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Performance testing and recording in meat and dairy goats
Small Ruminant Research 60 (2005) 83–93
Performance testing and recording in meat and dairy goats夽 J.J. Olivier a , S.W.P. Cloete b,c,∗ , S.J. Schoeman b , C.J.C. Muller b,c b
a Irene Animal Improvement Institute, Private Bag X5013, Stellenbosch 7599, South Africa Department of Animal Sciences, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa c Elsenburg Agricultural Development Centre, Private Bag X1, Elsenburg 7607, South Africa
Available online 11 August 2005
Abstract This contribution reviews the global importance of goat farming in subsistence-based, market-based and high-input production systems and discusses performance recording and performance testing of meat and dairy goats with special reference to the South African environment. Environmental effects, genetic parameters and breeding strategies are considered. Marked progress has been made with performance testing and recording of meat and dairy goats in South Africa, but there is still ample scope for further improvements in the national improvement programmes. © 2005 Published by Elsevier B.V. Keywords: Environment; Growth; Reproduction; Milk yield; Butterfat; Protein; Breeding strategy
1. Background Goats contribute largely to the livelihoods of the livestock-keeping households of low- and medium input farmers, many of whom have few resources beyond their smallholdings and livestock. The keeping of goats is thus mainly concentrated in the developing areas of the world. Of approximately 740 million goats in the world, 95.8% are found in the developing world (Table 1). Of these, 43.6% are found in Asia, 29.2% in Africa, 21.7% in China and 1.3% in Central America. 夽 This paper is part of the special issue entitled Plenary Papers of
the 8th International Conference on Goats, Guest Edited by Professor Norman Casey. ∗ Corresponding author. E-mail address: [email protected] (S.W.P. Cloete). 0921-4488/$ – see front matter © 2005 Published by Elsevier B.V. doi:10.1016/j.smallrumres.2005.06.022
In the regional context, there were 219 million head of cattle, 194 million goats and 189 million sheep in 15 farming sub-systems in Africa. From 1970 to the present, goat numbers grew moderately by 2.3% per annum in this region. An important “improvement” objective of livestock development is thus to improve the stability of production – and thereby reduce risk – in order to increase the food security and well being of producer households (Livestock in Development, 1998). Within production systems where livestock may be communally managed or grazed on common properties, genetic improvement programmes have various constraints, for example low numbers per owner, difficulty to identify contemporary groups, free roaming males and early slaughter of males for cultural/financial purposes. These factors may have a detrimental effect on genetic improvement of production traits. The
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Table 1 Numbers of goats in different regions (FAO, 2004) Region
Goat numbers (millions)
Asia China Africa Central America Developed world
323.64 161.49 217.22 9.57 31.45
Total
743.37
development of the Dorper sheep and Boer goat in South Africa illustrate what can be achieved in the absence of these constraints (Olivier et al., 2002). A working group facilitated by the FAO (INRA and CIRAD, 2002) recognized three broad levels of needs and opportunities for better management and more sustainable breeding programmes in livestock production. These levels do not represent different production environments, but rather identify important differences in access to markets and resources. In discussing performance testing and recording, these different production levels must be recognized. The broader definitions of these levels were as follows: 1.1. Level 1—subsistence-based production The poorest of the world’s poor people comprise hundreds of millions of families existing on less than $1 per day. Input and output markets are not easily accessible, and producers have to rely on household resources. The major objective is thus to attain food security, to contribute to family insurance, and to reduce risk. Genetic improvement options are limited due to the scarcity of input factors, the prevailing production risk, and limited output options (Peters and Zambach, 2002). 1.2. Level 2—market-based production Opportunities for small- to medium-scale commercial animal production reflect global patterns of urbanisation, economic development, and globalisation. Under these conditions of increasing market linkages and external inputs, the livestock enterprise reduces the number of functions and objectives it serves in the system, and tends to specialise. First decisions for reducing diversity have to be made and the livestock system is gradually oriented to market opportunities.
