Ali T; Lemma A; Yilma T

Research Article

Austin J Vet Sci & Anim Husb. 2015; 2(3): 1015.

Effect of Management Practices on Reproductive Performance of Smallholder Dairy Cattle

Ali T¹, Lemma A²* and Yilma T²

¹Department of Animal Sciences, College of Veterinary Medicine, Samara University, Ethiopia

²Department of Clinical Studies, College of Veterinary Medicine and Agriculture, Addis Ababa University, Ethiopia

*Corresponding author: Lemma A, Department of Clinical Studies, College of Veterinary Medicine and Agriculture, Addis Ababa University, Ethiopia

Received: October 05, 2015; Accepted: November 23, 2015; Published: November 27, 2015

Abstract

In smallholder dairy farms, reproductive performance has been described in relation to each cow’s calving date. The objective of this research was to evaluate the reproductive performance based on the intervals from each cow’s calving date and to assess effect of management practices involved thereof. A retrospective data of 529 farm characteristics and management practices, 722 cow reproductive histories and 700 inseminations results were recorded through records analysis and questionnaire. The mean (±SE) days from Calving to First Service Interval (CFSI) and Calving to Conception Interval (CCI) were 168.9±5.5 (n= 449) and 197.2±7.0 (n= 312), respectively. The mean (±SE) Days after Last Calving for non-pregnant cows (DALC) was 197.3±6.2 days. Number of Service per Conception (SPC) was 1.40± 0.04. First Service Conception Rate (FSCR) and Conception Rate (CR) were 40.6% and 73.4%, respectively. Dairy owner’s practice of submission of non-pregnant animals to service was known to influence performance and pregnancy outcomes. Pregnancy by day 200 post calving (PREG-200) and Non-Pregnancy by day 300 post calving (NPREG-300) were used to evaluate the management on performance. The proportion of PREG-200 and NPREG-300 were 28.2% and 53.8%, respectively. Poor BCS and age of the cows, production system, educational status of dairy owners and problems related to AI had all significant effect on the reproductive performance of the dairy cows in the smallholder practice.

Keywords: Reproductive performance; PREG200; NPREG300; Smallholder dairy

Introduction

Ethiopian dairy production is generally characterized as a year round calving system with low nutritional input and limited use of mixed rations. The lifetime productivity of a dairy cow depends on the number of calves born and the amount of milk produced during its active reproductive phase [1]. Thus, reproductive performance and milk production are the major determinants of dairy cow profitability. Reproductive performance optimization requires measurement of current performance, assessment of areas in which performance is less than desirable and subsequent suitable interventions [2,3].

The outcomes of reproductive performance can be described by measuring the distribution of conceptions over time and/or conception efficiency. Within their limitations mean interval from Calving to First Service (CFSI) and/or Calving to Conception (CCI) are the two mainly used indices to measure reproductive outcomes. The distributions of CCI/CFSI are usually positively skewed and their mean values are affected by small numbers of extreme values (particularly in small herds) which are among the limitations. Furthermore, CFSI/CCI exclude non inseminated cows that exceed voluntary waiting period and CCI do not explain cows that fail to conceive after several inseminations [4]. Thus, measuring mean CCI as major reproductive outcomes may underestimate variation of reproductive performance between populations. The limitations of CCI can be addressed by evaluating proportion of cows pregnant and non pregnant by specified intervals after their calving date that are often influenced by reproductive management practices. Reproductive performance can also be described using proportions of cows pregnant by specified time periods after their calving date since various time periods have been used, including 80 days [5], 100 days [4], 115 days [6], 150 days [7,8], 210 days [6] and 320 days [8].

Dairy production systems vary internationally due to differences in management system, physical environment, social-economic status of producers, relative cost of labor, nutrition economics, available reproductive technologies and breeding costs, infrastructure availability and the regulatory environment with adaptability and genetic composition of cattle [9].

