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Citation of this paper

Characterization of dairy cattle feeding systems in Algeria: impact on productive and reproductive performance

A Abdelli and M Iguer-Ouda1

National veterinary institute, University of Blida-1, Algeria
1 Department of biology, University on Bejaïa, Algeria


The current study aimed to investigate the existing cattle feeding systems in Algeria and the impact of feeding management, particularly the concentrate allocation, on dairy cow milk yield and reproductive performance. The study was conducted during 6 months with a total of 240 cows in Tizi-Ouzou area (north of Algeria). According to the concentrate allocation, three feeding systems were defined: S1 with a constant concentrate allocation whatever the physiological stage, S2 with a variable quantity of concentrate allocated according the stage of lactation and S3 with concentrate allocation according to the stage of lactation and milk production level.


The results revealed that the milk production was lower than what allowed by the genetic potential associated to altered reproductive performances. Compared to S1 and S2, it was observed that cows in S3 presented significantly higher mean milk yield (17.7 ± 3.96 kg) and reproductive performance (pregnancy rate at first insemination (58.3 %), low days to first service (89.4 ± 26.6) and short open days (105.9 ± 31.7)]. In S3, 66.7 % of cows presented BCS values higher than 2.75, a percentage directly related to the highest success at first insemination in this feeding system.

Key Words: AI, BCS, energy intake, genetics, milk yield


During the last decades, genetic improvement in dairy cows in developed countries led to a dramatic increase in feeding level to cover the high demand of mammary gland (Butler and Smith 1989; Butler 2003). Furthermore, evidence suggests that selection for milk yield increases the gap between energy input and output during early lactation (Veerkamp 1998) as dry matter intake (DMI) has not been included in the breeding goals (Pryce et al 2001; Berry et al 2006). Thus, energy intake is primary the limiting factor on milk yield in high producing dairy cattle (Allen 2000), consequently, to maintain a high production level during early lactation, cows mobilize body reserves to provide non esterified fatty acids (NEFA) as an energy fuel (Drackley et al 2001; Kokkonen et al 2005). This results to a negative energy balance (NEB) (Ingvartsen et al 2003) with subsequent repercussions on fertility (De Feu et al 2009), health status (Bobe et al 2004) and milk production (Reist et al 2003). It’s well established that NEB may be influenced by genetic merit for milk yield (Yániz et al 2008) and by the energy level of the offered feed (Cowan 1982). In this respect, feeding strategies are directed to increase energy intake and to avoid excessive mobilization of body reserves during the post-partum (Butler et Smith 1989). In this respect, body condition scores (BCS) is proposed as an effective managerial tool in evaluating adaptation to NEB (Coffey et al 2001; Alapati et al 2010). BCS is used particularly to investigate the relationship between NEB and far measures of productive and reproductive performance. In fact, important management decisions concerning feeding during the dry period (Santos 2001), transition period (Kokkonen et al 2004) and early lactation (Butler 2005) are carried out on the basis of BCS values. 


Algerian development policy in dairy production is based on a massive introduction from Europe of cattle breeds with high genetic milk yield potential (Madani et al 2008). Nevertheless, it is established that dietary forage is not offered ad libitum (Kadi et al 2007a), caused by the deficit of fodder area (Houmani 1999) and consequently it is observed an increase in concentrate diet energy (Madani et al 2004; Kadi et al 2007b). This lack in feeding resources seems to be the limiting factor for adaptation of European breeds (Madani et al 2008) with deficiencies in milk production, the better annual average of milk yield is not exceeding 4 700 kg (Kadi et al 2007a). Likewise, previous studies in Algeria have reported a low reproductive performance (Ghorbi et al 2015) expressed in terms of low pregnancy rate at first service (Miroud et al, 2014) and long parturition to AI intervals (Mouffok et al 2007; Madani et al 2008). However, in developing countries, particularly Algeria, little is known concerning the impact of the existing feeding systems on milk production and reproductive performance.


On the basis of the presented background, the current study aimed on one hand to explore the existing feeding systems, particularly the concentrate allocation strategies and on the other hand to investigate the impact of these systems on milk yield and reproductive performance.

