Breeding activities and adoption of artificial insemination amongst dairy herds in the dry zone of Sri Lanka

J Sinniah and G E Pollott*

Department of Animal Science, University of Jaffna, Thirunelvely, Sri Lanka
vathani96@yahoo.com
*SAC Sustainable Livestock Systems Group, Sir Stephen Watson Building, Bush Estate, Penicuik, Midlothian, EH26 0PH, Scotland, UK
Geoff.Pollott@sac.ac.uk

Abstract

A study was conducted to evaluate dairy cow breeding activities in five selected districts of the dry zone of Sri Lanka. Overall, the percentage of farmers adopting 'natural service', 'artificial insemination and natural service' and 'artificial insemination' were 63%, 27% and 10%, respectively. The major reasons for farmers not adopting AI were identified as "no knowledge about AI" and "no persuasion and advice". The major signs used for heat detection were mucous discharge, bellowing, restlessness and 'mount other animals or mounted by other animals'. Only 35% of the farmers in Jaffna and less than 3% of the farmers in all other districts used pregnancy diagnosis by a veterinarian to confirm conception. Among the sire breeds used, except in Jaffna, more than 75% of the animals used were from indigenous breeds. Most of the farmers accessed the veterinary office by push bike and bus, but in Jaffna around 57% of the farmers walked to access the veterinary office. The farmer's own bulls and neighbour's bulls were the major sources of sires in natural service. The number of inseminations per conception ranged from 1 to 3. The main occupation of the family, land holding size, distance of veterinary office from the farm, level of education, source of bull and number of inseminations per conception all had a significant impact on the adoption of AI.

Key words: Artificial insemination, confirmation of conception, heat detection, natural service, number of inseminations per conception

Introduction

Sri Lanka is an island of about 65,000 km² and is located at about 5.5 degrees above the equator (Jalatge 1986). The livestock population of Sri Lanka includes 1.73 million cattle, 0.86 million buffaloes, 548,000 goats, 24,000 sheep, 86,400 pigs, 9 million poultry and 24,400 ducks (Ministry of Livestock Development and Rural Industries (MLDRI 1995).

The dairy sector plays an important role in the agrarian economy of Sri Lanka; it produces animal products to meet a part of the domestic consumption demand and provides income for well over half a million rural smallholder farmers. Since independence much investment has been made in the dairy sector to improve productivity. Genetic upgrading of local cattle and buffaloes has been considered. Both natural breeding and artificial insemination were used as means of implementing the breeding policy but the latter strategy was pursued vigorously over the former, particularly in the recent past. Even after 50 years of consistent efforts, the institutions responsible for implementing genetic upgrading of cattle and buffaloes have been able to reach only a part of the national population, particularly in the wet zone and to a limited extent in the intermediate zone, leaving the larger portion of the dry zone relatively untouched (Abeygunawardena 1998). About 60% of the cattle in the dry zone produce 45% of the total cow's milk, whereas in the wet zone 20% of the cattle produce 40% of the milk and in the intermediate zone 19% of the cattle produce 15% of the milk (MLDRI 1995).

The recent reduction in the cattle population may become a serious constraint for future dairy development in the country (Department of Animal Production and Health 1999). Cattle breeding has been recognised as a critical issue for the dairy sector (MLDRI 1995) with many programmes and schemes implemented during the last few decades. However the expected improvements have not yet been seen. Consequently these issues need to be examined more carefully to see how these programmes can be made more effective (Ibrahim et al 1999 a, b).

Based on the fact that the vast majority of the cattle population is concentrated in the dry zone of Sri Lanka and though there has been implementation of breeding programmes to upgrade the cattle population in this zone, there has been no significant improvement in the production potential of the animals, an attempt was made to study the breeding activities and adoption of artificial insemination in the dry zone of Sri Lanka.
 

Materials and methods

A survey was conducted in five districts of the North-East Province from September 1997 to June 1998 in order to obtain all the necessary information. The districts of Batticaloa, Ampari, Trincomalee, Jaffna and Vavuniya were selected from the dry zone area of Sri Lanka. The total number of farms in these districts respectively were 3409, 3736, 970, 1633, and 1478 (Planning Secretariat, Northeast province Cooperatives, Trincomalee 1997). The reason for selecting the five districts was due to the ethnic crisis prevailing in the country. The North-East Province was severely affected and most of the census and research activities had been limited to the chosen areas due to inaccessibility. The livestock sector plays a major role in the livelihood of the people of the area but the field situation has not been studied for a long time. This was why the five districts out of the eight districts in the province were selected for the present study.

Farmers were selected using the 1997 Haemorrhagic septicaemia vaccination list, using a table of random numbers. From each district, 150 farmers were selected and altogether 750 farmers were interviewed. The farmers were grouped according to their veterinary ranges, then again into their respective villages. A questionnaire was designed and a personal interview was conducted with each of these farmers. The questions were answered either by the head of the household, housewife, children or the labourer who took care of the animals. Information was obtained on land holding size, the main occupation of the family, family size, educational level of the family, purpose of rearing, source of animal, type of service, reason for adoption of a particular service system, way of accessing veterinary office, sire breeds used and particulars on AI.

Details of household unit

Land-holding size was summarized using four different groups and farmers were categorized into either 'no land', '<1 ha', '1-3 ha' or '>3ha'. Family size was categorized as 'up to 3', '4 to 5', '6 to 7' and '>7' groups. The family's main occupation was categorized into agriculture, animal husbandry and other; whilst taking into account all other possible combinations. Abbreviations used while summarizing the data were Agric (Agriculture), Anihus (Animal husbandry) and Other (Other).