Options for improving livestock performance and management initially depend on the availability and cost of inputs, but increasingly on skills and information. 1.3. Level 3—high-input production In high input production systems, economic issues of food quality, food safety, and respect for animal welfare and the environment are regarded as the main concerns. A growing market demand for specific livestock products and the associated demands for product quality together with the need to increase labour and land productivity will lead towards more specialised livestock systems. These factors require an increased capital input for yield increasing and labour saving technologies. The general objectives in high-input production systems generally emphasise high production output on an animal basis. However, growing external inputs (investments) increase production risks and demand necessitates the constant improvement of productivity and efficiency. The curbing of inputs costs through the selection of better-adapted livestock is therefore of equal importance. Considering these levels of production, it is mentionable that goat production in the developing countries is mostly aimed on meat production. In the developed countries, on the contrary, dairy production plays an important role (Table 2). More than 30% of the goats in the developed world are classified as dairy/milk, whereas less than 20% of the goats in the developing world are regarded as dairy goats. Dairy production on a per animal basis is correspondingly markedly higher in the developed countries compared to the developing countries (Table 3). It is important to note that most goat producers are unable to influence the price paid for their products through a process of value adding. For sustainable production animals have to produce more and at a lower cost. To achieve this, the hardiness, adaptabilTable 2 Number and percentage of dairy goats in developed countries compared to developing countries (FAO, 2004) Classification
No. of dairy goats (million)
No. of meat goats (million)
Dairy goats as % of total
Developed Developing
9.70 135.62
31.45 711.90
30.9 19.1
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Table 3 Total production and average production per animal in developed and developing countries (FAO, 2004)
Table 4 Least squares means of corrected 100-day weight (kg) of South African Boer goats as influenced by various non-genetic effects
Classification
Effect
Developed Developing a
Total production (million tonnes)
Production per head (kg)
Milk
Meata
Milk
Meata
2.60 9.25
0.19 3.77
267.6 68.2
12.2 12.3
Carcass weight.
ity and survival rate of animals are of the utmost importance. Performance recording programmes must therefore focus on systems to record these traits and to incorporate the information in a short and easily understandable report for primary producers. The number of traits to be measured is determined by the breeding objectives. In this paper only a few traits are considered. Depending on the level of production, other recording systems (in some cases more advanced) may be applicable.
2. Meat goats 2.1. Growth traits A recording system for Level 1 producers is costly and difficult to facilitate and breeding objectives will differ from that of other levels. When a farmer has one or two animals, he/she knows many features of each, and hence a performance report for an individual animal may not add much to what is already known (Olivier et al., 2002). However, if the performance of animals is reported in relation to all other animals in the village, the information becomes very relevant. Development of appropriate software that meets the information requirements of smallholders and a data flow procedure assuring fluent feedback of information relevant at the level of individual farms is thus very important. The genetic improvement of animals in Level 1 is normally through a governmental station where animals are performance tested or distributed by means of a nucleus-breeding scheme. A basic recording system must consist of at least one weight record per animal. Under most production circumstances, a weight recorded at weaning is feasible. As in the case with other weights, weaning weight is effected by various non-genetic factors. The effects of
Ewes
Rams
Birth Status
Singles Twins Triplets
20.02 17.66 18.85
22.58 19.43 19.02
Age of dam
Young Adult
17.54 18.86
19.58 21.06
such factors on 100-day age-corrected weaning weight in 11,679 South African Boer goat kids (descended from 386 sires) in the National Small Stock Improvement Scheme (NSSIS) are illustrated in Table 4. Dam age, gender and rearing status also had important effects on weaning weight. The heritability of weaning weight is comparatively low. Estimates derived from the above data were 0.16 ± 0.02 for direct heritability (h2 ) and 0.15 ± 0.014 for maternal permanent environmental effects (c2 ). The maternal additive variance (m2 ) was not calculated, since a lack of depth in maternal pedigrees was characteristic of the data (Maniates and Pollott, 2002). In the literature, parameter estimates for weaning weight in Boer goats were 0.18 for h2 , 0.05 for m2 , and 0.07 for c2 (Van Niekerk et al., 1996). A corresponding total h2 estimate of 0.32 was reported for weaning weight in Australian Boer goats (Ball et al., 2001). From the direct h2 estimates it can be expected that genetic progress in weaning weight will be slow. Post-weaning weight records of 645 animals were available from the NSSIS database, but this number of observations does not warrant an extended genetic analysis. Estimates of h2 in the post-weaning stage increased to 0.37 and to 0.45 by yearling age in Australian Boer goats (Ball et al., 2001). Direct genetic correlations between these traits exceeded 0.69, and were generally higher for adjacent weight measurements. In South African Afrino sheep, Snyman et al. (1995) found a very high genetic correlation of 0.96 between live weight at weaning weight and at 9 months of age. The direct heritability for these older weights was nearly double that of weaning weight. These data suggest that 7–10 months weight can be recorded and used in performance analysis if post-weaning growth rate is considered as a selection objective. Boer goat data recorded in Australia were in broad agreement
