strojové dojenie kráv a kvalita mlieka.* SPU, ISBN 978-80-

Vegricht, J. (2002). Study of Using Automatic Milking Systems on Large Dairy Farms.

Weiss, D.; Moestl, E. & Bruckmaier, R. M. (2005). Physiological and Behavioural Effects of Changeover from Conventional to Automatic Milking in Dairy Cows with and without Previous Experience. *Veterinary Medicine*, Vol. 50, No.6, pp. 253-261, ISSN 0375-8427

Veselovský, Z. (2005). *Etologie-Biologie chování zvířat.* Academia, ISBN 80-200-1331-8, Praha Wagner-Storch, A.M. & Palmer, R.V. (2003). Feeding Behavior, Milking Behaviourand Milk Yields of Cows Milked in a Parlor versus an Automatic Milking Sysstem. *Journal of*

*Research In Agricultural Engineering*, Vol. 48, No. 1, pp.1-6, ISSN1212-9151

Functional foods may exhibit health benefits beyond their nutritional value. Many traditional recommendations on food selection have included this view. In more recent years the interest in food with specific health benefits has greatly increased and stimulated the development of respective products for the food market. At the same time large efforts are made to substantiate health claims by validated experimental methods.

Dairy products have come to play an important role in this context. Regarding the nutritional and extra nutritional characteristics of milk, it can be considered a multifunctional food product. Milk is a source of proteins, lipids, vitamins and minerals, but also exerts other beneficial properties due to the presence of numerous bio-active molecules either through the direct consumption of milk or its derivatives (Guimont and others, 1997).

It is widely recognized that pasture, when efficiently grazed, is the cheapest feedstuff (Dillon, 2006) and that the cost of milk production is reduced as the proportion of grazed pasture in the diet of the cow increases (Dillon and others, 2008). Nevertheless, using pasture as the only source of food is inadequate for high milk producing cows. The lower intake of dry matter (DM) and energy would be the main cause of suboptimal production even under conditions of adequate quantity and quality of pasture (Kolver y Muller, 1998; Reis y Combs, 2000; Bargo and others, 2002).

Cows adapt to grazing through increased forage intake, but in order to achieve this, it is necessary to ensure around 60 kg DM/cow/day (Delagarde and others, 2004). Under these

© 2012 Descalzo et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Descalzo et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

conditions, the maximum yield obtainable on high quality pastures seems to be at around 22-25 kg/cow/day in spring and 18 kg/cow/day in autumn. However, at high levels of energy supplementation, rumen pH, the diurnal pattern of volatile fatty acid concentration and fiber digestion may be altered because of changes in rumen fermentation (Tamminga, 1993). These changes may provoke lower consumption of forage and a drop in the fat content of milk. In this regard, studies carried out in EEA INTA Rafaela suggest that the optimal level of supplementation that could maximize milk production without affecting the rumen of dairy cows grazing high quality lucerne, would be 7 kg of concentrate containing approximately 70% corn (Salado and others, 2010 a and b).

On the other hand, it is also known that the amount of conjugated linoleic acid (CLA) in milk fat increases according to the level of pasture included in the diet, with the highest CLA concentrations found in cattle fed 100 % pasture (Dhiman and others, 1999).

Alike other country, Argentina is moving forward to more complex livestock production systems. In this regard, component feeding systems based on the use of grazed pasture are gradually being replaced by confining ones, in which conserved forages (hay, silage) and concentrates (cereal grains, agro-industrial byproducts) are fed to dairy cattle as a total mixed ration (TMR). This phenomenon could be explained by the fact that argentinean dairy farmers are looking for ways to release land for agriculture (mainly for the cultivation of soybean [Glycine max]), which is considered a more profitable and simpler activity than dairy farming.

Nowadays, it is supported that the extreme intensification of dairy production systems would lead to lose not only the advantage that grazed pastures have on the reduction of milk production costs (Garcia and Fulkerson, 2005; Dillon and others, 2008; Macdonald and others, 2008, McEvoy and others, 2008) but also the marketing niches that look for dairy products from pasture-fed cattle due to their high CLA content (White and others, 2002). In this respect, pasture-fed dairy cows produced a significantly higher concentration of CLA (83%) than TMR fed-confined dairy cows (0.66 vs. 0.36% of the total fatty acids) (White and others, 2001).

For that purpose, grass-based dairy production should consider some productive aspects such as high-quality pasture and livestock adapted to a high-forage diet.

Pasture is a highly organized structure which changes with season and management. These changes can affect photosynthesis and hence dry matter accumulation, the degree of utilization and the feeding value of herbage. At the same time, these changes in pasture can affect the quality of milk and thereby the quality of dairy products.

## **2. Functional compounds characterized in diets for dairy cows**

#### **2.1. Natural antioxidants: Lipid-soluble vitamins**

Plant secondary metabolites are incorporated into the tissues through dietary delivery. Among these, alpha- tocopherol is the main active form of vitamin E in animal tissues. It is selectively incorporated among eight isomers (alpha, beta, gamma and delta tocopherol and tocotrienol respectively) that are naturally found in plants. Vitamin E is the main fat-soluble antioxidant incorporated into animal cell membranes and is also a regulator of cellular functions that are mediated by protein kinase C.

Carotenoids are lipophilic secondary plant and algae products consisting of eight isoprene units (C40). Among about 800 known carotenoids, up to ten of them have been determined in forages, namely oxygen-containing xanthophylls (lutein, epilutein, antheraxanthin, neoxanthin, violaxanthin and zeaxanthin), and carotenes (preferably -carotene) of a hydrocarbon nature. Due to the high number of double bonds, carotenoids undergo oxidation and isomerisation (cis-isomers formation from all-trans-ones), in cut forage by the action of light. Carotenoids in cow's milk and consequently in milk products are important for human health and nutrition as natural antioxidants and some of them as precursors of vitamin A, among which all-trans--carotene is the main provitamin. Moreover, milk fat carotenoids cause the yellow color of butter and many cheeses, which is positively perceived by many consumers as ''green image" because of its association with grazing animals. This vitamin is implicated in reproduction, immunity and normal function of retinal cells. Lutein is another carotenoid which is abundant in plants and can be used as a tracer of pasture feeding, as animals are not able to synthesize this molecule and it is stored in the animal's fat after absorption and thus found in milk and meat (Prache 2005).

These molecules are highly variable in forages, silage and hay (Müller 2007; Hidiroglou 1996), and depend on the fermentation form as well as on the conservation method. Praché and others (2009) reported values between 40 and 123 microg/g DM for beta carotene (60 microg/g DM for lucerne) and from 167 to 437 microg/g DM for lutein (142 for lucerne) in pasture collected between Spring (May) and Summer (July).

Similar variation in carotenoid levels in mountain grazing was found by Calderón and others (2006) with values that ranged between 18 and 60.4 microg/g DM for beta carotene and between 96 and 262 microg/g DM for lutein.

Data from four separated experiments, which were carried out in INTA, also show a high variability. As shown in table 1, fresh pasture showed the highest input of alpha- tocopherol and beta-carotene among feed components. The variation of its composition is high, depending on seasonal variations, phenological stage and conservation technology, among other factors. Other components of feed are also sources of dietary antioxidants, but they showed less variation and their contribution in vitamins was less significant compared with pasture.

#### **2.2. Natural antioxidants: Polyphenols**

340 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

containing approximately 70% corn (Salado and others, 2010 a and b).

conditions, the maximum yield obtainable on high quality pastures seems to be at around 22-25 kg/cow/day in spring and 18 kg/cow/day in autumn. However, at high levels of energy supplementation, rumen pH, the diurnal pattern of volatile fatty acid concentration and fiber digestion may be altered because of changes in rumen fermentation (Tamminga, 1993). These changes may provoke lower consumption of forage and a drop in the fat content of milk. In this regard, studies carried out in EEA INTA Rafaela suggest that the optimal level of supplementation that could maximize milk production without affecting the rumen of dairy cows grazing high quality lucerne, would be 7 kg of concentrate

On the other hand, it is also known that the amount of conjugated linoleic acid (CLA) in milk fat increases according to the level of pasture included in the diet, with the highest

Alike other country, Argentina is moving forward to more complex livestock production systems. In this regard, component feeding systems based on the use of grazed pasture are gradually being replaced by confining ones, in which conserved forages (hay, silage) and concentrates (cereal grains, agro-industrial byproducts) are fed to dairy cattle as a total mixed ration (TMR). This phenomenon could be explained by the fact that argentinean dairy farmers are looking for ways to release land for agriculture (mainly for the cultivation of soybean [Glycine max]), which is considered a more profitable and simpler activity than dairy farming. Nowadays, it is supported that the extreme intensification of dairy production systems would lead to lose not only the advantage that grazed pastures have on the reduction of milk production costs (Garcia and Fulkerson, 2005; Dillon and others, 2008; Macdonald and others, 2008, McEvoy and others, 2008) but also the marketing niches that look for dairy products from pasture-fed cattle due to their high CLA content (White and others, 2002). In this respect, pasture-fed dairy cows produced a significantly higher concentration of CLA (83%) than TMR fed-confined dairy cows (0.66 vs. 0.36% of the total fatty acids) (White and others, 2001).

