| Livestock Research for Rural Development 29 (8) 2017 | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
The objectives of the present study were to evaluate the reproductive parameters of 200 Holstein dairy cows, to study some factors that may influence these parameters and to characterize the metabolic profile of repeat breeder cows (RBC). The experiment was carried out on a private farm located in the North of Algeria. Fertility was estimated for each cow by calculating the parameters: Calving-First service interval (CFSI), calving-conception interval (CCI), calving-calving interval (CI), first service - conception rate (FSCR) and fertility index (FI). Parity, dairy production and BCS were used as covariate to determine their effect on CCI and FSCR dependent variables. Blood serum parameters such as glucose, total protein, triglycerides, cholesterol, urea and B-hydroxybutyrate were assayed by spectrophotometry. Insulin and progesterone serum levels were detected by immunological tests.
There was a high CS1 interval. Also, 44% of animals had a prolonged CCI (>110 days) and the FSCR was estimated at 42%, below the recommended values (>60%). RBC were noted in 35% of the total, higher than the intended standard (< 15%). FSCR and CCI were significantly influenced by parity and BCS (P <0.001). No significant milk yield effect on FSCR were reported (P> 0.05) but CCI was significantly related to this parameter (P <0.001). RBC were characterized by low levels of insulin and high urea concentrations compared to fertile females (P <0.001). In the conclusion, the reproduction parameters obtained in this study were very far from the standard objectives recommended for effective management of reproduction. It can be due to bad heat detections or/and undernutrition. These parameters were influenced by parity, BCS and milk production. Concentrations of insulin and urea can have some effects on reproductive problems such as repeat breeding and they help to understand potential mechanisms impairing fertility in RBC.
Key words: Algeria, dairy cow, Holstein, infertility, insulin, urea
Reproductive performances of dairy cattle are one of the main preoccupations of breeders and their technical supervisors, since they tend to decrease year by year among dairy farms around the world (Enjalbert 1998, Diskin and Morris 2008). Ideally, the calving interval recommended is 12-13 months to make the animal economically viable (Vanholder et al 2005, Yousefdoodt et al 2012). However, the evolution of this parameter shows a clear degradation, which necessarily leads to an increase in farm expenses: economic costs of additional inseminations, time lost due to insemination failures, or culling of animals with reduced performances. These poor results can be related to the deterioration of fertility which is particularly noted in Prim'Holstein animals (Opsomer et al 1999). This may be the result of genetic improvement for dairy production and changes in husbandry conditions (Lucy 2001).
According to different epidemiological studies, the most common factors associated with fertility problems are: heat detection, inadequate synchronization between insemination and ovulation, inadequate luteal function, artificial insemination (AI) technique, feeding problems, follicular cysts, endometritis, heat stress, uterine involution delay and infectious agents (Kenny et al 2002). Other epidemiological studies have suggested that pathological factors (mastitis, placental retention, ovarian cysts) have a greater effect on fertility compared to non-pathological factors such as BCS (Body Condition Scoring) and milk production (Lucy 2001). Most reproductive studies use reproductive parameters such as number of open days or calving to conception interval (CCI), first service conception rate (FSCR), calving to first service interval (CFSI), and number of AI per conception resulting in calving-calving interval (CI), as dependent variables to measure the effect of selected parameters.
In Algeria, the State has intensified the genetic improvement of bovine livestock by encouraging the use of the National Center for AI and Genetic Improvement (CNIAAG) in 1988. Despite the concerted effort, the national average milk production (<15 liters/day) remains below expectations (Souames et al 2014) and Algeria is still among the first importers of milk in the world and is the first in the Maghreb (Ghoribi et al 2012). Therefore, the objectives of this current study are to assess the reproductive performances of dairy cows in the North of Algeria, to determine the factors that influence fertility and fecundity of these animals and to evaluate the metabolic profile of infertile cows (repeat breeder cows: RBC).
The current study was performed on 200 Holstein dairy cows selected from a private farm located in the region of Mitidja (in the North of Algeria). This region is characterized by a continental climate, cold in winter (the temperature can fall to -4 ° C), and warm in summer (the temperature can reach 40 ° C). The study was conducted between 3 august 2016 and 5 march 2017. The housing is semi-extensive and reproduction is provided by AI. The method of heat detection was a visual observation during 20 to 30 minutes, made 3 times/day. All data related to the heat detection were recorded and were systematically monitored by the staff. Ultrasound examination was performed to confirm the pregnancy diagnostic 30 days after AI by the veterinary practitioner. The cows were fed from forage and concentrate. The latter was produced on the farm and was distributed at a rate of 8 kg/dairy cow/day, to increase milk production.
