Livestock Research for Rural Development 16 (4) 2004

Citation of this paper

Performance of artificial insemination in smallholder dairies of Nharira-Lancashire in Zimbabwe

S Kaziboni, N T Kusina#, S Sibanda, S Makuza, O Nyoni and E Bhebhe*

Animal Science Department, University of Zimbabwe, PO Box MP 167, Mount Pleasant, Harare, Zimbabwe
* Paraclinical Veterinary Science Department, University of Zimbabwe
PO Box MP 167, Mount Pleasant, Harare, Zimbabwe
stanlakekaziboni@yahoo.com;   newtkus@uiuc.edu
Present address: #Corresponding author, Animal Sciences Laboratory,
1207 W Gregory Drive, University of Illinois, Urbana, IL 61801, USA


Abstract

The study was conducted during the period February to August 2002 to assess the efficacy of artificial insemination by "trainee farmers" from Nharira (communal farming area) and Lancashire (small scale commercial farming area) in Zimbabwe. Prior to the inception of the study, two farmers were selected for training in AI after which, they embarked on the artificial insemination of a total of 754 cows/heifers in Nharira-Lancashire using Friesian semen (0.5 ml straws). During the study, the following data were collected/recorded: conception (determined through rectal palpation 30 to 60 days following insemination), inseminator identity, cow identity, month of insemination, farming area, cattle breed, parity and body condition score.

Mean (±SE) number of inseminations/cow for successful conception was 1.74 ± 0.43 for Nharira and 1.65 ± 0.43 for Lancashire. The total number of cattle covered was 238 in Nharira and 441 in Lancashire. The proportion of cows that successfully conceived did not differ (P > 0.05) between the two farming systems (57 vs. 60%, for Nharira and Lancashire, respectively). However, overall conception varied with parity and breed of cattle (P < 0.05), with indigenous cattle (Sanga cows) recording lower conception than the crossbred and exotic cattle. There was a tendency of enhanced conception with an increase in body condition, although the effect was not significant (P>0/05).

It is inferred that the two trainee farmers successfully inseminated 59% of the cows/heifers and the result might be considered a success. The results provide evidence that overall conception rate and the number of inseminations for successful conception were significantly different (P < 0.05) among breeds with indigenous breeds recording lower conception compared to exotics and crossbred cows.

Key words: artificial insemination, conception, dairy, farmers, , smallholder


Introduction

Sustainable milk production of a dairy enterprise can be achieved through the purchase of replacement heifers, something that is not feasible for most poor-resource smallholder farmers. An alternative is the strategy of within herd good calf management (Mandibaya et al 1999). The latter is feasible because today most of smallholder dairy farmers own small dairy herds and with the introduction of AI this would provide farmers an opportunity to transform the herd structure by the introduction of "proven dairy semen" with a great potential to produce better heifers, thereby increasing milk production (Chupin and Schuh 1993). This strategy is in accordance with the government promotion of improved milk production by the smallholder sector (Shumbayaonda 1995).

Despite the fact that AI use in the Zimbabwe smallholder dairy sector has been minimal or non-existent, its potential to improve smallholder dairy productivity cannot be overemphasized. Improvement in service provision, such as AI centers within the vicinity of progressive dairy communities, development of "farmer managed" market structures and promotion of "value adding" to products such as fresh or fermented milk products and packaging can ensure a consistent flow of income to dairy farmers' households. The latter is obligatory in view of findings that excess milk during the rainy season was discarded in the area of study due to a lack of sufficient facilities to accommodate excess-presented milk. This created a disincentive to continue, let alone attract new farmers to engage in dairying. Additionally, AI services in Zimbabwe are under the monopoly of a privately owned company called the Animals Breeder's Company (ABC) that previously primarily served the large-scale dairy commercial farmers. However, with the demise of the commercial dairy sector there is a need to ensure provision of such a service to the smallholder farmer.

With the aforementioned observation, and as part of a long-term program, the objective of this study was to determine the ability of participating dairy farmers in Nharira-Lancashire to breed their dairy cattle by AI through the provision of these services by their colleagues (qualified personnel and localized AI services) to enhance productivity.


Methodology

Study site

As mentioned previously, this study is a part of a series of experiments of an on-going project conducted with 82 farmers in Nharira-Lancashire with funding provided by the Danish International Development Agency (DANIDA). In this particular study, the herds of dairy cattle monitored were reared by farmers previously trained to keep and maintain dairy records (Francis 1998) and on the art of AI. A detailed description of the study site was presented in an earlier report (Kaziboni et al 2003). Briefly, this site is located in a semi-arid ecological zone approximately 172 km southeast of Harare in Mashonaland East Province of Zimbabwe. It lies 1460 m above sea level on latitude 190 20S and longitude 300 350E. Mean annual rainfall received in the area for the period 1988 to 1998 was 640 mm and mean maximum and minimum temperatures were 25.40C and 120C, respectively (Steinfeld 1988). The vegetation in Nharira consists of sparsely scattered trees, short unbrowseable bushes and overgrazed natural veld growing in non-arable plains. In Lancashire, trees are more abundant and tall grasses such as Heteropogon and Hyparrhenia grow in several paddocks, some of which show evidence of underutilization by becoming moribund.