For that purpose, grass-based dairy production should consider some productive aspects

Pasture is a highly organized structure which changes with season and management. These changes can affect photosynthesis and hence dry matter accumulation, the degree of utilization and the feeding value of herbage. At the same time, these changes in pasture can

Plant secondary metabolites are incorporated into the tissues through dietary delivery. Among these, alpha- tocopherol is the main active form of vitamin E in animal tissues. It is selectively incorporated among eight isomers (alpha, beta, gamma and delta tocopherol and tocotrienol respectively) that are naturally found in plants. Vitamin E is the main fat-soluble

such as high-quality pasture and livestock adapted to a high-forage diet.

affect the quality of milk and thereby the quality of dairy products.

**2.1. Natural antioxidants: Lipid-soluble vitamins**

**2. Functional compounds characterized in diets for dairy cows**

CLA concentrations found in cattle fed 100 % pasture (Dhiman and others, 1999).

Polyphenols are antioxidants that may prevent various pathologies (cancer, cardiovascular diseases, immune deficiencies, etc.). Particularly, isoflavones may have a preventive effect against breast and prostate cancers and, to a certain extent, against some disorders related to menopause or andropause. The active molecules are plant-based isoflavones (directly absorbed) as well as a transformation component in the large intestine, equol. The main source of isoflavones in human diet are soy and soy-derived food. Among forage plants, other legumes (certain types of clover and lucerne) contain high levels of isoflavones. They may constitute up to 2% of dry matter in purple clover and are well preserved after silaging. Of the active isoflavones in forage, daidzein and formononetin were transformed in the rumen into

equol, which was secreted in the milk of goat and cow species in sufficient quantities to suggest they may exert a biological effect. Moreover, Besle and others (2010) found different phenolic compounds that can be used as tracers for the diet of dairy cows. Data shown in table 1, indicate that coumestrol was the main phytoestrogen found in lucerne (pasture and hay), corn silage, sunflower expeller and wheat bran. Concentrate mixture showed higher amounts of daizein, than other dietary compounds, probably derived from soy. In this experiment, the aglycone and glucoside forms were not differentiated. The extraction was performed in acid medium and therefore, the species were converted into the aglycone form.

a For the determination of antioxidant vitamins, samples from individual components of the diet were extracted with hexane (after a saponification step) and quantified using high performance liquid chromatography. Carotenoids were monitored at 445 nm, tocopherols were detected by fluorescence at 296-330 nm ( exc- em respectively) and retinol at 335 nm (Rossetti et al, 2010).

b Mean variation coefficient of all vitamins calculated for each dietary compound.

**Table 1.** Levels of fat-soluble antioxidants in dietary components used in INTA experiments with dairy cows.

#### **2.3. Volatile compounds**

342 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

medium and therefore, the species were converted into the aglycone form.

*Genistein* 2.165 Not determined 1.110 Not

*Lutein* 481.92 1128.21 85.80 Not

*Daidzein* 5.568 Not determined 14.061 Not

*Coumestrol* 25.821 Not determined 10.993 Not

*Oat pasture*

*tocopherol* 19.679 9.10 67.231 101.538 3.519 *Alfa-tocopherol* 64.415 16.14 15.937 7.042 20.720 *Beta-carotene* 97.518 66.67 0.903 0.301 nd

*% CV* 61.98 17.15 34.51 nd 21.13

*tocopherol* 20.111 1.521 48.809 3.345 *Alfa-tocopherol* 17.702 2.445 27.533 24.612 *Beta-carotene* 13.243 0.355 nd 5.493 *Genistein* 1.593 Not determined 1.085 2.103

*Daidzein* 5.070 Not determined 1.665 1.962 *Coumestrol* 22.731 Not determined 14.652 18.461 *% CV b* 35.75 nd 9.95 38.68

a For the determination of antioxidant vitamins, samples from individual components of the diet were extracted with hexane (after a saponification step) and quantified using high performance liquid chromatography. Carotenoids were monitored at 445 nm, tocopherols were detected by fluorescence at 296-330 nm ( exc- em respectively) and retinol at

**Table 1.** Levels of fat-soluble antioxidants in dietary components used in INTA experiments with

*Lucerne pasture*

*(mg/Kg DM) Corn silage Sunflower*

*Lutein* 41.57 Not determined Not

b Mean variation coefficient of all vitamins calculated for each dietary compound.

*Antioxidants (mg/Kg DM)a*

*Antioxidants*

335 nm (Rossetti et al, 2010).

dairy cows.

*Gamma-*

*Gamma-*

equol, which was secreted in the milk of goat and cow species in sufficient quantities to suggest they may exert a biological effect. Moreover, Besle and others (2010) found different phenolic compounds that can be used as tracers for the diet of dairy cows. Data shown in table 1, indicate that coumestrol was the main phytoestrogen found in lucerne (pasture and hay), corn silage, sunflower expeller and wheat bran. Concentrate mixture showed higher amounts of daizein, than other dietary compounds, probably derived from soy. In this experiment, the aglycone and glucoside forms were not differentiated. The extraction was performed in acid

*Concentrate*

*mixture Soy expeller Sunflower*

determined

determined Not determined

*pellets Wheat Bran Lucerne Hay*

determined 2.884

determined 2.035

determined 11.943

*expeller*

Not determined Terpenes are direct biomarkers of animal diet as they are not modified by animal metabolism. In addition, the transfer of these compounds into milk fat is very fast and apparently exhibits no memory effect (Viallon and others, 2000). Therefore, their presence in milk and dairy products could be used to discriminate contrasting feeding conditions (Prache and others, 2005).

Lucerne pasture showed the highest volatile production (table 2). The most abundant terpene was d-limonene found in all dietary components. Only three minor compounds, nerol, menthol and linalyl acetate were found exclusively in lucerne. Terpinen-4-ol, was found in lucerne pasture and maize silage in a 2 to 1 proportion. In addition, ocimene was 4.5 times higher in lucerne pasture compared with the other components and linalool was 4 times higher than in maize silage and wheat bran. These results indicate that a group of terpenes, and their abundance and not a single compound could be an indicator of the different components of the diet.

a Volatile terpens were determined in individual feedstuff given to dairy cows in INTA experiments (2008-2009). For this assay, a fiber (CAR/PDMS/DVB 2cm-50/30mm) was used to extract/concentrate volatile compounds in the headspace (HS) of chopped samples. GC-FID was performed on a Shimadzu 14-B GC with a flame ionization detector (Negri et al., 2008).

b Mean variation coefficient of all volatiles calculated for each dietary compound.

**Table 2.** Volatile terpenoid compounds in dietary components used in INTA experiments with dairy cows.

Lucerne samples also presented a higher proportion of volatile aldehydes (table 3), at the expense of trans-2-hexenal, which was the most abundant volatile after d-limonene. Tava and Peccetti (1997) reported that trans-2-hexenal was the most frequent volatile in 13 genotypes of lucerne. This compound, originated in flowers, should have a physiological role for plant pollination by hymenopterans (Peccetti and others 2002).

Hexanal and trans-2 hexenal are end-products of the oxidation of C18:2 and C18:3 fatty acids respectively. In fact, fresh pasture shows the highest hexanal production, resulting from linoleic acid, whereas trans-2-hexenal indicates a high percentage of C18:3. These fatty acids are oxidized when fresh pasture is cut, producing the corresponding aldehydes. Both aldehydes and terpenes are responsible for the typical odour of different plant species and could be found in milk and dairy products.

a Volatile aldehydes were determined as indicated in table 2.

b Mean variation coefficient of all volatiles calculated for each dietary compound.

**Table 3.** Volatile aldehydes in dietary components used in INTA experiments with dairy cows.

#### **2.4. Fatty acids**

344 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

role for plant pollination by hymenopterans (Peccetti and others 2002).

*Oat pasture*

could be found in milk and dairy products.

*Lucerne pasture*

a Volatile aldehydes were determined as indicated in table 2.

b Mean variation coefficient of all volatiles calculated for each dietary compound.

*Aldedhydes (Relative Area Units) a*

Lucerne samples also presented a higher proportion of volatile aldehydes (table 3), at the expense of trans-2-hexenal, which was the most abundant volatile after d-limonene. Tava and Peccetti (1997) reported that trans-2-hexenal was the most frequent volatile in 13 genotypes of lucerne. This compound, originated in flowers, should have a physiological

Hexanal and trans-2 hexenal are end-products of the oxidation of C18:2 and C18:3 fatty acids respectively. In fact, fresh pasture shows the highest hexanal production, resulting from linoleic acid, whereas trans-2-hexenal indicates a high percentage of C18:3. These fatty acids are oxidized when fresh pasture is cut, producing the corresponding aldehydes.  Volatile aldehydes in dietary components used in INTA experiments with dairy cows.

