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%.

Among the Bos taurus breeds, the most frequently occurring were Jersey and Jersey cross. The percentages listed may be less than real percentages since sometimes the farmers did not know what breed was used to inseminate their animals because the insemination receipt showed only the code number of the breed, not the breed name.

The Bos indicus breeds listed were Sindhi, Sahiwal, Tharparkar, Khillari, Kangayam, Hariana and indigenous. Khillari and Kangayam were found in Batticaloa, Ampari and Trincomalee. Hariana was found in Ampari and a small percentage of this breed was found in Batticaloa as well.

The indigenous cattle of Sri Lanka are small with an average adult weight of 160kg. Their mean 305d lactation yields are around 450kg. In the dry zone the cattle forage on poor natural pastures with virtually no inputs of concentrate feed; managerial skills are limited and milk is regarded as a by-product of animals that are raised mainly for beef (Buvanendran and Mahadevan 1975).

Way of accessing veterinary office by district

Table 7 shows the percentage of farmers adopting different ways to access the veterinary office and the percentage who do not access the veterinary office. This is an important factor since the farmer has to inform the veterinary office so that their animals can be inseminated on time and to get other assistance from the respective veterinary offices.

The proportion of farmers who do not visit the veterinary office were 73, 10, 29, 9 and 30% in Batticaloa, Ampari, Trincomalee, Jaffna and Vavuniya, respectively. The majority of the farmers used push-bike (20 - 83%) and bus (5 - 33%) to access the veterinary office. The percentage of farmers using a motorbike or on foot were <1 - 12% and <1 - 57% respectively. The use of the telephone, three-wheeler and car ranged from 0 - 2%.

Source of bulls by district

Table 8 shows the different sources of bulls by district. Where the no response group adopts only AI. The percentage of farmers depending on 'own bull', 'neighbours bull' and the stud centre were 21% - 63%, 51% - 97% and 1% - 10%, respectively.

The results revealed that the percentage of farmers relying on a neighbour's bull was higher than those depending on their own bull to service their animals. But the percentage depending on a stud centre was considerably lower (1 - 10%) than those who relied on their own bull and a neighbour's bull. The percentages depending on stud centres were 10% in Jaffna and about 7% in Vavuniya. In other districts it was around 1%.

Number of inseminations per conception

Table 9 shows the number of inseminations required per conception. Among the farmers who adopted only AI, the number of inseminations required per conception varied from one to five. But when the number of inseminations became more than three, the percentage went down drastically.

Distance of the veterinary office from the farm

Table 10 shows the distance of the veterinary office from the farms in kilometers. About 32 - 79% of farms fell within the range of 1 - 5km. It should be noted that about 79% of farmers in Jaffna fell within the range of 1-5 km but the percentage for other districts was 32 - 49%. The percentages of farms within the ranges of 5 - 10 km were 10% - 20%. With exception of Jaffna, in other districts 7 - 21% farmers fell within 10 - 15 km while in Jaffna only 1% of farmers fell into this group. Likewise 18 - 39% of the farmers in Batticaloa, Ampari, Trincomalee and Vavuniya were farther than 15 km from the veterinary office.

Factors affecting adoption of AI

Landholding size and use of AI

The trend in use of AI with land holding size is shown in Table 11. The adoption of AI with respect to land holding size ranged from 1 to 21%. Except in the 'no land' category, the general trend was a reduction in adoption of AI with increase in land holding size.

Main occupation of the family and AI

Table 12 shows the change in the adoption of AI with main occupation of the family. Difference in the use of AI with respect to main occupation was significant (P< 0.01). It was observed that adoption of AI among the farmers belonging to the main occupation category of 'other' and 'agriculture' was greater than that of other categories. It should be noted that the adoption of AI among the farmers with a main occupation of 'animal husbandry' was less compared to the main occupation category of 'other' and 'agriculture'.

Distance of AI centre and use of AI

Table 13 shows the effect of distance of farm from the AI centre on the use of AI. A significant impact of distance of AI centre on the adoption of AI was observed (P < 0.01). There was a clear trend of a reduction in the adoption of AI with increased distance of the veterinary centre from the farm.