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Table 5 Example of the output document from a central testing programme operated in South Africa Animal ID
Starting weight (kg)
End weight (kg)
Average daily gain (gram per day)
Selection index %
Scrotum circumferencedev (cm)
87-99-0321 59-99-0023 142-99-0213
36.1 34.7 29.1
46.1 42.1 33.9
71.5 52.9 33.7
112 98 76
3 2 −1
Average
37.0
43.9
49.3
with this recommendation (Ball et al., 2001), and it seems reasonable to incorporate it in a basic recording system. Goat breeders are therefore encouraged to record a post-weaning weight if their ultimate objective is to increase direct growth rate in progeny. 2.2. Central testing of males The central testing of males on natural pasture can play an important role in evaluating males for growth traits (referred to as Veld ram tests in South Africa). Growth rate under natural grazing conditions can be associated with some fitness traits (Frisch, 1981). Due to age differences at the start of tests, as well as marked differences in live weight at the commencement of a trial, all tests have to conform to specified criteria. The testing period must exceed 140 days, following an adaptation period of at least 14 days. The difference in the initial live weights of all the males in a group is not allowed to exceed 12 kg (roughly 1 standard deviation), and all must have been born within a 60-day period. The minimum number of animals per test group is 20 and an average daily gain exceeding 50 g has to be achieved over the entire test period. In addition to starting weight and final weight at the end of the test, three additional weights are recorded. These weights are used to derive a regression of live weight on chronological age, depicting the growth of individual animals. Estimated initial weight and final weight are also derived. The average daily gain (ADG) of individuals is derived from these weights. Selection index (SI) theory is then used to calculate a value that combines growth rate and weight at the end of the test. This value is then presented as a percentage from the mean (SI%). At the end of the test, the scrotum circumference is also measured and displayed as a deviation from the mean. An example of the test results is given in Table 5. The correlation between results in a central test (under feedlot conditions) and progeny performance in
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a commercial environment was less than 2% for Suffolk in the Midwestern United States (Waldron et al., 1990). Concern that the same situation would prevail for Dorper rams tested in Veld ram clubs required further consideration. The progeny of three males with an SI of 22% higher than the mean of their contemporaries were compared to those of three low index contemporaries under extensive pasture conditions. They were mated to a total of 300 commercial females (150 to high and 150 to low index males), and the progeny were raised in a single flock. At weaning, the progeny of the high index males were 2.14 kg (9%) heavier than those of the low index ones. The average performance of Boer Goats in three different testing stations is given as illustration in Table 6. 2.2.1. Selection for reproduction rate Net reproduction rate, defined as total weight of kid weaned accumulated over the lifetime of a doe, is by far the most economically important trait in meat goat farming. Despite its importance, this trait is usually ignored during selection of replacements. On buck sales, conformation and breed standards are regarded by the broader industry as the most important traits. The reason for this is mainly due to the sex-limited character of reproduction, expression of this trait late in productive life, as well as the complex distribution of the trait. Moreover, low h2 estimates are commonly reported for net reproduction rate, although it does exhibit high levels of phenotypic variation. Marked variation exists in Table 6 Average weights of Boer goats tested in three different farms under natural grazing conditions for at least 140 days Test location
Initial weight (kg)
Final weight (kg)
ADG (g/day)
Farm 1 Farm 2 Farm 3
31.4 33.0 34.2
45.2 44.6 51.9
81.2 63.3 71.4
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Table 7 Reproductive performance of the best and poorest third of the available ewes on two farms, as measured over four productive years Farm Aa
Trait recorded
Farm Ba
Best (28 Ewes) Number of lambs born per ewe per productive year Number of lambs weaned per ewe per productive year Total weight of lambs weaned per ewe per productive year Average weaning weight index of lambs a
1.41 1.40 42.2
Poor (28 Ewes) 0.75 0.41 12.4
102
Best (23 Ewes)
Poor (23 Ewes)
1.29 1.23 32.6
101
0.80 0.61 15.1
105
102
Farm A: rams with ewes constantly, Farm B: mated for 6 weeks annually.