*Sunflower*

*expeller Corn silage Wheat Bran*

Information available about the fatty acid composition of pasture lipids is scarce. Unsaturated fatty acids, particularly n-3 polyunsaturated fatty acids (PUFA) and conjugated linoleic acid (CLA) may be beneficial to human health. Therefore, maximizing C18:3 and CLA in milk and dairy products would benefit human health and nutrition. Milk and meat are the only significant source of CLA in the human diet and this appears to be related to the consumption by ruminant of fresh pastures (Elgersma and others, 2003).

Sources of variation in pasture lipid composition are plant species, growth stage, temperature and light intensity. Fresh grass contains a high proportion (50-75%) of its total fatty acid content in the form of n-3 linolenic acid. Levels of linolenic acid vary with plant factors such as stage of maturity and with light disponibility. Fatty acid profiles are distinctive to particular species, which confirm that fatty acid composition of grasses is under considerable genetic control (Dewhurst and others 2001). This offers the potential to select for grasses with higher concentrations or altered types of fatty acids.

Garcia et a. (2007) evaluated the range of fatty acid concentrations within various pastures and forage crops, at different cutting dates and season in Argentina for beef and milk production. Season affected ether extract (EE%) and fatty acid composition of lucerne cultivars (table 4). No interactions were detected between dormancy group and season (p<0.344). During the fall increased C18:2 and decreased C16:0, C16:1, C18:0. The differences between spring and summer were for C16:1, C18:0, C18:2 and C18:3. The EE% and contribution of C18:2 and C18:3 were higher in fall compared with the other seasons.

The fatty acid composition, percentage of ether extract and contribution (g/100g DM) of C18:2 and C18:3 of several cultivars are shown in tables 5 and 6. Cutting date has affected significantly these parameters.

a b c Means in the same column with different letters differ (p<0.05)

**Table 4.** Fatty acid composition (%), ether extract (EE) and mg/g EE of C18:2 and C18:3 of different lucerne cultivars according to season.

Whole milk powder from pasture produced milk showed the same fatty acids profile as raw milk (table 5), and could constitute a natural source for n-3 fatty acids. In a similar way, antioxidants profile reproduced the composition of raw milk with estimated losses around 20 and 40% being higher for retinol and vitamin D3.

Higher natural antioxidants in whole milk powder lowered TBARS values in pasture (A70) samples compared to silage samples (A0) after 18-months shelf life assay (20ºC in sealed bi- laminated plastic pouches under nitrogen atmosphere).

M: Raw Milk; WMP: Whole Milk Powder; CH: Cheese (regianitto).

a: Thiobarbituric Reactive Substances

b: Relative Area Units

c: Ferric Reducing antioxidant activity

d: A0, A35 and A70, feed treatments with 0, 35 and 70% Lucerne pasture

ND: not done; nd: not determined, NS: Not significant

\* p<0.05 \*\*1 p<0.01 \*\*\*p<0.001

**Table 5.** Fatty acid composition (%) and ether extract (EE) (g/kg DM) of several pastures and forage crops. RGB (Rye grass Bill), Rye grass Florida (RGF), wheat Charrua (WC), wheat Guapo (WG), triticale Don Santiago® (TDS) and Centeno Quehue(CQ).

Differential Characteristics of Milk Produced in Grazing Systems and Their Impact on Dairy Products 347

\* p<0.05 \*\*p<0.01 \*\*\*p<0.001, NS: not significant

346 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

2.5c 4.4ab

2.6c 4.5ab

3.3abc 4.9a

3.0bc 4.0abc

3.5abc 3.9abc

4.8a 3.4abc

NS \*\*\* \*\*

Whole milk powder from pasture produced milk showed the same fatty acids profile as raw milk (table 5), and could constitute a natural source for n-3 fatty acids. In a similar way, antioxidants profile reproduced the composition of raw

Higher natural antioxidants in whole milk powder lowered TBARS values in pasture (A70) samples compared to silage samples (A0) after 18-months shelf life assay (20ºC in sealed bi- laminated plastic pouches under nitrogen

**Table 5.** Fatty acid composition (%) and ether extract (EE) (g/kg DM) of several pastures and forage crops. RGB (Rye grass Bill), Rye grass Florida (RGF), wheat Charrua (WC), wheat Guapo (WG), triticale

2.0b 1.9bc

1.8bc 1.9bc

2.0b 1.8bc

2.5a 2.0b

1.9b 1.5cd

1.7bcd 1.4d

milk with estimated losses around 20 and 40% being higher for retinol and vitamin D3.

 Contribution (g/100 g DM) of C18:2 n-6 and C18:3 n-3 of several pastures and forage crops

As expected, fatty acids profile was different among dietary components. As shown in table 7, the most abundant fatty acid in lucerne pasture was C18:3 and it can be the molecule that originates branched aldehydes, especially trans-2-hexenal, in this assay.

a mean of samples from years 2007 and 2008.

b mean variation coefficient of all fatty acids calculated for each dietary compound, nd: not detected.

**Table 7. Fatty acids** in dietary components used in INTA experiments with dairy cows.

## **3. Incorporation of antioxidant vitamins into milk**

### **3.1. Properties of milk produced on pasture**

The nature of cow's forage diet, i.e. botanical composition, maturity stage and preservation mode, strongly influences milk composition in fatty acids, vitamins and carotenoids (Chillard 2001, Hartman 1965). The contents of retinol, α-tocopherol and β-carotene, lutein, xantophylls, saturated and polyunsaturated fatty acids profile in plasma, milk and milk fat are influenced by the diet. In addition, some of these parameters also differed according to sire and stage of lactation (Lucas 2006, Soren 1999).

Calderón and others (2007) reported that the incorporation of grass silage and lucerne protein (75:25), in an experimental diet designed for Montbéliarde cows in midlactation induced a rapid increase in plasma concentrations of beta carotene and vitamin E. The incorporation of these compounds varied in a linear form with the proportion of grass within the diet. Similar responses were observed in milk for vitamin E, whereas there was an apparent saturation in milk concentrations of beta carotene at high levels of carotenoid intake, i.e., when plasma beta carotene exceeded 5 microg/ml.

In an experiment conducted in INTA-Rafaela (Santa Fe, Argentina), two isoenergetic diets (1.55 Mcal EN/kgDM) of contrasting nature: 70% lucerne pasture (ALF) and grain sorghum silage (SS) were offered to Holstein cows on their second third of lactation. Milk and protein yields were higher (P < 0.01) in ALF than SS diet (31.78, 26.14 l/v/d and 1.019, 0.841 kg/v/d, respectively). Fat concentrations were lower on ALF than SS diet (P < 0.01). All remaining variables were not significantly different, with the only exception of total solid contents (P < 0.05). No significant differences were found on live weight variation (ALF= 0.084, SS= 0.271 kg/v/d).

As shown in figure 1, fat-soluble vitamins increased over the first 20 days after a dietary shift from silage to grazed pasture (70% lucerne), and the concentrations were stabilized at day 40 with persistence until day 60 (Rossetti and others, 2010). Similarly, Calderón and others (2007) observed a rapid increase during the first 14 days in antioxidant vitamins in plasma and milk of Montbéliarde dairy cows, after a dietary shift from a low-carotenoid diet based on hay and concentrates to a high-carotenoid diet based on grass silage that was prepared from perennial ryegrass (*Lolium perenne*). The concentration of α-tocopherol, 13-cis-β-carotene, all-trans-βcarotene and lutein reached a plateau between 21 and 28 days and persisted until day 42. The incorporation of zeaxanthine into plasma was reported but it was either not detected in milk (Calderón et al., 2007), or its concentration was around 1% of total carotenoids (Butler 2008).

### **3.2. Effect of increasing pasture content into a total mixed ration diet in relation to health promoting compounds**

An increase of pasture in the diet, at the expense of a total mixed ration (TMR), has been shown to improve the incorporation of antioxidant vitamins into milk. Figure 2 shows the relationship between the percentage of pasture (grazed oat pasture) vs. α-tocopherol, retinol, all-trans-β-carotene and lutein in milk after 30-day treatments.