Material and methods

Farms data collection


The study was conducted from January to June and included data from 240 primiparous and multiparous purebred or crossbred Holstein–Friesian, Montbéliarde and Fleckvieh dairy cows. Each herd was visited fortnightly. During the farm visits, all demographic events (entry, mortality, culling), production data, reproductive events (calving, insemination, pregnancy diagnosis, drying-off) and diseases occurred during the two previous weeks were recorded. Once a month, investigations were carried out in each farm to reconstitute the forage calendar and the concentrate distribution. The follow-up of the distributed rations was based on weighing the concentrate and measuring forge biomass. All cows were stall fed in a zero grazing system except during the periods where cows had access to spring pasture (April to June). Forage was cut, chopped and fed to cows in feeding troughs. The energetic values were estimated by using feed table values (INRA  2007).


Animal measurements and body condition scores


Body weight was measured monthly by the same technician. Theoretical energy balance (TEB) was estimated as the difference between net energy distributed and the sum of net energy requirements for maintenance, pregnancy, and milk production, using the French net energy system (INRA 2007). This system uses “unité fourragère lait” (UFL) as the unit of net energy equivalent to 1 kg of standard air-dried barley (1 700 Kcal). Cows were milked twice a day and milk yields were measured at each milking. Expected milk yield (milk yield allowed by diet energy) was estimated as the difference between net energy distributed and the sum of net energy requirements for maintenance and pregnancy divided by 0.44 (INRA 2007). Potential milk yield was estimated according to the prediction equation of INRA (2007). A body condition score (BCS) was used to assess the individual level of body fatness (Edmonson et al 1989), a scale of 1 (emaciated) to 5 (obese) was used to score each cow using half-point increments. All BCS measurements were done by a trained single operator. Body condition at first service and at calving was determined as the monthly closest BCS to calving or first insemination.


The following equations were used to determine the energy required for maintenance (equation 1), pregnancy (equation 2) milk output (equation 3) and theoretical energy balance (equation 4):

Energy required for maintenance (NEM)


NEM = 0.041×BW0.75×Aact with Aact:  Activity allowance (Aact= 1 in tie stalls, 1.1 in free stalls) (equation 1) ;


Energy requirement for pregnancy (NEP) assuming a calf weight of 40 kg: 


(NEP40) = 0. 0288× e0.116× weekP with week p: week of pregnancy (equation 2) ;


Energy requirement for milk production with 4% of fat content (NEL4%) = 0.44× kg of milk produced (equation 3) ;


Theoretical energy balance


TEB (UFL) =NE-NEL4%-NEM-NEP40 with NE: Theoretical energy intake (UFL) and NEL4% : Energy requirement for milk production with4% of fat content (equation 4).


Statistical analyses


All the data editing and statistical analyses were performed in Statistical Analysis Systems, Statview®, version 5.0 (SAS Institute Inc, 1998). Quantitative variables were analyzed by ANOVA using a one-way factorial design.  Qualitative variables were analyzed by the chi-square test. For all tests, p values <0.05 were considered significant.


According to the concentrate allocation strategy, three feeding systems were observed (Table 1):


S1: a constant quantity of concentrate was allocated whatever the physiological stage of the animal;

S2: the cows received the concentrate according to the physiological stage (lactation, drying up, i.e., dry cows fed separately from milking ones);

S3: the cows received the concentrate according to stage of lactation (early lactation, mid-lactation, late lactation, drying up).


The number of cows per fodder area was 1.7 cow/ha, S2 presented the highest area (125 ha) with a small number of cows per fodder 1.2. Feeding was not distributed ad libitum in relation to the high cost and the unavailability of the food (forage and concentrate). In winter, cows were generally given barley silage, oats haylage, sorghum and dry hay. The summer ration was composed of spontaneous herbs, oats and ryegrass. The concentrate contained ban, maize, barley, soya hulls with mineral supplement. The animals were milked twice a day and fed three to four times.


The concentrate energy represented more than 61% of the distributed ration. The mean total energetic diet was 9.22 UFL/cow. S3 appeared to provide the highest energetic diet compared to the other systems (10.4 UFL, 9.89 UFL and 8.77 UFL in S3, S1 and S2, respectively).