net reproduction rate, with coefficients of variation generally exceeding 40%, as illustrated in Table 7 using sheep as an example. In comparison to the poorest ewes, the best had more lambs, which survived better and weighed more at weaning. The economical superiority of the best females over their poorest contemporaries is obvious. The bottom line is: there are not only some superior females in every flock, but also some very uneconomical ones. In a Merino flock divergently selected for multiple rearing-ability, since 1986 Cloete et al. (2004) reported an annual increase of 0.392 kg (or 1.8% of the overall mean) for weight of lamb weaned per ewe joined in a line selected for the reproductive performance of their dams (H line). The corresponding reduction in the performance of the corresponding line selected in the downward direction (L line) amounted to 0.265 kg (1.3% of the overall mean). These responses led to respective phenotypic means after 17 years of selection of 25 kg of lamb weaned per ewe joined in the H line, compared to 12 kg in the L line. The H line ewes had more multiple lambs, but also lower levels of preweaning mortalities (31% in L line compared to 23% in H line). Ewes in the H line also weaned significantly heavier lambs. These responses to a designed experiment accorded with the results presented in Table 7. Stemming from this, it is argued that net reproduction rate can be increased in a selection programmes despite its low heritability, and that a reasonable response of up to 2% per annum (similar to other production traits) could be obtained in small ruminants.
Marked within flock variation is available which can be utilized for current and future generation gains. In the South African NSSIS, weaning weight is used to calculate total weight of lamb or kid weaned. In addition, the females that did not lamb or kid or that had stillbirths also need to be recorded. Weaning weight is corrected for age and sex and then accumulated over a females’ lifetime. Then the total weight of lambs weaned is regressed for number of parities and expressed as a deviation from the mean value of her contemporaries. In addition, the average weaning weight of all her lambs is also calculated. An example of a reproduction output is given in Table 8. The first ewe listed had a good performance, with seven lambs weaned with an average weaning index of 104. The second ewe is also prolific and had nine lambs in total. The average weaning index of 81 for her lambs suggested poor mothering ability, resulting in the weaning of inferior lambs. The last ewe listed weaned four lambs in total, but under-performed relative to her contemporaries. 2.2.2. Other traits Improvement of muscling and reducing fat in goat carcasses have not received the attention it warrants, especially for level 3 high-input production systems. The value of ultrasound technology to predict carcass characteristics, as an example, is widely used in beef, pork and lamb. Carr et al. (2002) reported a positive correlation (0.67) between live weight and muscle measurements in Boer goat crosses. Carr et al. (2002) concluded that prediction of carcass fat by means of
Table 8 Example of output for reproductive performance from the South African national Small Stock Improvement Scheme Ewe ID
Productive years
Number of lambs born
Number of lambs weaned
Ewe productivity index
Mean index of lambs
ABF-96-0231 ABF-99-0104 ABF-99-0630
5 5 5
7 9 7
7 7 4
17 1 −14
104 81 89
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ultrasound was not useful. This technology should be further tested and investigated. Improvement in growth and live weight seems to be positively correlated to muscle measurements. As illustrated in Table 2, the majority of goats are found in the developing countries. With limited resources, the adaptability and hardiness of goats are very important traits. Goats are susceptible to gastrointestinal nematode infection, but evidence of their genetic resistance is limited. Baker et al. (2001) reported that small East African goats were more resistant to nematode parasites than Galla kids, as shown by their significantly lower faecal egg count (FEC) in the post-weaning period (8- to 14-month-old kids) and lower mortality from birth to 14 months of age. Heritability estimates for records taken at 4.5 and 8 months of age from a repeated measures analysis were 0.18 (s.e. 0.08) for PCV and 0.13 (s.e. 0.07) for logarithmtransformed FEC (Baker et al., 2001). Mandonnet et al. (2001) reported a heritability of 0.37 ± 0.06 for transformed FEC at weaning in Creole kids.