The nature of cow's forage diet, i.e. botanical composition, maturity stage and preservation mode, strongly influences milk composition in fatty acids, vitamins and carotenoids (Chillard 2001, Hartman 1965). The contents of retinol, α-tocopherol and β-carotene, lutein, xantophylls, saturated and polyunsaturated fatty acids profile in plasma, milk and milk fat are influenced by the diet. In addition, some of these parameters also differed according to

Calderón and others (2007) reported that the incorporation of grass silage and lucerne protein (75:25), in an experimental diet designed for Montbéliarde cows in midlactation induced a rapid increase in plasma concentrations of beta carotene and vitamin E. The incorporation of these compounds varied in a linear form with the proportion of grass within the diet. Similar responses were observed in milk for vitamin E, whereas there was an apparent saturation in milk concentrations of beta carotene at high levels of carotenoid

In an experiment conducted in INTA-Rafaela (Santa Fe, Argentina), two isoenergetic diets (1.55 Mcal EN/kgDM) of contrasting nature: 70% lucerne pasture (ALF) and grain sorghum silage (SS) were offered to Holstein cows on their second third of lactation. Milk and protein yields were higher (P < 0.01) in ALF than SS diet (31.78, 26.14 l/v/d and 1.019, 0.841 kg/v/d, respectively). Fat concentrations were lower on ALF than SS diet (P < 0.01). All remaining variables were not significantly different, with the only exception of total solid contents (P < 0.05). No significant differences were found on live weight variation

As shown in figure 1, fat-soluble vitamins increased over the first 20 days after a dietary shift from silage to grazed pasture (70% lucerne), and the concentrations were stabilized at day 40 with persistence until day 60 (Rossetti and others, 2010). Similarly, Calderón and others (2007) observed a rapid increase during the first 14 days in antioxidant vitamins in plasma and milk of Montbéliarde dairy cows, after a dietary shift from a low-carotenoid diet based on hay and concentrates to a high-carotenoid diet based on grass silage that was prepared from perennial ryegrass (*Lolium perenne*). The concentration of α-tocopherol, 13-cis-β-carotene, all-trans-βcarotene and lutein reached a plateau between 21 and 28 days and persisted until day 42. The incorporation of zeaxanthine into plasma was reported but it was either not detected in milk (Calderón et al., 2007), or its concentration was around 1% of total carotenoids (Butler 2008).

**3.2. Effect of increasing pasture content into a total mixed ration diet in relation**

An increase of pasture in the diet, at the expense of a total mixed ration (TMR), has been shown to improve the incorporation of antioxidant vitamins into milk. Figure 2 shows the relationship between the percentage of pasture (grazed oat pasture) vs. α-tocopherol,

retinol, all-trans-β-carotene and lutein in milk after 30-day treatments.

**3. Incorporation of antioxidant vitamins into milk**

**3.1. Properties of milk produced on pasture**

sire and stage of lactation (Lucas 2006, Soren 1999).

(ALF= 0.084, SS= 0.271 kg/v/d).

**to health promoting compounds**

intake, i.e., when plasma beta carotene exceeded 5 microg/ml.

**Figure 1.** Lucerne or silage feeding. Evolution in -tocopherol (●), all-*trans*-β-carotene (■), retinol (▲) and vitamin D3 (\*) in milk of dairy cows fed diets differing in ALF (solid lines) or SS (dashed lines). All groups were fed sorghum silage during a pre-experimental period of 6 wk. Means ± SEM for 5 cows per group are presented.

**Figure 2.** Oat pasture vs. TMR feeding. Concentration of -tocopherol (○), all-*trans*-β-carotene (), retinol (▲) and lutein (x) in milk-fat of dairy cows fed diets differing in the percentage of oat pasture. Linear tendency for -tocopherol and retinol (dashed lines) and all-*trans*-β-carotene and lutein (solid lines) are shown. All groups were fed corn silage during a pre-experimental period of 6 wk. Each point represents eight cows per treatment. Data were compiled from two separated experiments with comparable diets.

Vitamin A in milk is incorporated under the form of retinol and carotenoids. Among these, all-trans-β-carotene was the mayor form recovered followed by lutein, which was consistent with what is generally reported. Although lutein was the major carotenoid found in pasture, its incorporation rate into milk was 10-fold lower than the incorporation of all-trans carotene. The same observation has been reported by Calderón (2007) and Butler (2008). Recovery of these species of vitamin A in milk was directly related to their concentration in feed. Pearson correlation coefficients for all-trans--carotene and lutein in feed and milk were R=0.8323 and R=0.7521 respectively. Concerning the lipoperoxyl radical-scavenging activity of all-trans retinol it behaves as a more effective antioxidant at a low partial oxygen pressure, low retinol concentration and high radical flux (Tesoriere 1997)

In a revision article, Nozière and others (2006), have discussed that the extent of carotenoid degradation by microorganisms in the rumen remains uncertain because of the wide range of results, mostly on -carotene, from in vitro and in vivo studies. Whereas some authors reported no degradation (Dawson and Hemington, 1974; Cohen Fernandez and others, 1976a), others found moderate (10–25%; Davison and Seo, 1963; Potkanski and others,1974; Cohen Fernandez and others, 1976b; Mora and others, 1999) or higher -carotene disappearance (40– 55%; King and others, 1962). The carotenoid supplement form could explain discrepancies among experiments, because degradation rates were usually higher when carotenoids were supplied as purified products than when provided in forages. This hypothesis was recently confirmed in vitro at the INRA in a study where no apparent degradation was detectable for lutein provided in forage, whereas 50% of the initial amount disappeared when the same quantity of lutein was added as a pure commercially available source.

Vitamin E concentration ranged from 16.5 to 46 microg/g milk-fat for milk from silage or pasture diets respectively. Pasture values were even higher than concentrations reported by Al Mabruk (2004) for grass silage supplemented with vitamin E (22.26 microg/g milk fat). Calderón and others (2007) showed an increase from 8 to 14 microg/g milk fat depending on the proportions of experimental feed (75% grass silage + 25% lucerne protein concentrate) in the basal diet (from 0 to 66%).

Four different assays conducted in INTA (Argentina) indicate that after a shift from silage to pasture diet α-tocopherol augmented from 16.7 to 40.37 (70% grazed lucerne); from 11.59 to 26.05 and 28.53 (35 and 70% grazed lucerne pasture); 22.78 to 36.91 (80% grazed oat pasture) and 24.36 to 30.42 (75% oat pasture). In fact, Schingoethe and others (1978) reported a high variability in the concentrations of vitamin E in milk, depending on the grazing conditions. The concentration augmented from 20 to 50 microg/g milk fat at the time cows started grazing pasture. The same observation was reported by La Terra and others (2010) with an increase of 37% and 68% in -tocopherol for cows fed 30:70 and 70:30 ratio for pasture:TMR, respectively in comparison to TMR diet. Therefore, results from INTA experiments showed the same tendency. However, one experiment conducted in autumn 2007, showed no differences between maize silage or pasture (lucerne) fed cows with an average of 27.5 microg/g milk fat, thus indicating that the quality of the diet and the pasture affects the incorporation of vitamin E into milk.

Vitamin D may be consumed in the diet as either ergocalciferol (D2) from plant sources or cholecalciferol (D3) from animal sources. With the exception of fatty fish, relatively few foods are naturally rich in vitamin D. In mammalians with ample sunlight exposure, the greatest source is endogenous vitamin D produced when 7-dehydrocholesterol in the epidermis and dermis of the skin is converted into vitamin D3 after exposure to ultraviolet B radiation. Vitamins D2 and D3 from dietary sources are transported to the liver, and from there to the tissues. Vitamin D3 from skin is transported through plasma. Therefore more evidence is needed to understand the factors that induce its variation in milk content. Consequently, for vitamin D, the dairy industry proposed technologically vitamin D-enriched dairy products to consumers (Graulet 2010). The vitamin D3 endocrine system has mostly been studied for its role in calcium and phosphorus metabolism and its possible role as an antioxidant has been neglected. Sardar and others (1996) demonstrated that D3 treatment brought about similar reduction in the extent of lipid peroxidation and induction in superoxide dismutase (SOD) activity, as with vitamin E supplementation in rats.

350 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

pressure, low retinol concentration and high radical flux (Tesoriere 1997)

quantity of lutein was added as a pure commercially available source.

the basal diet (from 0 to 66%).

incorporation of vitamin E into milk.