Table 1. Characteristics of the three feeding systems S1, S2 and S3

Feeding system










Fodder area (ha)





Number of cows per fodder area (cow/ha)





Total energetic diet (UFL*)

9.89 ± 0.00

8.77 ± 1.29




Proportion of the concentrate in the diet, UFLc**/UFLtotal*** (%)

64.7 ± 00.0

61.1 ± 16.9

55.0 ± 11.4

61.4 ± 14.3

* UFL = unité fourragère lait ; 1 UFL = 1700 kcal of NEL (INRA 2007) ;
** UFLc = concentrate energy ;
*** UFLtotal = total distributed energy.

Milk yields


When analyzing the all three systems together, a significant difference (p<0.001) was observed between the potential daily milk yield (26.8 ± 7.9 kg) on one hand (calculated by using the prediction equation of INRA, 2007) and the expected (12.0 ± 4.9 kg) and the actual daily milk yield (13.3 ± 3.1 kg) on the other hand (Figure 1). However, there was no significant difference (P>0.05) between expected and actual milk yield.

Figure 1. Potential milk yield, expected milk yield, and actual milk yield (kg) of dairy cows.

Production performances and TEB of the feeding systems are summarized in Table 2. No significant difference (P>0.05) was observed concerning the potential milk yield between the feeding systems. However, a significant difference (P<0.05) was recorded concerning actual milk yield and the expected milk yield when comparing the different feeding systems. In S3 it was also observed the highest TEB (P < 0.001) with the lowest ratio potential milk yield / actual milk yield (P < 0.001). Furthermore, S3 presented a narrow TEB heterogeneity (figure 2) compared to S1 and S2. A positive correlation was recorded between milk yield and diet energy (r = 0.33, p<0.001).

Table 2. Production performances and theoretical energy balance (TEB) in the feeding systems


Potential milk
yield* (Kg)

Actual milk
yield (Kg)

Expected milk
yield (Kg)

Potential milk yield
/actual milk yield

Theoretical energy
balance (UFL)


27.4 ± 7.7a

13.8 ± 4.5a

11.3 ± 1.0a

2.37 ± 1.17a

1.12 ± 3.39a


26.3 ± 8.7a

11.1 ± 4.6b

13.3 ± 1.1b

2.63 ± 1.25a

1.61 ± 2.67a


27.4 ± 2.0a

17.7 ± 4.0c

17.6 ± 7.0c

1.59 ± 0.38b

4.33 ± 1.49b

* Potential milk yield was calculated by using the prediction equation of INRA (2007)
a, b, c values of the same variable with the same superscript are not significantly different

Figure 2. Box plot diagram representing theoretical energy balance (TEB) in the feeding systems.
100% of cows in S3 presented a positive TEB, 79% in S2 and 59% in S1.

The mean BCS at calving in the three systems was 3.39 ± 0.46 with 2.69 ± 0.68 at the first insemination (Table 3). The three systems all included presented 1.44 ± 0.63 points losses in the first 60 days of postpartum. S3 was the feeding system showing cows with the highest BCS at first insemination (66.7 %). A significant BCS loss was recorded during 60 days postpartum in S1 with a significant level (P<0.05) of cows with high BCS loss (87.5%).

Table 3. BCS at calving, BCS loss, BCS at first insemination, percentage of cows with a high BSC loss (>1) and percentage of cows with a high BCS at first insemination (≥2.75) in the three feeding systems (S1, S2 and S3)


BCS at

BCS loss*

BCS at first

% of High BCS
loss** (>1)

% of high BCS at first
insemination ≥2.75


3.55 ± 0.64

1.87 ± 0.64a

2.59 ± 0.61ab




3.32 ± 0.38

1.39 ± 0.52ab

2.53 ± 0.80a




3.43 ± 0.45

1.00 ± 0.55b

3.04 ± 0.52b



* Change in condition score from calving to 4 weeks post-calving;
** Change in condition score from calving to 4 weeks post-calving higher than 1 point.