Animal selection

A total of 679 cattle comprising 584 pluriparous, 56 primiparous cows and 39 heifers were selected from the original 752 cattle examined for normal ovarian function in the previous study. Prior to the study, most farmers agreed to remove all bulls from both farming areas. As an important consideration, local bulls were separated from the study cows/heifers and confined in separate enclosures at night. Additionally during the period of study, cattle were sustained mainly on natural pasture in both communities and lactating cows were milked twice daily in the morning and evening.

Inseminators

Firstly, farmers identified two sites for setting up insemination points located centrally (5-6 km radii); one in Nharira and the other in Lancashire. Handling facilities at the AI units, suitable for restraining cattle while offering the inseminators both protection and a good working environment, were set up. In the meantime, two farmers were selected for training in AI at an Agricultural College called Chibero approximately 20 km west of Harare. The two selected farmers spent one week at Chibero Agriculture College (Norton, Zimbabwe) where they were successfully trained, attained adequate practical expertise in AI in cattle and received certification.

Semen

Frozen semen in 0.5 ml French straws of Red Dane and Friesian bulls were purchased from the Animal Breeders Company (Harare, Zimbabwe). Semen was thawed at ambient temperature before insemination. In addition, all equipment and accessories required for the study were also purchased from the Animal Breeders Company (Harare, Zimbabwe).

Insemination procedure

Visual checks for oestrus were carried out at least twice daily. Standing to be mounted ("standing heat"), bawling, and attempting to mount were the three criteria used for determining the presence of spontaneous oestrus. Subsequently, a farmer would trek a cow/heifer to the closest AI site for insemination using the "am-pm guideline" (Peter and Ball 1995). Briefly, any cows/heifers noted in heat in the morning were inseminated that afternoon and those identified in the afternoon were inseminated the next morning. Cows/ heifers were re-inseminated according to observed oestrus until they were confirmed pregnant. Each AI unit had a resident inseminator who was responsible for insemination in the respective study area.

Data collection

During the study the following data were collected: conception (determined by rectal palpation 30 to 60 days following insemination), inseminator identity, cow identity, month of insemination, farming area, cattle breed, parity (were confirmed by questioning the farmers and/or farmer records). Body condition scoring was assessed using a scale ranging from 1 (emaciated) to 5 (obese) (ADAS 1978). All dates of inseminations and conception, and pregnancy diagnosis were recorded. Data obtained from insemination through PD were used to generate number of inseminations for successful conception and overall conception rate by farming system, and these were utilized in statistical analyses.

Statistical analysis

Data computed on conception rate were transformed by arcsine square root function, whilst number of inseminations / conception were transformed using natural logarithmic to attain normality then subjected to analysis using Proc Freq, Proc General Linear Model Procedure of SAS (SAS 1998) as illustrated in the model below.

Statistical model

Y ijklmno = µ + Ii + Bj+ Tk + Pl + Am + Mn + Eijklmno

Where
     Y ijklmno = variable number of inseminations/conception
                µ = overall mean common to all observations;
                Ii = effect of inseminator (1- Nharira and 2- Lancashire inseminators);
                Bj = effect of breed (1 - indigenous, 2- crossbred and 3 - exotic);
                Tk = effect of body condition score (k = 1; 2; 3; 4; 5);
                Pl = effect of parity (0 - heifer, l- parity 1,…, 4- parity 4);
                Am = effect of area (1 - Nharira and 2 - Lancashire);
                Mn = effect of month (1 - February, 2 - April, 3 - June, 4- August);
                E ijklmno = random residual error.


Results

There were no differences between farming systems in conception rate nor in numbers of inseminations per conception (Tables 1 and 2). 

Table 1.  First conception rates (%) recorded in Nharira and Lancashire areas

Month

Farming system

Overall

Nharira

Lancashire

February

54 (35)

59 (56)

57 (91)

April

56 (33

64 (70)

61 (103)

June

60 (41)

55 (33)

58 (74)

August

55 (26)

61 (109

60 (135)

Overall

57 (135)

60 (266)

59 (401)

Figures in brackets () indicate the number of cattle recorded


Table 2.  Mean ( SEM) of number of inseminations per conception recorded in Nharira and Lancashire areas

Month

Farming system

Overall

Nharira

Lancashire

February

1.77 0.46 (59)

1.74 0.43 (238)

1.78 0.5 (158)

April

1.77 0.46 (59)

.35 0.26 (110)

1.65 0.33 (169)

June

1.46 0.21 (68)

1.52 0.29 (58)

1.65 0.25 (126)

August

1.51 0.37 (47)

1.42 0.33 (179)

1.50 0.34 (226)

Overall

1.74 0.43 (238)

1.65 0.43 (441)

1.64 0.36 (679)

Figures in brackets () indicate the number of cattle recorded

Conception rate at first service differed among breeds (P<0.05; Figure 1) with lower rates for the indigenous breed.