Major reproductive performance factors encompass both management factors (such as methods of husbandry, feeding, estrus detection, semen handling and transition cow management) and cow-level factors (such as age, BCS, post parturient problem, disease, milk yield, and genetics) [10,11]. Studies in tropical countries showed that performance of smallholder dairy cattle affected by various factors such as genotype, location (geographical location), and season of calving; suckling status, parity, and body condition score [12-15]. While it is debatable, social status of farm owners and attendants (such as education level of farmer owners and attendants, and years in farming) is a potential factor for poor reproductive performance of cows and hindrance in the effectiveness. Hence, performance needs to be assessed not only on an individual cow basis, but also at herd and general management level. The major objectives of this study were to describe the reproductive performance and to evaluate the effect of reproductive management factors on the reproductive performance of smallholder dairy cows under AI in selected sites of Ethiopia.

Materials and Methods

A retrospective data analysis was conducted in smallholder dairy farms. Since smallholder dairy systems vary substantially by geographic location, 5 districts were selected based on coverage of AI technology and agro-ecological constitution from four regions (Amhara, Oromia and South Nation and Nationality People Region). The study enrolled only AI breeding farms and their cows with ≥60 days postpartum. Data for 529 farms were recorded on farm characteristics and their management strategies through record analysis and questionnaire. Data on reproductive history from 722 cows and 700 inseminations were also collected. Reproductive tracts of cows were palpated per rectum at Day >60 post AI to determine pregnancy. Cows were classified as non pregnant only after a negative pregnancy diagnosis, and return to estrus within 56 days post AI.

Data was entered into a Microsoft Excel 2007 data sheet and analyzed using SPSS 15 for Windows (SPSS 2006, LEAD Technologies Inc). Data was described; with additional summarization of results by graphs and plots to describe pregnancy success. The fixed effects of independent variable that were screened for significant effect (cow level factors and management factor, (Table 1) by univariate model were later analyzed using generalized linear models. PREG200 and NPREG300 were taken as dependent variables. Conception rates were described for first inseminations and all AI.

Table 1: Factor variables screened for further analysis using general liner model.




  
    Cow level factors 
    Management level factors 
  
  
    Breed
    Sex of the farm owner
  
  
    Zebu
    Male
  
  
    Zebu*Holstein Friesian cross
    Female
  
  
    Age 
    Farm attendant
  
  
    ≤ 4 years
    Owner
  
  
    4< X ≤ 6 years
    Family member
  
  
    6< X≤ 8 years
    Employed laborer
  
  
    >8 years
     
  
  
    Parity (1, 2, 3, >4)
    Farm attendant educational status (educated, uneducated)
  
  
    BCS  on 1-5 scale (≤2,    2<X≤3,  >3)
    Years involved in dairy farming (≤5, 5<X≤10, >10 years)
  
  
     
    Years in using AI breeding(≤5, 5<X≤10, >10 years
  
  
    Availability of bull in the farm (Yes/No)
  
  
    Production system (Intensive/Extensive)
  
  
     
    Feed sufficiency in the view of the farm attendant (Yes/No)
  
  
    Regularity of AI service (Yes/No)
  
  
    Pregnancy diagnosis for inseminated cows (Yes/No)
  
  
    Problems related to AI other than regularity (Yes/No)



Table 1:  Factor variables screened for further analysis using general liner model.

Results

The mean (±SE) for CFSI was 168.9± 5.5 days (range = 33–583 days) and CCI was 197.2± 7.0 days (range = 40–587 days). The mean (±SE) for DALC for non-pregnant cows were 197.3± 6.2 days (range = 62–698 days). Mean (±SE) SPC for cows/heifers were 1.44± 0.04 (range = 1–5) and the CR and FSCR were 73.8% and 40.9%, respectively. Pregnancy result was known to be a function of days post last calving (Figure 1) although a significant difference was observed in PREG200 and NPREG300 between farm locations. Cows in Bishoftu had the highest PREG200 than cows in Guangua (OR= 2.79) and cows in Aletawendo had the lowest PREG200 (OR= 0.616). The percentage of cows those were pregnant by day 200 post calving were 28.2% while non-pregnant cows by day 300 post calving were 53.8%.

Figure 1: Pregnant and non-pregnant cows by days post calving.

    
    
    Figure 1:  Pregnant and non-pregnant cows by days post calving.