Table 4 represents the effect concentrate feeding systems on BCS at first insemination and the impact of BCS loss on first insemination success, days to first service and open days. The pregnancy rate at first insemination, days to first service and open days were in the three systems 34.8 %, 115.4 ± 73.6 days and 181.8 ± 114.6 days, respectively. The results showed a significant difference (P<0.05) in days to first service and open days between S1 and the two other systems. The results showed an independence of days to first service and open days to the level of BCS at first insemination. However, pregnancy rate at first insemination was tightly related to the level of BCS at insemination and to the feeding systems.

Table 4. The effect of concentrate feeding systems and BCS at first insemination on success at first insemination, days to first service and open days

Success at first
insemination (%)

Days to
first service

Open days

Feeding system



205.7 ± 98.0a

277.6 ± 129.8 a




140.1 ± 70.9b




105.9 ± 31.7b


BCS at first insemination (n=48)




107.0 ± 22.3a




119.7 ± 70.2a

a, b, c values of the same variable with the same superscript are not significantly different.

Figure N°3 shows the calving interval (CI) in the three feeding systems. The mean CI was 466 ± 114 days. In S1, more than 50% of the cows presented an interval higher than 562 days and only 25% of the cows have an interval lower than 450 days. In S3, only 25% of the cows showed an interval higher than 400 days with 50% with an interval lower than 349 days. In S2, 50% of the cows presented an interval lower than 406 days and 25% with an interval higher than 480 days.

Figure 3. Box plot diagram showing calving interval (days) in the three feeding systems, dots represent
values that are more extreme than the whisker values. In S3, all cows presented calving interval
between 346 and 447, in S2 between 335 and 581 and in S1 between 339 and 913 days.

The BCS mean at first insemination was 2.67.  More than 50% of cows with a BCS higher than 3 conceived at the first insemination.  The cows failed to conceive at the first insemination presented in 50% of the case a BCS lower than 2.5, and only 25% presented a BCS higher than 3 (Figure 4). When analyzing the rate of success at the first insemination in each feeding system (Figure 5), a perfect superposition was observed according to BCS. The highest success at first insemination (58.3 %) was recorded in S3. In this system it was noticed that the inseminated cows presented the highest BCS (66.7 % of the cows had a BCS higher than 2.75). In S2 and S1, lower success rates were recorded (25.7 and 35.3% respectively) with only 42.0 and 43.7 % of the cows showing BCS higher than 2.75.

Figure 4. Box plot diagram showing BCS values according to the success at the first insemination.

Figure 5. Sector diagram representing the percentage of success at the first insemination (left panel) and the
percentage of BCS (right panel, by considering 2.75 as a cutoff value) in the three feeding systems.


The current study aimed to investigate the existing feeding systems in Algerian dairy farms and the consequent impact on productive and reproductive performance. The results showed that diets were mainly hay and the concentrate exceeded 64 % with a mean of 61 % of the total provided energy. Previously, a percentage of 56 % was reported by Ouakli and Yakhlef (2003); this situation is caused by the average quality of forage but also by the small consumed quantities due generally to the weakness of fodder area and to the limited use of the irrigation (Kadi et al 2007a). In this respect, Benyoucef et al (1999) reported that cultivated forages and spontaneous herbs in Algeria represent only 25 % of the total feeding. This high concentrate percentage may induce physiological disorders compromising thus the optimal production. In fact, Wolter (1992) recommended a maximum of 40 % of fermentable glucids, beyond this proportion, the risk of metabolic disorders is significantly increased (Østergaard et Grohn 2000). Furthermore, digestibility depression is usually greater in high concentrate diets (Mulligan et al 2002).


The results showed a significant difference between the potential and the real milk yield (Figure 1); however no difference was observed between the expected milk yield allowed by the diet ration and the milk yield. This indicates that the dairy production is noticeably limited by the distributed ration. The impact of interaction between genotype and environment on production performance in dairy cattle was previously described in different studies (Kolver et al 2002; Vetharaniam et al 2003; Bryant et al 2005). The milk production of a particular genotype varies under different environmental conditions such as feeding levels or feeding systems (Bryant et al 2005). In Algeria, it is well known that cows produce less milk compared to cows of the same breed in developed countries, Madani and Far (2002) reported that the milk mean production is 3 173 kg compared to 7 285 kg in the same breeds in tempered countries.