Figure 1.  Conception (%) results following first insemination
among the different breeds during the period February to August 2002

 

Conception rate also varied among parities (Figure 2) with lower proportions conceiving in the first parity..

Figure 2.  Conception (%) results following first insemination
classified according to parity during the period February to August 2002

There appeared to be an improvement in conception rate with increasing body condition score (Figure 3) but the differences were not significant.

Figure 3.  Overall conception (%) in relation to body condition scores
of the cattle during the period February to August 2002


Discussion

The major findings of this study were that it is highly feasible to introduce AI at the  "local level" in the smallholder dairy sector of Zimbabwe such as Nharira-Lancashire with the farmers taking the central role in the implementation and management of artificial insemination, and thereby realize relatively good fertility rates in their dairy herds as indicated by an overall 59% conception rate. Additionally, it was noted that the overall conception rate varied among breeds with the indigenous breed recording lowest conception rate compared to exotics and crossbred cows. There was no significant relationship between body condition score and conception, but conception varied significantly among parities.

To our knowledge this is the first research that has been conducted on AI in a smallholder dairy sector where some participating farmers are trained in the art of AI and are given the opportunity to inseminate successfully their own and other community members' cattle. Similar results and successful participation and management of AI by smallholder farmers is prevalent in Kenya where it has improved milk production from dairy farms where dairy farming in close proximity to the urban market has become a lucrative business (Oluoch-Kosura et al 1999). The overall mean conception observed in this study (59%) was the same as the report by other researchers within the tropics such as Correa et al (1990) who recorded conception of 59% in Zebu cattle. Galina and Arthur (1990) reported relatively higher proportions of 63 to71%, whereas Toolsee et al (1996) reported lower conception rates ranging between 35% and 40%. Differences between studies can be attributed to a multitude of factors such as efficacy of insemination, timing, semen quality, breeds of animals in the studies among other factors (Esselmont 1992; Wattiaux 1996). Nonetheless, it is encouraging to note that Arthur et al (1996) reported that when using AI to breed cattle, the aim should be to improve first serve conception from 40% to 60%.

The observation that indigenous cows responded differently compared to crossbreds and exotic cows was not surprising since it has been observed that the Zebu normally has a tendency for lower first conception rate than crossbreds or exotic breeds (Azage Tegegne et al 1981; Kuwiwa et al 1983; Mukasa-Mugerwa et al 1991a). Some of the possible reasons for lower proportions of indigenous cows conceiving at first insemination are that the Zebu does not exhibit overt estrus signs like the crossbreds and/or exotic breeds (Mukasa-Mugerwa et al 1991b). Estrus in the Zebu tends to be shorter and is often subdued (Mattoni et al 1988). Furthermore, Zebu cows often refrain from repeated mounting (Dawuda et al 1989). Estrus detection is therefore more difficult to determine in Zebu than in Bos taurus cattle because of these many physiological and managerial problems. In the case of this study, the indigenous cattle are mainly of the Sanga breed, a crossbreed of Bos taurus and Bos indicus.

An important finding to note was the differences in response by parity with an improvement in the conception as parity increased. This is consistent with reports in the literature whereby heifers are normally recognized to be immature and may not exhibit a fertile first estrus (Alexandra et al 1999). However, there are inconsistent reports in the literature on the effects of parity on conception rate with some reporting similar results to our study (Mukasa-Mugerwa et al 1991c) or no differences (Perez et al 1999). As mentioned earlier, it is difficult to compare studies because of the differences in environment and circumstances that are unique to each study.

Normally, good body condition of cows is expected to have a positive impact on conception rate (Rowlands et al 1994). Our results in this study do not fully corroborate this assumption although there was a tendency for conception rate to increase with increase in body condition score. The failure for the observed differences to be significant may be attributed to the finding that the variation in condition of the animals was minimal. The implication  is that the farmers in the  Nharira-Lancashire area practiced good feeding and management, and had mastered the art of heat detection as reported previously (Kaziboni et al 2003). Further evidence is provided by the findings that there were no differences between the performance of the two inseminators for Nharira (communal area) and Lancashire (small scale commercial farming area).


Conclusion

The results from this study indicate that it is highly feasible to introduce AI at "local level" in the smallholder dairy sector of Zimbabwe such as Nharira-Lancashire with the farmers taking the central role in the implementation and management of artificial insemination, and thereby realize relatively good fertility rates in their dairy herds.


Acknowledgement

We thank the Danish International Development Agency (DANIDA) for funding this study, and the Department of Animal Science of the University of Zimbabwe for giving us logistical support. We also acknowledge the farming community of Nharira-Lancashire for the support and use of their cattle for the study.


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Received 11 January 2004; Accepted 3 March 2004

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