Family size and use of AI

Table 14 shows the effect of family on use of AI. With an increase in family size there was a reduction in the adoption of AI. However the chi-square test results revealed a non-significant effect of family size on adoption of AI.

Education level of the family and use of AI

Table 15 shows the impact of education level on the use of AI. The education level of the family was found to influence the use of AI significantly (P< 0.01). Adoption rate was substantially higher for the index groups of '1 to 2' and '>2' compared to index groups '0 and <1'.

Source of bull and use of AI

Table 16 shows the influence of the source of bulls on the use of AI. The source of bulls had a significant (P < 0.01) influence on the adoption of AI. There was an increase in the adoption of AI in the order of 'own bull', 'own bull and neighbour's bull' and 'neighbour's bull'. Other sources of bulls had little impact on the adoption of A.I.

Number of inseminations and use of AI

Table 17 shows the effect of number of inseminations on the use of AI. The number of inseminations had a significant impact on the use of AI (P<0.01).  The number of inseminations 0 implies the farmers have tried or have been trying AI but it was not successful yet or had not been successful at all, so that they could not tell the number of inseminations. The general trend was a reduction in adoption rate with increase in number inseminations per conception.

Purpose of rearing and use of AI

Table 18 shows the effect of rearing purpose on the use of AI. The purpose of rearing had a significant impact on use of AI (P < 0.01). Among the farmers adopting AI, the majority of them kept cattle for 'milk and manure'. The farmers who have been adopting AI had milk as one of their reasons for keeping cattle.

Discussion

Breeding activities

As far as the breeding activities were concerned, about 63% of the farmers depended on natural service to breed their animals while the rest depend on AI or a combination of AI and natural service. Based on the reasons listed for not using AI, there was much room to increase the number of farmers adopting AI. The major reasons listed were no knowledge about AI and no persuasion or advice. According to Girdhar (1967) the ignorance of the proper time of insemination and lack of knowledge about the advantages of AI were the resisting factors in the adoption of AI in Haryana. Similarly, Tyagi (1975) observed that the adoption of breeding practices by the farmers of the Intensive Cattle Development Project, Karnal was influenced by the herd size, knowledge, family education, farm size and sales of milk.

To detect heat the farmers generally depended on supplementary signs, rather than primary signs, and they did not have much knowledge about the various signs. This highlighted a need to raise awareness of both primary and secondary signs and their meanings, and help farmers understand these signs and to encourage them to act accordingly. Since the pregnancy diagnosis percentage was very low, and farmers just relied on the absence of heat signs to confirm conception, there was a considerable loss in the reproductive cycle. Knowledge that the use of pregnancy diagnosis confirms conception must be encouraged and promoted. Animals must be re-inseminated at the proper time, to prevent losses in reproductive cycle.

The distance from the veterinary office was one of the major factors determining the provision of services to the farmers. Less than 50% of the farmers fell within 1-5km from the veterinary office, except in Jaffna where 79% of farmers fell within this range. Zero - 3% of the farmers mentioned the ethnic crisis as one of the reasons for the selection of natural service. The reason for this is, if the farmer wants rely on AI for breeding their cows there should be proper transport and communication facilities to ensure that the insemination occurs on time.

Less than 1% of the farmers opted for AI to narrow down the calving interval, with most of the farmers adopting an extensive system of management which relied mostly on their own bull or a neighbour's bull for mating but if the bull is not available all the time then services will be delayed. If the farmers maintain a proper record and good infrastructure facilities are available, narrowing down the calving interval through AI is possible.

The ranking of the districts based on the number of farmers adopting AI were Jaffna, Ampari, Trincomalee, Vavuniya and Batticaloa. One of the causes for the ranking may be number of veterinary offices in each district. In Jaffna there are eight veterinary offices, therefore the distance from the veterinary office to the farm was less, so the response to the call may be quicker. In Ampari there were six veterinary offices and for Trincomalee, Batticaloa and Vavuniya the numbers were four, four and one respectively. When there are fewer veterinary offices, due to lack of communication and poor infrastructure facilities, the response is delayed or ignored. This reduces the number of farmers adopting AI. Even though there are some trained private AI technicians, the coverage is low and the charge is comparatively high compared to the veterinary office charges.