Vitamin A in milk is incorporated under the form of retinol and carotenoids. Among these, all-trans-β-carotene was the mayor form recovered followed by lutein, which was consistent with what is generally reported. Although lutein was the major carotenoid found in pasture, its incorporation rate into milk was 10-fold lower than the incorporation of all-trans carotene. The same observation has been reported by Calderón (2007) and Butler (2008). Recovery of these species of vitamin A in milk was directly related to their concentration in feed. Pearson correlation coefficients for all-trans--carotene and lutein in feed and milk were R=0.8323 and R=0.7521 respectively. Concerning the lipoperoxyl radical-scavenging activity of all-trans retinol it behaves as a more effective antioxidant at a low partial oxygen

In a revision article, Nozière and others (2006), have discussed that the extent of carotenoid degradation by microorganisms in the rumen remains uncertain because of the wide range of results, mostly on -carotene, from in vitro and in vivo studies. Whereas some authors reported no degradation (Dawson and Hemington, 1974; Cohen Fernandez and others, 1976a), others found moderate (10–25%; Davison and Seo, 1963; Potkanski and others,1974; Cohen Fernandez and others, 1976b; Mora and others, 1999) or higher -carotene disappearance (40– 55%; King and others, 1962). The carotenoid supplement form could explain discrepancies among experiments, because degradation rates were usually higher when carotenoids were supplied as purified products than when provided in forages. This hypothesis was recently confirmed in vitro at the INRA in a study where no apparent degradation was detectable for lutein provided in forage, whereas 50% of the initial amount disappeared when the same

Vitamin E concentration ranged from 16.5 to 46 microg/g milk-fat for milk from silage or pasture diets respectively. Pasture values were even higher than concentrations reported by Al Mabruk (2004) for grass silage supplemented with vitamin E (22.26 microg/g milk fat). Calderón and others (2007) showed an increase from 8 to 14 microg/g milk fat depending on the proportions of experimental feed (75% grass silage + 25% lucerne protein concentrate) in

Four different assays conducted in INTA (Argentina) indicate that after a shift from silage to pasture diet α-tocopherol augmented from 16.7 to 40.37 (70% grazed lucerne); from 11.59 to 26.05 and 28.53 (35 and 70% grazed lucerne pasture); 22.78 to 36.91 (80% grazed oat pasture) and 24.36 to 30.42 (75% oat pasture). In fact, Schingoethe and others (1978) reported a high variability in the concentrations of vitamin E in milk, depending on the grazing conditions. The concentration augmented from 20 to 50 microg/g milk fat at the time cows started grazing pasture. The same observation was reported by La Terra and others (2010) with an increase of 37% and 68% in -tocopherol for cows fed 30:70 and 70:30 ratio for pasture:TMR, respectively in comparison to TMR diet. Therefore, results from INTA experiments showed the same tendency. However, one experiment conducted in autumn 2007, showed no differences between maize silage or pasture (lucerne) fed cows with an average of 27.5 microg/g milk fat, thus indicating that the quality of the diet and the pasture affects the The concentration of vitamin D3 showed different tendencies among experiments. In one experiment, it ranged from 5.851.21; 8.012.13 and 10.292.34 microg/g milk-fat for diets containing 0, 40 and 80% oat pasture respectively, showing a linear increase with the proportion of pasture in the diet. This observation agreed with the described in figure 1, although values were lower (average of 30 microg/g milk-fat, in figure 1 with 70% of lucerne pasture diet vs. 10 microg/g milk-fat, with an 80% oat pasture diet). In contrast, in a second experiment the concentrations were 0.80; 0.91; 1,98 and 1.34, without significant differences (P >0.05) for diets containing 0, 25, 50 and 75% of oat pasture. Therefore, a tendency for higher values of vitamin D3 with the increase of grazed pasture was observed but further assays are necessary to describe the productive parameters that may be used to improve the concentration of natural vitamin D3 in cow milk.

#### **3.3. Effect of increasing concentrate content on grazed pasture.**

The addition of concentrate on a pasture based feed was studied in two separated experiments: total feed assignment around 30 and 31.9 kg DM/head/day in experiments 1 and 2 respectively. Pasture consumption diminished with the increase of concentrate within the diet: 18.0; 16.1 and 14.2 kg DM/ head/day for 3.5; 7.0 and 10.5 kg of concentrate respectively in experiment 1 but not in experiment 2. In both cases, the addition of concentrate had no effect on the concentration of antioxidants in milk. The average concentrations were 5.34; 37.25; 1.09 and 6.51 microg/g milk fat for retinol, -tocopherol, tocopherol and -carotene respectively. These values resulted similar to those obtained with pasture proportions above 40% in the diet (figures 1 and 2).

Calderón et. al, 2007 demonstrated that the transfer of -carotene from plasma to milk reached a plateau when plasma levels exceeded 5 microg/ml and resulted a limiting factor in terms of the secretion of this antioxidant, that can be due to a limited uptake by the mammary gland or limited transport by binding to transport proteins like -lactoglobulin (Dufuor and Hartle, 1991) and/ or to saturation of milk fat globules.

It can be concluded that good quality pasture favored a linear increase of antioxidant vitamins in milk, and that this increment was not affected by the addition of concentrate in the feed.

#### **3.4. Functional fatty acids in milk**

There is a growing interest in CLA, considered to be beneficial in prevention of carcinogenesis (Ip and others, 1999), the n-6 to n-3 polyunsaturated fatty acids (PUFA) relationship, and the low percentage of saturated fatty acids, as these conditions are beneficial for the prevention of cardiovascular diseases. Their percentages in milk products can be increased through a suitable dietary regimen. Changes in milk fatty acids profile are possible due to the plasticity of milk fat (reviewed in Chillard and others 2000).

Dietary changes after shifting from silage to lucerne pasture diet induced significant changes in some fatty acids. Saturated fatty acids showed higher values in lucerne than in silage produced milk (P<0.05): C12 (2.79 ± 0,123 vs. 2,45 ± 0,132), C14 (10,79 ± 0,344 vs. 9,30 ± 0,359); C16 (29,63 ±0,617 vs. 26,26 ± 0,650); C17 (0,72 ± 0,012 vs. 0,64 ± 0,013) and C18 (8,84 ± 0,504 vs. 12,22 ± 0,563) respectively throughout the experiment. Conversely, silage produced milk showed higher values for C18:1c (21,00 ± 0,823 vs. 25,22 ± 0,840); C18:2c (2,63 ± 0,085 vs. 2,98 ± 0,098) and C18:2t (0,40 ± 0,029 0,15 ± 0,031) than lucerne produced milk. In addition, pasture diet favored the incorporation of C18:3 (1,03 ± 0,051 vs. 0,54 ± 0,054) into milk. In this experiment, C18:1t (3.80 ± 0,269 vs. 3.51 ± 0.285) and CLA (1.52 ± 0.096 vs.1.25 ± 0.107) values for lucerne and silage milk respectively showed similar results (P>0.05). This result was not expected as Castillo and others (2006) reported a positive association between proportion of lucerne pasture in the diet and content of cis9 trans11 CLA and C18:1t in milk under similar experimental conditions within the same geographical region.

A second experiment was conducted in INTA with increasing percentages of lucerne pasture. Twenty four Holstein cows from the experimental herd (6 heifers and 18 multiparous) in mid lactation, were randomly assigned to three different treatments. During a first pre-experimental period of 7 days, considered as co-variable, all cows were fed the same diet, in a second period each group was assigned to their treatment during 21 days and in the third period of 28 days, each group received the corresponding diet, with 0 (A0 or maize silage control diet), 35 (A35) and 70 % (A70) lucerne pasture. Diets were isoenergetic and isoproteic. Lucern pasture was cut daily and offered to the cows. Rations were fed as TMR (20.34 kg DM/cow/day, in average).

Milk production, protein and urea content showed lower values for lucerne groups compared with milk from silage-fed cows (P<0.05). For other parameters, milk from the three treatments showed a similar overall quality.

As mentioned above, the proportion of pasture in the diet influenced the composition of saturated fatty acids (SFA) in milk (table 8). C4, C15, C16 and C17 FAs were higher in A70 milk compared with A35 and A0 treatments (P < 0.05). C12 FA was higher in A0 than in A35 and A70 milk (P < 0.05). Total C18:2 FA was higher in A0 milk, but differences were observed for individual isomers. Trans-9, trans-12 C18:2 and cis-9, trans-12 C18:2 showed similar percentages in all milk samples. However, trans-9, cis-12 C18:2 increased with the higher content of lucerne in the diet (A70 compared with A35 and A0), and conversely, cis-9, cis-12 C18:2 decreased with an increase of lucerne (P < 0.05). These results differed from Schroeder and others (2003) that reported higher levels of trans-vaccenic and c9 t 11 CLA in milk from pasture (Avena sativa L.) fed cows compared to milk from TMR fed cows. The mean percentage of CLA in argentine milk from the central Region was reported to be 1.2 (Castillo and others, 2006; Páez and others, 2007). The nature of the differences with previous reports could rely on the quality of pasture and its highly variable composition, as discussed in the section above. The mean C18:3 n-3 FA content in lucerne milk was approximately 1.7 and 2.9 times higher (A35 and A70 respectively) than in silage milk (P< 0.01). In addition, C 20:5 n-3 showed an lucerne-dependant increase (A70> A35 > A0). Therefore, the incorporation of lucerne pasture in the diet was associated with a higher proportion of milk n-3 FA compared with maize silage diet. Indeed, the plot of C 18:2 vs. C 18:3 for milk samples showed a differential distribution of milk according to the diet and could therefore be used to differentiate the incorporation of lucerne into the diet of dairy cows (figure 3).