The feeding system S3 recorded a higher milk production level compared to the other systems (P < 0.001) with an average of 17.7 kg. In addition, in this system, the real milk yield was closer to the potential milk yield expressed through the lowest potential milk yield / milk yield ratio (1.59 compared to 2.37 and 2.63 in systems S1 and S2). S3 was characterized by the distribution of the concentrate according the lactation stage by grouping cows in relation to their nutrient requirements.


In the three feeding systems, BCS at calving was 3.39 ± 0.46 (mean± standard deviation), values closer to those reported by Mouffok et al, (2013) (3.40 ± 0.05). The BCS loss during the first 60 days postpartum was higher (1.44 ± 0.63) than what recommended in the literature (Butler, 2005). Cows in this study did not differ significantly in terms of BCS at calving (p>0.05), there was, however, a significant difference (P < 0.05) in BCS at first insemination particularly between S3 and S1. Furthermore, in S3 we recorded the lowest percentage of cows with BCS loss higher than 1 and a greater percentage of cows with BCS higher than 2.75 at first insemination. In postpartum, the extent of negative energy balance is expressed through the degree of BCS loss, the difference among individual cows is explained largely by differences in energy intake rather than milk yield (Villa-Godoy et al 1988). Thus, cows with a higher average dry matter intakes (Veerkamp et al 995), present less severe negative energy balance and tended to mobilize less body tissue in early lactation (Berry et al 2006). However, Veerkamp et al, (1995) failed to report any significant effect of diet on average BCS during the first 26 weeks of lactation.


The overall average of reproduction parameters showed that the reproduction performances were deficient compared to the usual standards and the management objectives. Pregnancy rate at first insemination was 34.4 %, and days to first service and open days were 115 and 181, respectively. Similar results were reported previously in recent studies (Bouzebda et al 2008; Miroud et al 2014). The current results showed that there was a significant effect of feeding systems on pregnancy rate at first insemination, days to first service and on the open days. In the system S3, where the concentrate is distributed according to the stage of lactation and level production allowed to optimize the reproduction performances (58.3 % cows conceive at first insemination and all the cows presented a calving interval inferior to 430 days.

Following parturition, it is constant for dairy cows to present a negative energy period and a body condition loss to support milk production compromising consequently reproductive performance (Heuer et al, 1999). Alternative management strategies during the prepartum (dry) and early postpartum periods may ameliorate this loss (Ingvartsen et al 2001; De Feu et al 2009). In this respect, Philipot et al (2001) reported that individualization of concentrate distribution has a positive effect on reproduction performances.


In the current study, the pregnancy rate at first insemination was greater (P<0.05) in cows with BCS at first insemination ≥2.7. Similarly, a large survey study found that cows with a high BCS at insemination were most likely to become pregnant (Loefer et al 1999). However, the results showed that days to first service and open days were independent to BCS higher than 2.75 at first insemination. In fact, it is demonstrated that these two indicators are influenced by cow fertility as well as by other herd management factors like heat detection and the length of the voluntary waiting period (Grummer 2007).


Larger losses in body condition score (BCS) during the first postpartum month was associated with the reduction in conception rate (Domecq et al 1997). In fact, cows in S1 receiving the concentrate independently to the physiologic and lactation stages presented an important percentage of cows with a high BCS loss (>1) and a significant loss of BCS during the 60 first days postpartum (1.87) compared to cows receiving the concentrate according to the physiologic stage (S2 =1.39) and lactation stage (S3 = 1.00). The deficiency in energy generally involves a poor BCS (Philipot et al 2001; Ben Salem et al 2006) and impacts the heats manifestation and causes the failures at artificial insemination. Furthermore, Jorritsma et al (2004) observed that undernutrition which is characterized by a high concentration of NEFA, reduces in vitro proliferation of granulosa cells, delayed oocyte maturation and impaired blastocyst production.


Additionally, it is shown that cows losing one unit of BCS or more during early lactation express greatest risk to develop low fertility with conception rates of 17–38%.   Guidelines from previous studies indicated that cows with marked losses in BCS (≥1.25 unit) were only half as likely to conceive at first AI (Gillund et al 2001) and the conception rate increases 10% for every unit increase in BCS (Stevenson et al 1999).



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Received 11 July 2016; Accepted 6 October 2016; Published 1 January 2017

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