Main occupation of the family and AI

The reasons for the differences in the adoption of AI may be the time availability to go to the veterinary office, the number of animals belonging to each group, breed type and the interest of the farmer in AI.

Distance of AI centre and use of AI

These observations on distance of AI centre from the farm and the adoption rate of AI suggest that the distance of a farm from the AI centre is a critical factor affecting its uptake.

Education level of the family and use of AI

Index groups '0' and '<1' showed a low level of adoption. This may be due to the low level of knowledge about AI or that the number of farmers in these groups was low. For index group '1 to 2' and '>2' the percentage was 22% and 15% respectively. It may also be attributable to the higher number of farmers falling into these groups and a greater level of knowledge about AI. Compared to index group '1 to 2', index group '>2' had a lower level of adoption. This may be attributable to the tendency towards other jobs with an increase in education level. It explains why fewer farmers are in the index group '0' and '<1'. The majority of the farmers were in index group '1 to 2' and '>2' and among them a reasonable number of farmers were adopting AI. Extension or advice and persuasion via veterinary offices may bring more farmers to adopt AI.

Source of bull and use of AI

The source of bulls data show that the availability of bulls makes farmers depend mostly on natural service rather than on AI. The drawback of depending on natural service was that the bulls available are not genetically superior ones. Most of them are indigenous and have not been looked after properly. Dana (1994) designed a study to find out the association between the attitude of livestock owners towards AI in cattle and the availability of a local breeding bull, conception rate to AI and the availability of critical inputs. He also investigated the effect of different situations, viz availability of a local breeding bull, availability of critical inputs and conception rate of AI in cattle, on the attitude of livestock owners towards AI in the Bareilly district of Uttar Pradesh. The availability of critical inputs was significantly and positively correlated with the attitude of livestock owners towards AI in cattle. Availability of a local breeding bull and conception rate of AI in cattle was significantly and negatively correlated. The livestock owners having favorable attitudes were characterized by not having a local breeding bull in their villages and these livestock owners felt the high availability of critical inputs. Fewer inseminations required for a conception was one of the reasons for a favorable attitude towards AI in cattle, whereas a higher number of inseminations required for a conception frustrated the livestock owners and they tended to develop an unfavourable attitude towards AI.

Number of inseminations and use of AI

The results show that there is a possibility to increase the percentage of farmers adopting AI by taking action to reduce the number of inseminations required per conception. Singh and Singh (1993) studied the effect of various factors, such as size of land holding, main occupation of the family, extent of facilities available for AI, distance of AI centre, family size, educational level of the family, knowledge about AI and the AI centre, on the adoption of AI in cows under field conditions in the Bareilly district of Uttar Pradesh. An overall rate of adoption of AI in rural cows was found to be 61.3%. Adoption was comparatively low among those who have relatively more land. It increased with the increase in the AI facilities. Distance from an AI centre was found to have a significant impact on the adoption rate. It was not influenced by the family education and family size. Differences between AI centers was significant.

Purpose of rearing and use of AI

The percentage of farmers adopting AI was high when cattle were kept for 'milk and manure' (19%) and 'milk, meat, draught and manure' (8%). The general trend was that among the farmers adopting AI (37%) almost all the farmers had milk as one of the main purposes for rearing cattle.
 

Conclusion

• The percentage of farmers adopting natural service and AI differed significantly within districts and over all the districts. With the exception of Jaffna, the majority of the farmers in the other four districts used indigenous bulls to breed their animals.

• Except for farms with no land, an increase in land holding size was associated with a decrease in the adoption rate of AI.

• Family size had an impact on the adoption of AI.

• When the distance from the farm to veterinary office increased the adoption rate decreased.

• Education level did affect the use of AI.

• Availability of own bull and neighbor's bull affected the use of AI.

• With an increase in the number of inseminations required per conception, the adoption rate of AI rate decreased. Adoption of AI percentage was high amongst dairy cattle.
   

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