352 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

(Dufuor and Hartle, 1991) and/ or to saturation of milk fat globules.

**3.4. Functional fatty acids in milk**

TMR (20.34 kg DM/cow/day, in average).

three treatments showed a similar overall quality.

mammary gland or limited transport by binding to transport proteins like -lactoglobulin

It can be concluded that good quality pasture favored a linear increase of antioxidant vitamins in milk, and that this increment was not affected by the addition of concentrate in the feed.

There is a growing interest in CLA, considered to be beneficial in prevention of carcinogenesis (Ip and others, 1999), the n-6 to n-3 polyunsaturated fatty acids (PUFA) relationship, and the low percentage of saturated fatty acids, as these conditions are beneficial for the prevention of cardiovascular diseases. Their percentages in milk products can be increased through a suitable dietary regimen. Changes in milk fatty acids profile are

Dietary changes after shifting from silage to lucerne pasture diet induced significant changes in some fatty acids. Saturated fatty acids showed higher values in lucerne than in silage produced milk (P<0.05): C12 (2.79 ± 0,123 vs. 2,45 ± 0,132), C14 (10,79 ± 0,344 vs. 9,30 ± 0,359); C16 (29,63 ±0,617 vs. 26,26 ± 0,650); C17 (0,72 ± 0,012 vs. 0,64 ± 0,013) and C18 (8,84 ± 0,504 vs. 12,22 ± 0,563) respectively throughout the experiment. Conversely, silage produced milk showed higher values for C18:1c (21,00 ± 0,823 vs. 25,22 ± 0,840); C18:2c (2,63 ± 0,085 vs. 2,98 ± 0,098) and C18:2t (0,40 ± 0,029 0,15 ± 0,031) than lucerne produced milk. In addition, pasture diet favored the incorporation of C18:3 (1,03 ± 0,051 vs. 0,54 ± 0,054) into milk. In this experiment, C18:1t (3.80 ± 0,269 vs. 3.51 ± 0.285) and CLA (1.52 ± 0.096 vs.1.25 ± 0.107) values for lucerne and silage milk respectively showed similar results (P>0.05). This result was not expected as Castillo and others (2006) reported a positive association between proportion of lucerne pasture in the diet and content of cis9 trans11 CLA and C18:1t in milk

A second experiment was conducted in INTA with increasing percentages of lucerne pasture. Twenty four Holstein cows from the experimental herd (6 heifers and 18 multiparous) in mid lactation, were randomly assigned to three different treatments. During a first pre-experimental period of 7 days, considered as co-variable, all cows were fed the same diet, in a second period each group was assigned to their treatment during 21 days and in the third period of 28 days, each group received the corresponding diet, with 0 (A0 or maize silage control diet), 35 (A35) and 70 % (A70) lucerne pasture. Diets were isoenergetic and isoproteic. Lucern pasture was cut daily and offered to the cows. Rations were fed as

Milk production, protein and urea content showed lower values for lucerne groups compared with milk from silage-fed cows (P<0.05). For other parameters, milk from the

As mentioned above, the proportion of pasture in the diet influenced the composition of saturated fatty acids (SFA) in milk (table 8). C4, C15, C16 and C17 FAs were higher in A70

possible due to the plasticity of milk fat (reviewed in Chillard and others 2000).

under similar experimental conditions within the same geographical region.

This result is consistent with other results shown for grazed systems (Chillard and others 2001; Castillo and others 2006; La Terra and others 2010). The source of the variability remains to be further studied and strategies different that using pasture as the unique factor to improve trans- vaccenic acid and CLA in milk continue under experimentation.

Low percentages of CLA (around 0.34 %) was found to be associated to cows fed fermented roughage and concentrates (most intensive production farm) whereas high percentages (around 0.80 %) was found in the ecologically produced milk fat. The concentration of CLA correlated positively with and trans-vaccenic acid (Jahreis 1997). The high C18:3 content of young grass and its low fibre content interact to increase the production of CLA or its trans C18:1 precursors (Chillard and others 2000). The presence of lipid precursors is one of the factors described by Griinari and Bauman (1999), to improve CLA biosynthesis. The second factor are the changes associated with the microbial activity associated with ruminal biohydrogenation, which is incomplete and leads to an accumulation of trans vaccenic acid, the precursor of CLA biosynthesis. In addition, factors that regulate these patways as well as the activity of the Δ-9 desaturase in the mammary gland and its regulation are a matter of investigation.

CLA isomers exert different biological activities. Juárez and others (2010) have found that feeding 1068 IU vitamin E, reduced the total trans-18:1 content in backfat (P < 0.01), as well as the percentage of trans 10-18:1 (P < 0.001), which are related to an increased risk for cardiovascular diseases. On the other hand, trans 11-18:1 (vaccenic acid) the precursor for cis9, trans 11-18:2 (CLA), increased (P< 0.01). Vitamin E could, therefore, be used to decrease trans-18:1 in beef and improve its isomeric profile.

**Table 8.** Fatty acids profile in milk produced on 70 (A70), 35 (A35) and 0 (A0) percentage of Lucerne pasture.

**Figure 3.** Relationship between linoleic acid (C18:2 n6) and linolenic acid (C18:3 n3) in milk from cows fed diets with different contents of lucerne (0, 35 and 70% for A0, A35 and A70 respectively) Different letters indicate significant differences (P<0.05).

## **4.  Fatty acids profile in milk produced on 70 (A70), 35 (A35) and 0 (A0) percentage of Lucerne

*Fatty acids (FA) percentages*

pasture.

#### **4.1. Oxidative status in milk and milk powder**

Pasture feeding improved the antioxidant status in raw milk but also the peroxidative index at expenses of an increase of the n-3 fatty acids. However, feeding experiments carried out under the same production conditions showed quite dissimilar results depending on the seasonal variations.

Dry matter consumption differed between pasture and silage feeding regimes and also between Autumn and Spring. This feature is probably attributable to the differences between pasture qualities between assays. Total protein (TP) and butter fat (BF) were lower in pasture than in silage milk (3.38 vs. 3.71 % BF and 3.13 vs. 3.28 % TP respectively). -tocopherol and -carotene were significantly higher in pasture samples

(P<0.05), with a significant interaction between treatment and season for retinol and tocopherol, probably due to differences in pasture properties. -carotene was highly correlated with the proportion of lucerne in the diet, as shown in figure 2 for oat pasture. As a consequence of the increase of cartonoids, milk, milk powder and cheese had higher b\* values, which was traduced in a yellower fat colour that could differentiate dairy products from milk produced on pasture. Keenan and others., (1970) demonstrated that carotene was the major pigmented material encountered in bovine plasma membrane, and the plasma membrane origin of MFGM (milk globule fat membrane) would explain the origin of -carotene in milk, which reflects the dietary origin of this provitamin.

Gamma-tocopherol was the minor isomer of vitamin E in milk and dairy products. Its incorporation into milk was not influenced either by seasonal variation or by dietary treatment. The significance of this minor form of vitamin E incorporated in milk should be further stated. Its concentration remains at least one order below -tocopherol levels in vivo. However, its activity should be taken into account to prevent tissue damage. Particularly, tocopherol has the ability to protect against nitrogen-based free radicals, whilst -tocopherol cannot (Jiang and others, 2001).

Vitamin D3 was influenced by seasonal variation. The concentration in raw milk resulted 10-fold higher in spring than in autumn, probably due to degree of exposure to the sun which induces its biosynthesis in cows. Nevertheless, milk for human consumption is supplemented with 400 UI per quart (1 IU of vitamin D3 corresponds to 0.025 micrograms) to achieve the recommended mean intake of 5 micrograms per day (WHO). Natural vitamin D3 was not found in reggianito cheese.

Pasture feeding also modulated retinol content. Values were higher in pasture milk (p<0.05) with an interaction effect for seasonal variation. These differences were also found in milk powder and cheese, being enhanced in spring. . Therefore the susceptibility to peroxidation due to the number of double bonds in PUFAs in milk, increased with the proportion of pasture in the diet (table 9).

Oxidation in raw milk and milk powder was determined using the thiobarbituric acid reactive substances assay or TBARS, which is one of the oldest and most commonly used methods for assessing lipid oxidation in foods (Ladikos and others 1990).

This method is based on the spectrophotometric determination of extracted malonaldehyde, a minor product of oxidation, and can be performed either directly on the food product or on a steam distillate of the food. Due to the simple procedure and high correlation with sensory scores (Igene and others 1979), many studies have relied on TBARS for determination of oxidative status. The major disadvantage of the TBARS reaction is that it is not specific for malonaldehyde. No significant differences were found for TBARS (P>0.05), but this parameter showed consistently lower values in pasture milk, but was highly influenced by stationary variability.