The selected factors for modeling of PREG200 and subsequent result are presented in (Table 2). PREG200 was strongly associated with location, BCS, age of the cow and production system. Dairy cows kept in Bishoftu site had better reproductive performance in PREG200 than kept in Guangua which was the reference category (OR= 2.2.79). Cows with very poor condition (BCS ≤2, OR= 0.257) and cows with moderate condition (BCS= 3, OR= 0.527) were less likely to be pregnant by Day 200 post AI compared to those with relatively good condition (BCS >3). PREG200 was significantly better for cows in intensive production system than those in extensive system (P<0.05; OR= 1.844).

Table 2: Cow and management factors screened for their effect on PREG200.




  
    Cow Variables 
    N
    Proportion [%]
    P-value
    External Variables 
    N
    Proportion [%]
    P-value
  
  
    Age of the cow (Year)
     
    Gender of the farm owner
     
  
  
    <4
    134
    0.37
    0.005
    Female
    122
    0.39
    0.004
  
  
    4<X<6
    176
    0.21
     
    Male
    435
    0.25
     
  
  
    6<X <8
    152
    0.32
     
    Production system
     
  
  
    >8
    95
    0.10
     
    Intensive
    361
    0.35
    0.000
  
  
    BCS
     
    Extensive
    196
    0.16
     
  
  
    <2
    62
    0.10
    0.000
    Access to regular AI service
     
       
  
  
    2X<3
    287
    0.25
     
    No
    423
    0.24
    0.001
  
  
    >3
    208
    0.39
     
    Yes
    134
    0.40
     
  
  
    Breed 
     
    Breeding site
     
  
  
    Cross
    515
    0.29
    0.037
    On farm
    241
    0.36
    0.001
  
  
    Zebu
    42
    0.14
     
    AI center
    316
    0.28
     
  
  
     
    Location
     
  
  
    Aletawendo
    134
    0.20
    0.037
  
  
    Bishoftu
    110
    0.51
     
  
  
    Dale
    99
    0.29
     
  
  
    Fogera
    113
    0.25
     
  
  
    Guangua
    101
    0.17



Table 2:  Cow and management factors screened for their effect on PREG200.

The descriptive statistics and odds ratio for factors affecting NPREG300 is shown in (Table 3). NPREG300 was strongly associated with location, BCS, age of the cow, years involved in dairy farming and educational status of farm attendant. Dairy cows kept in Aletawendo had the highest number of non-pregnant cows by 300 after calving (OR= 1.637).

Table 3: Descriptive statistics and odds of factors affecting NPREG300.




  
    Variable 
     
      N 
    Proportion of non pregnant by day 300 post AI 
    Odds ratio (95% CI) 
    P-value 
  
  
    Location
     
     
     
     
  
  
    Aletawendo
    98
    0.60 
    2.169 (1.059-4.441)
    0.014
  
  
    Bishoftu
    102
    0.35 
    0.853 (0.410-1.774)
     
  
  
    Dale
    82
    0.39 
    0.881 (0.418-1.857)
     
  
  
    Fogera
    94
    0.57 
    1.294 (0.622-2.507)
     
  
  
    Guangua
    70
    0.54 
     
     
  
  
    BCS
     
     
     
     
  
  
    <2
    42
    0.69 
    2.706 (1.167-6.274)
    0.046
  
  
    2<X<3
    223
    0.52 
    1.447 (0.914-2.290)
     
  
  
    >3
    181
    0.41 
     
     
  
  
    Production system
     
     
     
     
  
  
    Intensive
    298
    0.43
    0.586 (0.364-0.943)
    0.027
  
  
    Extensive
    1488
    0.62
     
     
  
  
    Educational status of farm attendant
     
     
     
     
  
  
    Uneducated
    270
    0.44 
    1.629 (1.079-2.458)
    0.020
  
  
    Educated
    176
    0.56



Table 3:  Descriptive statistics and odds of factors affecting NPREG300.