Education level

Regarding the family's level of education, firstly each of the family members was categorized into the groups, 'illiterate', 'up to primary', 'above primary- up to middle', 'above middle- up to high school' and 'above high school'. These groups were given a value of 0 to 4. Based on this, an education index was developed for each family. Finally, a score of 1 to 4 was given to different index groups (Singh and Singh 1993) as follows:

Education level Value given

1. Illiterate Zero
2. Up to primary 1
3. Above primary up to middle 2
4. Above middle up to high school 3
5. Above high school 4

Education index Score:
Zero       1
<1          2
1-2         3
>2          4
* Total score of the family is the summation of education level of the members in a family.

Purpose of rearing cattle

Purpose of cattle rearing was mainly grouped into milk, meat, draught and manure. In addition to this, all other possible combinations were taken into account. The abbreviations and the respective combinations were:

Abbreviation Combination:

Cattle breeding

Under breeding activities, farms were summarized based on the type of service used, viz. natural, AI and both. There were 32 reasons listed for the adoption of AI with 162 different combinations. Since the farmers had more than one reason, only the reasons with more than one percentage (listed over all the five districts) were summarized. The figures were rounded to the nearest whole number hence the totals could add up to <100% or >100%. In addition, information was collected on the detection of heat, confirmation of conception, sire breeds used to breed the animals, way of accessing the veterinary office, number of inseminations required per conception and distance of the veterinary office from the farm. In respect of distance of the veterinary office from the farm; the actual distance of the veterinary office from the farm was recorded; later they were grouped into different groups namely '1 to 5' km, '5.1 to 10' km, '10.1 to 15' km and >15km.

Statistical analysis

Microsoft Excel was used to input data and concatenate different combinations of response. Microsoft Access was used to get frequencies of some variables and the SAS procedure "Frequency" was used to summarize other variables. Figures were rounded to the nearest whole number and do not necessarily add up to 100 percent. Whenever there was more than one response per informant the total exceeded 100%. A chi- squared test was performed to study the effect of different factors on AI.

Results

Type of service by district

Figure 1 shows the type of service by district. Overall, the percentages of farmers adopting 'natural service', 'artificial insemination and natural service' and 'artificial insemination' were 63%, 27% and 10% respectively. But when the districts were considered separately (within the district) adoption of natural service alone was high in Batticaloa (79%), and low in Jaffna (19%). As far as AI alone was concerned the situation was reversed. The adoption of AI alone was high in Jaffna (37%) and low in Batticaloa (4%).

Reason for selecting AI or natural service by district

Tables 1 and 2 show the reasons listed by farmers for the selection of either natural service or AI. The major reasons listed all over the districts for natural service or non-adoption of AI (Table 1) were 'no knowledge about AI' (2 - 84%) and 'no advice and persuasion' regarding AI (0 - 17%). Other reasons listed were having own bull, (this includes neighbour's bulls as well) (2 - 10%), failure of AI (1 - 9%), extensive system (2 - 9%), small size of indigenous animals (1 - 3%), distance of veterinary office from the farm (0 - 4%), ethnic problems (0 - 3%) and do not have time (0 - 2%). If the farmer wants to adopt AI, proper transport and communication facilities are essential to get the insemination done in time. Due to the ethnic crisis the transport and communication facilities were severely affected which made the farmer have to rely on natural service.

The major reasons given for the adoption of AI (Table 2) were expectation of good breeds (4 -21%), high milk yield (3 - 20%), easy to do (0 - 12%) and cheap (0 - 37%). The other reasons listed were, narrow down calving interval (0 - 1%), absence of bull (0 - 1%), healthy calf (0 - 2%), difficulties in maintenance of bull (2 - 4%) and ethnic problems (0 - 3%). When the farmers rely on AI to breed their animals, proper record keeping will ensure breeding on time. In contrast if they rely on natural service to breed their animals, most of the time they rely on a neighbour's bull. If the bull is not available on time, it will prolong the calving interval.

Under these circumstances farmers prefer AI over natural service. Only the farmers in the Jaffna district listed narrowing down calving interval (1%) and obtaining healthy calves (2%) as reasons for adopting AI.

Detection of heat by district

Table 3 shows the signs used to detect heat to inseminate the animal. The major signs used were bellowing (10 - 74%), mucous discharge (6 - 71%), restlessness (1 - 23%), loss of appetite (2 - 27%) and mounting of other animals or mounted by other animals (9 - 49%) and drop in milk yield (0 - 16%).

Table 4 shows the heat signs on which farmers depend to detect heat. Though the percentage of farmers adopting AI varied between districts, overall most of the farmers relied on more than one sign to detect heat. It should be noted that in Jaffna about 61% of the farmers relied on three or more than three signs to detect heat.

Confirmation of conception by district

Table 5 shows the signs on which farmers depend to confirm conception. Overall the major signs listed were absence of heat signs (24 - 62%), do not allow the calf to suck (1 - 32%), ballotement (3 - 57%), mammary development (0 - 58), drying off (0 - 20%), crossed by bulls (0 - 24%) and pregnancy diagnosis (0 - 35%). Other reasons listed were milk becoming sticky, calving calendar, seasonal calving and by experience. Among the signs listed above, pregnancy diagnosis is the most accurate method of confirming conception. In Jaffna in addition to other signs, 35% of the farmers relied on pregnancy diagnosis by a veterinarian to confirm conception, while in other districts only 1 or <1% of the farmers relied on this technique.

Sire breeds used to breed the animals by district

Table 6 shows the sire breeds used to service the animals by both natural and artificial insemination and their percentages by district. The percentage of sire breeds used varied among districts. In Batticaloa, Trincomalee and Vavuniya, more than 75% of the sire breeds were indigenous. In Ampari district, the percentage of indigenous breeds was about 75% while in Jaffna it was 24%.