Other method for the detection of intermediate peroxide compounds is the Peroxide Value. This parameter, as TBARS, was similar for all samples (data not shown).

Differential Characteristics of Milk Produced in Grazing Systems and Their Impact on Dairy Products 357

A0, A35 and A70: Diet with 0, 35 and 70% Lucerne pasture respectively.

M: Raw Milk; WMP: Whole Milk Powder; CH: Cheese (regianitto).

a: Thiobarbituric Reactive Substances

b: Relative Area Units

356 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

origin of -carotene in milk, which reflects the dietary origin of this provitamin.

cannot (Jiang and others, 2001).

D3 was not found in reggianito cheese.

pasture in the diet (table 9).

influenced by stationary variability.

(P<0.05), with a significant interaction between treatment and season for retinol and tocopherol, probably due to differences in pasture properties. -carotene was highly correlated with the proportion of lucerne in the diet, as shown in figure 2 for oat pasture. As a consequence of the increase of cartonoids, milk, milk powder and cheese had higher b\* values, which was traduced in a yellower fat colour that could differentiate dairy products from milk produced on pasture. Keenan and others., (1970) demonstrated that carotene was the major pigmented material encountered in bovine plasma membrane, and the plasma membrane origin of MFGM (milk globule fat membrane) would explain the

Gamma-tocopherol was the minor isomer of vitamin E in milk and dairy products. Its incorporation into milk was not influenced either by seasonal variation or by dietary treatment. The significance of this minor form of vitamin E incorporated in milk should be further stated. Its concentration remains at least one order below -tocopherol levels in vivo. However, its activity should be taken into account to prevent tissue damage. Particularly, tocopherol has the ability to protect against nitrogen-based free radicals, whilst -tocopherol

Vitamin D3 was influenced by seasonal variation. The concentration in raw milk resulted 10-fold higher in spring than in autumn, probably due to degree of exposure to the sun which induces its biosynthesis in cows. Nevertheless, milk for human consumption is supplemented with 400 UI per quart (1 IU of vitamin D3 corresponds to 0.025 micrograms) to achieve the recommended mean intake of 5 micrograms per day (WHO). Natural vitamin

Pasture feeding also modulated retinol content. Values were higher in pasture milk (p<0.05) with an interaction effect for seasonal variation. These differences were also found in milk powder and cheese, being enhanced in spring. . Therefore the susceptibility to peroxidation due to the number of double bonds in PUFAs in milk, increased with the proportion of

Oxidation in raw milk and milk powder was determined using the thiobarbituric acid reactive substances assay or TBARS, which is one of the oldest and most commonly used

This method is based on the spectrophotometric determination of extracted malonaldehyde, a minor product of oxidation, and can be performed either directly on the food product or on a steam distillate of the food. Due to the simple procedure and high correlation with sensory scores (Igene and others 1979), many studies have relied on TBARS for determination of oxidative status. The major disadvantage of the TBARS reaction is that it is not specific for malonaldehyde. No significant differences were found for TBARS (P>0.05), but this parameter showed consistently lower values in pasture milk, but was highly

Other method for the detection of intermediate peroxide compounds is the Peroxide Value.

methods for assessing lipid oxidation in foods (Ladikos and others 1990).

This parameter, as TBARS, was similar for all samples (data not shown).

c: Ferric Reducing antioxidant activity

d: A0, A35 and A70, feed treatments with 0, 35 and 70% Lucerne pasture

ND: not done; nd: not determined

L\*, a\*, b\*: CIELab Sysytem, D65 Illuminant and 10º geometry.

**Table 9.** Mean values of antioxidant vitamins, color parameters and oxidative stability indicators in two different seasons.

As indicated for feed components, numerous aldehydes are produced during oxidation, including octanal, nonanal, pentanal, and hexanal. Hexanal is the dominant aldehyde produced during oxidation (Dupuy and others 1987; Ajuyah and others 1993). It arises from both the 9 and 13 hydroperoxides of linoleate, and from other unsaturated aldehydes formed during the oxidation of linoleate (Shahidi and Pegg, 1994). It is a useful tool to assess lipid secondary oxidation products in milk (Erickson, 1999; Smet, 2009). This compound was higher in silage milk and could reflect a lower oxidative stability in milk and dairy products derived from silage-fed cows. Whole milk powder from pasture showed the same fatty acids profile as raw milk (table 9), and could constitute a natural source for n-3 fatty acids. In a similar way, antioxidants profile reproduced the composition of raw milk with estimated losses around 20 and 40% being higher for retinol and vitamin D3.

Higher natural antioxidants in whole milk powder lowered TBARS values in pasture (A70) samples compared to silage samples (A0) after 18-months shelf life assay (20ºC in sealed bilaminated plastic pouches under nitrogen atmosphere).

The total antioxidant activity was measured using the Ferric reduction assay (FRAP). Antioxidant compounds such as α-tocopherol, trolox, vitamin C, uric acid and billirubin, among others, are able to reduce ferric- to ferrous-tripyridyltriazine which develops a blue colour (Benzie and Strain 1996) with an adsorption maximum at 593 nm. Smet and others. (2009), demonstrated that FRAP and DPPH assays provide useful information about the oxidation process, particularly about the very early changes in the oxidative stability of milk. FRAP values for A0, A35 and A70 milk, were 332, 428 and 448 micromolar equivalents of Fe+2 respectively (P<0.05) and served as an indicator of the antioxidant capacity of milk. In milk powder, no differences were detected, thus indicated that some thermo labile antioxidants could be destroyed due to the processing of milk.

It can be concluded that there is not a unique method to describe the antioxidant capacity of biological samples. The complexity and diversity of mechanisms that contribute to the onset of oxidation and the mechanisms that counteract oxidative reactions involve multiple pathways. Also -tocopherol, -carotene and retinol contents were higher in pasture samples throughout the shelf life experiment.

#### **4.2. Instrumental sensory odour**

The electronic nose (E-nose) has been successfully applied to distinguish seasonal variations in whole milk powder (Biolatto and others., 2005). Also its application permitted the differentiation of virgin olive oil that showed different volatile production and vitamin E degradation patterns during the frying process (Messina and others., 2009).

The application of e-nose measurement to milk samples permitted to demonstrate that a dietary shift from silage to Lucern pasture, induced changes in the odour profile of the raw milk.

As shown in figure 4, at the beginning of the experiment (time 0), all samples were grouped differently than after turn into pasture. Also changes in the profile were detected at time 60 days, thus indicated an interaction between diet and time in the experiment, probably due to changes in the nature of the volatile compounds produced by Lucern plants as the experiment proceeded.

Data were analyzed using a Discriminant Function Analysis (DFA) and were discriminated with 86% of recognition of each sample within each group for the original cases.

A positive correlation was found for e-nose measurements and the content of different antioxidant vitamins in milk. Rossetti and others., 2010, showed than individual e-nose sensors correlated with the concentration of antioxidant vitamins in milk. The sensor called LY2/gCTI correlated positively with α-tocopherol, β-carotene and retinol, and negatively with γ-tocopherol (p<0.05). The sensor P30/1 correlated positively with retinol and the sensor P10/1 showed a positive correlation with γ-tocopherol (p<0.05).

Odour components have shown also to be transferred from raw milk to whole milk powder from cows fed 0, 35 and 70% Lucern pasture. When the value of each sensor was compared in both products, a linear relationship was found with a correlation coefficient of 0.9851 (P<0.05). In reggianito cheese, and after six months maturation, this relation was different (figure 5b), probably due to the development of odour compounds during the fermentation and maturation processes.

358 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

laminated plastic pouches under nitrogen atmosphere).

antioxidants could be destroyed due to the processing of milk.

samples throughout the shelf life experiment.

**4.2. Instrumental sensory odour**

experiment proceeded.

Higher natural antioxidants in whole milk powder lowered TBARS values in pasture (A70) samples compared to silage samples (A0) after 18-months shelf life assay (20ºC in sealed bi-

The total antioxidant activity was measured using the Ferric reduction assay (FRAP). Antioxidant compounds such as α-tocopherol, trolox, vitamin C, uric acid and billirubin, among others, are able to reduce ferric- to ferrous-tripyridyltriazine which develops a blue colour (Benzie and Strain 1996) with an adsorption maximum at 593 nm. Smet and others. (2009), demonstrated that FRAP and DPPH assays provide useful information about the oxidation process, particularly about the very early changes in the oxidative stability of milk. FRAP values for A0, A35 and A70 milk, were 332, 428 and 448 micromolar equivalents of Fe+2 respectively (P<0.05) and served as an indicator of the antioxidant capacity of milk. In milk powder, no differences were detected, thus indicated that some thermo labile

It can be concluded that there is not a unique method to describe the antioxidant capacity of biological samples. The complexity and diversity of mechanisms that contribute to the onset of oxidation and the mechanisms that counteract oxidative reactions involve multiple pathways. Also -tocopherol, -carotene and retinol contents were higher in pasture

The electronic nose (E-nose) has been successfully applied to distinguish seasonal variations in whole milk powder (Biolatto and others., 2005). Also its application permitted the differentiation of virgin olive oil that showed different volatile production and vitamin E

The application of e-nose measurement to milk samples permitted to demonstrate that a dietary shift from silage to Lucern pasture, induced changes in the odour profile of the raw milk.