The fertility was estimated for each cow by calculating the parameters: Calving-First service interval (CFSI), calving-conception interval (CCI), calving-calving interval (CI), first service conception rate (FSCR) and fertility index (FI).
We were interested in FSCR and CCI, which are dependent variables that can be influenced by different independent variables such as: parity, dairy production and BCS.
Body condition scoring (BCS) was evaluated at the time of AI according to Edmonson et al (1989), giving a note between 0 and 5.
In the present study, 40 animals have been selected for biochemical and hormonal analyses. They were divided into two groups: group 1 was constituted by fertile cows (FC) (20 animals characterized by conception success at AI1 without any fertility problems). Group 2 represented by repeat breeder cows (RBC) (20 females needing more than three AI to conceive).
Blood samples from each animal were collected by jugular venipuncture. Collection of all samples was performed before feeding (except for BHB, 1-4 hours after feeding). After collection, the blood serum was separated from the coagulated blood by centrifugation (3000 rpm/20 minutes) and stored at - 20°C until analysis.
Blood serum parameters such as glucose, total protein, triglycerides, cholesterol, urea and B-hydroxybutyrate (BHB) were assayed by spectrophotometry on automated clinical chemistry analyser Architect plus, ci 4100 (Architect c Systems, Abbott Diagnostics, Germany). Insulin serum measurement was performed on another analyzer Cobas e411 by electrochemiluminescence (Roche Diagnostics GmbH, Germany). The minimum detection limit was 0.2 µU / ml.
Assessment of serum progesterone (P4) was determined at the time of AI on Architect plus, ci 4100 by competitive immunoassay using chemiluminescence technology. According to the manufacturer, the minimum detection limit is 0.1 ng/ml. A serum cut-off value of 1 ng/ml was considered to distinguish between follicular and luteal phase, as reported by Ginther et al (2013).
Statistical analysis was performed using the Statistica software (Version 10, Stat Soft France, 2003). To analyze the collected data, an analysis of variance procedure was used. Statistical differences in the concentrations of metabolic parameters between FC and RBC were carried out using Student’s t-test. Chi-squared test were used to analyze the effect of parity, BCS and mean milk yield on FSCR and CCI parameters. Parity, BCS and mean milk yield were considered as covariate. Data were expressed as percentage or mean ± standard deviation. The results were considered significant when P < 0.05.
Calving-first service interval (CFSI)
Table 1 presents data about CFSI parameter.
|
Table 1. Calving to first service interval (total n = 200) |
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|
CFSI, days |
|||
|
<45 |
45-70 |
>70 |
|
|
n |
36 |
44 |
120 |
|
% |
18 |
22 |
60 |
|
p |
<0.001 |
||
|
n: number of cows |
|||
According to this table, CFSI was greater than the normal values, since only 44 cows (22%) were inseminated between 45 and 70 days PP, the optimal period described by Wattiaux (2006). Similarly, 18% of the cows were inseminated before 45 days PP. These early inseminations can lead to high failure conception rates (Cauty and Perreau 2003). Furthermore, 60% of animals had CFSI greater than 70 days which is considered abnormal. Insemination carried out after 70 days must be justified: are they linked to a voluntary policy to organize calvings by the staff or, on the contrary, to a problem of heat detection or health problems (acidosis, metritis, ovarian cysts) as reported by Cauty and Perreau (2003). In this farm, the prolongation of CFSI would be directly related to the estrous detection problem, in agreement with the results of Wattiaux (2006). However, CFSI may also be dependent on prolonged anoestrus post-partum due to undernutrition and long period of negative energy balance (Enjalbert, 2000). Similarly, cows with low BCS at conception (less than 2.5) usually present longer CFSI than cows with normal BCS (Haresign, 1981).
Data obtained are presented in table 2.
The objective intended is an interval less than 110 days as described by Cauty and Perreau (2003). Our results (Table 2) showed that 56% of cows had a standard CCI and 44% had a prolonged CCI (greater than 110 days).