Cows with poor condition (BCS ≤2, OR= 2.706) were highly likely to be non pregnant by day 300 after calving compared with those with relatively good condition (BCS>3). Uneducated farm owners/attendants were less likely to submit their animals at the right time and hence were more likely to be non pregnant by Day 300 post AI (OR= 1.629). Similarly, the NPREG300 was lower for intensive farms (OR= 0.586). NPREG300 was also influenced by location of the farms.

Discussion

Location differences in reproductive performance are often results of difference in feed and feeding, microclimatic conditions including temperature and humidity, and management practices. The relatively better reproductive performance of dairy cows at Bishoftu is associated with the conditions of better feed availability, better AI services in bigger towns, and educational status and farming experience of dairy owners. As reported by Obese et al. [16], and Domecq et al. [17], lack of supplementary feeding in extensively grazed dairy cows affect their reproductive performance. Estrus activities are also suppressed due to heat stress [18-20]. Besides the nutritional deficiency, parasitic loads, and suckling, as is a common practice in extensive management systems, interfere with ovarian function, thereby prolonging the days open [21]. The effect of low level of nutrition on extended postpartum period due to weight loss was also noted by Gebreegziabher et al. [22]. Cows reared under very limited resources and unfavorable climate of extensive management systems may fail to become pregnant [21]. Such cows lose another year before being capable of conceiving and two-years CI are not uncommon, especially in the semi-arid tropics. Similar finding differences in performance found between cows under intensive and extensive system was also reported in previous study [23] with higher pregnancy rate for intensive farms compared to extensive farms.

PREG200 and NPREG300 were strongly associated to BCS. Cow’s good condition is twice as likely to be pregnant compared to those with poor condition. This finding agrees with the that of Buckley et al. [24] and Roche et al. [25] who reported odds of successful pregnancy by 6-week in-calf rate were reduced by 1.28 times and 1.62 times with each 0.5-unit decrease in BCS. Besides Roche et al. [25] also reported that the odds for successful pregnancy at 12 wk were 1.7 times lower in cows that had 0.5 BCS units lower. Cows losing over 10% of body weight loss also have prolonged CCI [26]. Cows that lost 0.3 BCS unit (5-point scale) are 1.17 times less likely to conceive than cows that did not lose condition after calving [25]. Cows in low BCS (less than 2) in early lactation are known to have delayed intervals to conception [27].

In the present study, the likelihood of NPREG300 for cows with poor condition (BCS of 2 or less) was 2.706 times higher than cows with BCS 3 or above. This result coincides that of Buckley et al. [24] who reported similar finding. This may be attributed to delay for first service (prolonged CFSI), delayed onset of cycling postpartum and embryo and fetal loss [28-33].

The proportion of NPREG300 were higher for cows managed by uneducated than educated attendants or owners (44% vs. 56%). As also evidenced by the difference in SPC (1.36 for cows managed by uneducated attendants versus 1.58 for educated) was mostly associated with heat detection efficiency and understanding reproductive events such as estrus, services, pregnancies and calving. Success of AI has been associated with knowledge and farming skill of dairy farm attendants or owners [15,23,34].

Conclusion

Improving reproductive performance of dairy cows’ requires understanding of current performance, identifying challenges and opportunities with the causal factors, and subsequently creating sustainable options for overcoming the problems. Management practices were known to affect pregnancy success and reproductive performance of smallholder dairy cows. In the present finding, the CFSI, CCI and DALC are longer than the optimal values further complicated by the influence of location of the farms, nutritional status of the cows, and management which in turn is influenced by educational level of the farm attendants and production system type. Apart from the conventional methods of evaluating reproductive performance, the introduction of evaluation parameters such as submission to service at the right time post partum, pregnancy by day 200 and non pregnancy by day 300 provide which are directly influenced by the management practices provide a useful evaluation alternative. The extended CCI, lower PREG200 and higher NPREG300 in the current finding indicate that the management practice in smallholder dairy farms is poor or inefficient.

References

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Citation: Ali T, Lemma A and Yilma T. Effect of Management Practices on Reproductive Performance of Smallholder Dairy Cattle. Austin J Vet Sci & Anim Husb. 2015; 2(3): 1015. ISSN:2472-3371

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