As shown in figure 4, at the beginning of the experiment (time 0), all samples were grouped differently than after turn into pasture. Also changes in the profile were detected at time 60 days, thus indicated an interaction between diet and time in the experiment, probably due to changes in the nature of the volatile compounds produced by Lucern plants as the

Data were analyzed using a Discriminant Function Analysis (DFA) and were discriminated

A positive correlation was found for e-nose measurements and the content of different antioxidant vitamins in milk. Rossetti and others., 2010, showed than individual e-nose sensors correlated with the concentration of antioxidant vitamins in milk. The sensor called LY2/gCTI correlated positively with α-tocopherol, β-carotene and retinol, and negatively with γ-tocopherol (p<0.05). The sensor P30/1 correlated positively with retinol and the

with 86% of recognition of each sample within each group for the original cases.

sensor P10/1 showed a positive correlation with γ-tocopherol (p<0.05).

degradation patterns during the frying process (Messina and others., 2009).

**Figure 4.** Odor profile of ALF (70% Lucerne pasture diet) milk at 0 (time 0), 20 (time 20), 40 (time 40) and 60 (time 60) days after the shift from silage to pasture diet. Odor profile was determined in the head space of milk samples (3 samples per cow per time) with 3 mL of sample in a vial, incubated at 50ºC for 10 min with an agitation speed of 500 rpm (Autosampler HS100, Alpha MOS). For electronic nose analysis (Alpha Fox 4000, Alpha MOS), 1 mL of headspace sample was injected and the acquisition was obtained either with 18- semi-conductor oxide metallic sensors (MOS) or 4 mL of headspace was injected in a quadrupole mass spectrometer (Alpha Kronos, Alpha MOS) with an electronic impact (70 eV) ion source (Rossetti et al., 2010).

Further research will allow explaining the biochemical changes that occur during the transformation of milk from different productive origin; in this concern the use of an e-nose approach represents an alternative powerful tool to traditional methods of odour measurements.

**Figure 5.** Relationship between e-nose sensors values in raw milk and its derived whole milk powder or reggianito cheese sample. Each point represents the value for each individual sensor applied to raw milk, and the whole milk powder or reggianito cheese that derived from the same alaboration batch. 0, 35 and 70 indicates Lucerne pasture percentage in the diet. Aut: autumn; Spr: spring.

#### **4.3. Natural pigments in milk and cheese**

Consumer's perception for natural and functional dairy products is an actual concern. In a survey over 179 consulted consumers, 87 % answered that would associate functional and natural concepts. In addition, 47% associated cheese with functional dairy products (other choices were yogurt, milk powder, ice-cream and dietary supplements).

In this concern, the concentration of carotenoids, especially -carotene and lutein, could serve as natural pigment in milk fat and cheese.

360 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

measurements.

**Whole Milk Powder**

**Cheese**

Further research will allow explaining the biochemical changes that occur during the transformation of milk from different productive origin; in this concern the use of an e-nose approach represents an alternative powerful tool to traditional methods of odour

**E-nose (a)**

y = 0.9257x - 0.005

A0 Spr A35 Spr A70 Spr A0 Aut A35 Aut A70 Aut

A0 Spr A35 Spr A70 Spr A0 Aut A35 Aut A70 Aut

**Milk**

**E-nose (b)**

**Figure 5.** Relationship between e-nose sensors values in raw milk and its derived whole milk powder or reggianito cheese sample. Each point represents the value for each individual sensor applied to raw milk, and the whole milk powder or reggianito cheese that derived from the same alaboration batch. 0,

**Milk**

Consumer's perception for natural and functional dairy products is an actual concern. In a survey over 179 consulted consumers, 87 % answered that would associate functional and natural concepts. In addition, 47% associated cheese with functional dairy products (other

35 and 70 indicates Lucerne pasture percentage in the diet. Aut: autumn; Spr: spring.

choices were yogurt, milk powder, ice-cream and dietary supplements).

**4.3. Natural pigments in milk and cheese**

$$IV = \left(\frac{T450}{2} + T460 + T470 + T480 + T490 + T500 + T510 + T520 + \frac{T530}{2}\right) \text{\* 10}$$

where T is the translated value corresponding to the reflectance intensity at each wavelength. Results in figure 6 indicate a linear relationship between IV and -carotene. The -carotene content could be predicted in function of IV by the following expression:

$$Z\_{\beta-\text{contone}} = -0.11 \, + \, 0.895 \, Z\_{IV}$$

Where Z is the standardized value of the variables -carotene and IV ( <sup>2</sup> *i i x x <sup>Z</sup>* ).

This result indicates that the transformation of spectral data in the range of carotenoids, may be used as a representative index of the feeding system.

**Figure 6.** Correlation between IV and -carotene content. ALF: 70% grazed Lucerne (●) values in the region of SS: Sorghum Silage diet (○) samples corresponded to milk at the beginning of the assay (time 0)

Experimental data obtained with reggianito cheese, which was elaborated within the experimental pilot plant of INTA are shown in table 5. Colour parameters indicate differences in cheese attributable to feeding management. Pasture cheese from A35 and A70 treatments presented higher b\* values, which indicates a more yellow color, than A0. Differences were also distinguished by visual appreciation (figure 7). No significant differences were observed for L\* parameter.

**Figure 7.** Aspect of reggianito cheese elaborated with milk from A0, A35 and A70 (feeding treatments containing 0, 35 and 70% Lucern pasture respectively) after six-month maturation. Left to right: A0, A35 and A70 treatments.

## **5. Conclusions**

Productive strategies can be used to develop naturally added value dairy products. These strategies may be considered together with the consumer's perception about the role of functional and natural dairy products on human health. Preliminary studies on "pasture milk", clearly demonstrated the important changes in total lipids and fatty acid composition, especially in the contribution of C18:2 and C18:3 due to cultivar, cutting date and season. The importance of these fatty acids as substrate for CLA and n-3 PUFA concentration in milk lipids need to be considered.

The disadvantage of milk enriched with n-3 and CLA fatty acids is the possibility to suffer oxidation due to its high content of double-bonded molecules, which are prone to oxidation onset. This will not probably affect raw milk, but can bring inconvenient for the processing and commercialization of dairy product.

Therefore, pasture enhanced antioxidant status on raw milk, could be a strategy to solve part of the problem and consequently to avoid the development of rancidity and other off flavors and off odors in dairy products.

In addition, naturally pigmented cheese and milk products are positively accepted by consumers.

Pasture value added milk has an aspect which covers two purposes: preventing food spoilage and giving a fresh and healthier look to dairy products.

#### **Author details**

362 Milk Production – Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health

differences were observed for L\* parameter.

and A70 treatments.

**5. Conclusions**

concentration in milk lipids need to be considered.

and commercialization of dairy product.

Experimental data obtained with reggianito cheese, which was elaborated within the experimental pilot plant of INTA are shown in table 5. Colour parameters indicate differences in cheese attributable to feeding management. Pasture cheese from A35 and A70 treatments presented higher b\* values, which indicates a more yellow color, than A0. Differences were also distinguished by visual appreciation (figure 7). No significant

**Figure 7.** Aspect of reggianito cheese elaborated with milk from A0, A35 and A70 (feeding treatments containing 0, 35 and 70% Lucern pasture respectively) after six-month maturation. Left to right: A0, A35

Productive strategies can be used to develop naturally added value dairy products. These strategies may be considered together with the consumer's perception about the role of functional and natural dairy products on human health. Preliminary studies on "pasture milk", clearly demonstrated the important changes in total lipids and fatty acid composition, especially in the contribution of C18:2 and C18:3 due to cultivar, cutting date and season. The importance of these fatty acids as substrate for CLA and n-3 PUFA

The disadvantage of milk enriched with n-3 and CLA fatty acids is the possibility to suffer oxidation due to its high content of double-bonded molecules, which are prone to oxidation onset. This will not probably affect raw milk, but can bring inconvenient for the processing A.M. Descalzo, L.Rossetti, R. Páez, G. Grigioni, P.T. García, L. Costabel, L. Negri, L.Antonacci, E. Salado, G. Bretschneider, G. Gagliostro, E. Comerón and M.A. Taverna *Instituto Nacional de Tecnología Agropecuaria (INTA), Argentina*

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