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Table 2. Calving-conception interval (CCI) (total n = 200) |
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|
CCI, days |
||
|
<110 |
>110 |
|
|
n |
112 |
88 |
|
% |
56 |
44 |
|
p |
<0.001 |
|
|
n: number of cows |
||
At the individual level, a cow is classified as functionally infertile when CCI is greater than 110 days (Bonnes 2005). In dairy cattle, each day of delay when the animal is still not fertilized at 90 days PP represents a non-negligible loss for the breeder (Jactel 1990). This is why the staff may provide specific information on the reproduction failure in these cows (Ghoribi et al 2012). According to Wattiaux (2006), the extension of CCI would be directly related to bad heat detection and to early or late inseminations. Similarly, Haresign (1981) and Enjalbert (2000) reported that cows with low BCS (which is necessarily related to feeding) usually showed long CCI. Indeed, different authors have reported the negative effect of reproductive pathologies (placental retention, metritis, ovarian cysts) (Loisel 1978, Steffan and Humblot 1985) and increased milk production (Hagman et al 1991) on this parameter. Our data are in agreement with those reported by Ghoribi et al 2012.
Table 3 presents data about CI.
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Table 3. Calving interval results (total n = 200) |
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|
CI, days |
|||
|
<400 |
400-500 |
>500 |
|
|
n |
124 |
54 |
22 |
|
% |
62 |
27 |
11 |
|
p |
<0.001 |
||
|
n: number of cows |
|||
In the current study, 62% had CI less than 400 days, which is considered normal according to Wattiaux (2006). Whereas 27% of animals presented CI between 400 and 500 days, and 11% had CI more than 500 days. These results showed fertility disruption (Khangmate et al 2000) when 30% of cows had CI greater than 420 days. Loisel (1978) reported that delayed fertility or more precisely infertility is characterized by the following abnormalities: intense or weak heats, elongated or shortened cycles, abortion, metritis, presence of ovarian cysts and persistent corpus luteum. These manifestations are the result of often infectious disease, malfunctioning of reproductive organs or undernutrition (Enjalbert 2000). Prolonged CCI is accompanied by an equivalent elongation of CI as noted by Ethrington et al (1985).
In the total of 200 cows, FSCR was estimated at 42% (84 cows), which is less than the standard reported by Seegers and Malher (1996) (>60%). The rates recorded were higher than those reported by Ghoribi (2000) and Bouzebda et al (2006) who were 20 to 24% and from 29 to 31% and less than those noted by Ghoribi et al (2012). The FSCR would be directly related to good heat detection. The easy method is to provoke the heat and to ensure that the cows receive a balanced diet as recommended by Lamb (1999). The fertility data at AI moment would allow to know definitively the quality of prepared and selected seed (Adamou N'diaye et al 2003). The timing of AI relative to estrus detection and the location of seed deposition influence the FSCR (Saumand 2001). The other problems with AI technique are mainly related to the non-respect of seed thawing protocol (Seegers 1998). Prolonged energy deficit in early lactation and in late pregnancy can lead to the delay of ovarian activity and resumption of cyclicity and then to low FSCR (Saives et al 1998). The CFSI affects very clearly fertility as well as FSCR (Champy 1982), and requires a good monitoring to determine certainty the animal status (pregnant or not) after gynecological or ultrasound examinations (Seegers and Malher 1996).
Data showed that 35% of the cows (70 animals) were fertilized after the 3rd AI, which is not in the standard reported by Seegers and Malher (1996) (<15%). Different factors may be responsible for RB increase: metritis, hypoglycemia resulting in progesterone deficiency and glucose deficiency in uterine milk, embryonic mortality, acidosis, minerals, trace elements and vitamins imbalances. It is also necessary to consider how the breeder conducts AI (how he or she) detects the heat and when AI is performed in relation to the time of heat?) (Vagneur 1994, Ennuyer 2002). Seegers and Malher (1996) reported that big attention must be paid to this parameter because it is related to the herd reform policy, which is strongly influenced by FSCR.
The fertility index corresponds to the number of AI necessary to obtain a pregnancy. In the present study, the FI was 2.33. This value was consistent with the objective (2.5) reported by Hanzen (1994). Similarly, FI close to 2.5 were recorded by different authors (Horan et al 2005, Ansari-Lari et al 2010).
Table 4 presents data about FSCR and CCI according to parity, BCS and mean milk yield.
|
Table 4. Effect of parity, mean milk yield and BCS on FSCR and CCI parameters (total n = 200) |
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|
Parameter |
n |
FSCR
|
CCI
|
|
Parity
|
90
|
32 (35.6)
|
171±20
|
|
p |
- |
<0.001 |
<0.001 |
|
Mean milk yield, liters
|
35
|
10 (28.6)
|
206±32
|
|
p |
- |
>0.05 |
<0.001 |
|
BCS
|
26
|
2 (7.7)
|
185±26
|
|
p |
- |
<0.001 |
<0.001 |
The results obtained showed that FSCR is strongly influenced by parity. Values of 35.6% and 43.6% were recorded in primiparous and multiparous females, respectively (P <0.001). The latter were characterized by significantly short CCI compared to that of primiparous cows. This can be explained by negative energy balance, dystocial calving and postpartum anoestrus, which are more pronounced in primiparous animals. At the same time, no effect of milk production on FSCR was recorded in this study. Nevertheless, the increase of this parameter is significantly related to prolonged CCI. In this context, Ansari-Lari et al (2010) reported that increase by 100 kg of milk production corresponds to 0.3 day increase in CCI. Lucy et al (1992) noted the depressive action of energy deficit in early lactation on the resumption of postpartum ovarian activity.
Most cows had body condition scores (BCS) ranging from 2 to 3. These cows presented higher FSCR and shorter CCI compared to those with BCS < 2 or > 3. Indeed, Freret et al (2005) reported that loss of BCS between 0 and 60 days PP is related to the non-fertilization and the early embryonic mortality. Similarly, each half point lost is associated with 10% decrease in conception rate. Tillard (2003) noted that early introduction to reproduction of cows with low BCS increases the risk of embryonic mortality. Indeed, insufficient BCS (< 2.5) at the time of AI is a possible cause of a decline in the RSCR (Loeffler et al 1999).
The results about blood metabolic and hormonal parameters are summarized in Table 4. They are expressed as mean ± standard deviation.
In general, concentrations of total protein, cholesterol, triglycerides, glucose, P4 and BHB were all within the accepted range and the statistical test revealed no significant differences between the two groups of animals. In RBC group, two animals presented P4 serum levels superior than 1.5 ng/ml which indicate that they may have been inseminated at the luteal phase.
RBC were characterized by higher urea levels than FC (P<0.001). It is well recognized that urea is a very important indicator of the nitrogen nutritional status (Vagneur 1996). In the current study, elevated urea levels may be due to high crude protein diets (17-19%), which constitute usually the basic ration of high producing dairy cows (Moussa et al 2015). In fact, reports about the effect of high urea levels on fertility are contradictory, although all authors agree that the possible negative effect could act at the level of the oocyte (Alves et al 2014). Gonzalez and Rocha (1998) noted a very high uremia in cows that had more than 120 days postpartum. Thereafter, Jackson et al (2011) reported that an excess of urea seems to affect ovary and uterus. In addition, Butler (2001) revealed that high amounts of urea resulted in an increase in PGFα secretion from the endometrium and a decrease of LH binding to its ovarian receptors which in turn results in low levels of P4 and causes poor fertility. Our data corresponds to that of Vagneur 1994.
In the contrast, insulin concentrations were significantly greater in FC than in RBC (P< 0.001). Insulin is implicated in the regulation of ovarian functions (Braw-Tal et al 2009). Low concentrations of insulin in dairy cows could probably prevent post-partum resumption of cyclicity leading to the problems of fertility as repeat breeding (Obese et al 2015).
|
Table 5. Metabolic and hormonal profile of FC and RBS |
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|
Serum parameter |
FC
|
RBC
|
p |
Reference range |
|
Glucose (mmol/l) |
4.13 ± 0.88 |
4.78 ±0.52 |
>0.05 |
2.1- 5.56 (Leroy et al 2004) |
|
Insulin (µU/ml) |
4.11±1.43 |
2.58±1.30 |
<0.001 |
4.92 – 11.25 (Yousefdoost et al, 2012) |
|
TP (g/l) |
70.1 ± 5.8 |
71.4 ± 5.0 |
>0.05 |
70 – 94 (Leroy et al, 2004) |
|
TG (mmol/l) |
0.17 ± 0.02 |
0.14 ± 0.11 |
>0.05 |
0.06 – 0.2 (Leroy et al, 2004 ; Alves et al, 2014) |
|
Chol (mmol/l) |
2.69 ± 0.75 |
3.17 ± 0.69 ns |
1.3 – 8 (Leroy et al, 2004) |
|
|
Urea (mmol/l) |
4.08 ± 0.20 |
6.18 ± 2.45 |
<0.001 |
3.3 – 6.06 (Leroy et al, 2004) |
|
BHB (mmol/l) |
0.5 ± 0.09 |
0.3 ± 0.06 |
>0.05 |
< 0.8 (Leroy et al, 2004) |
|
P4 (ng/ml) |
0.85±0.10 |
0.91±0.03 |
>0.05 |
< 1 (Ginther et al 2013 |
|
TP: total protein, TG: triglycerides, Chol: total cholesterol, BHB: beta-hydroxybutyrates. |
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Received 21 March 2017; Accepted 19 May 2017; Published 1 August 2017