3. Dairy goats 3.1. History of dairy goat production and milk recording in South Africa Next to dairy cattle, sheep and goats are the most important milk-producing animals in both the temperate and tropical environments of the world (Devendra and Coop, 1982). The population of dairy goats in the world has increased over the last three decades. Dairy goats were mainly kept for household purposes in the past. The reason for keeping dairy goats, however, has changed to a large extent. In South Africa there is a growing demand for goat milk products mainly for the tourist industry. There is also a large demand for dairy goat milk products for people with health problems. The dairy goat industry in South Africa probably started before the arrival of Europeans in this country. Goats similar to those of Nubian or Egyptian origin were encountered among the Namaqua people in the vicinity of the Olifants River. During the early years very little was done to improve the genetic quality of the local dairy goats. The Cape Agricultural Department imported three Saanen bucks and 12 does from (probably) Switzerland in 1898. Most of the present-
Fig. 1. Number of lactation records in the South African milk goat recording scheme according to year of kidding from 1956 until 2001.
day Saanen goats in South Africa originated from two bucks and 15 does that were imported from Switzerland in 1903. Dairy goats in South Africa are included with dairy cattle in the National Dairy Animal Improvement Scheme. Thus, the milk yield of does is measured over a 300-day lactation period from 10 monthly tests of the amount of milk produced at two or more milking per 24-h period. Milk samples are collected from each doe and analysed for fat and protein content. Initially only the fat content of the milk was determined at these monthly tests. Milk production records (Fig. 1) of dairy goats are available from the 1957/58-production year when a number of mostly first lactation does were recorded. For all practical purposes, the 1981/82-production year is regarded as the first year of milk recording for dairy goats, as the number of lactation records was low and variable before that year. At present, milk recording is done every 5 weeks and eight tests are needed for a lactation record of the milk, fat and protein yield, as well as fat, protein and lactose content for each doe. The milk yield and milk composition of dairy animals are influenced by a large number of factors. In South Africa registered and non-registered dairy goats produce milk under a wide range of climatic conditions. Goats are both grazers and browsers, making them well adapted to survive and produce under a wide range of production systems. Donkin and Boyazoglu (2000) noted that Saanen goats could produce more than 700 kg of milk over a lactation period of 288 days, while the milk yield of local (or indigenous) goats could be as little as 23 kg over a lactation period of less than 100 days. Indigenous animals, however, produce milk
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Table 9 Number of lactation records available for all production years and number of records removed from the data sets of the different breeds Parameters
Dairy goat breeds Saanen
British Alpine
Toggenburg
Number of lactations
14688
560
900
Records removed Milk = 0 Lactation period (>304 days) Lactation period (<60 days) Lactation number >8 No lactation record Lactation record >2500 kg Usable records
594 72 262 33 3558 5 10164
25 4 2 1 174 – 354
50 1 12 2 372 – 463
from the natural herbage that is of poor quality for most of the year, while also rearing one or more kids. The number of records available for the different dairy goat breeds is presented in Table 9. The data set of the Toggenburg breed had 372 records (41% of all does) with no lactation information, while the data set of the British Alpine breed had 174 records (31% of all ewes) with no lactation information. The number of usable records for the British Alpine and Toggenburg breeds was 63 and 51% of the original number of lactations respectively. In the Saanen breed 24% of all lactations (3557 of 14,688 does) had no lactation records. Almost 70% (10,164) of Saanen lactations had production records conforming to the required criteria. Production parameters for Saanen, British Alpine and Toggenburg dairy goats are very similar in the national database, although fat content of Saanen goat milk is generally lower than that of the other two breeds. The large variation in the number of records between the different breeds also makes comparisons very difficult (Fig. 2). The owner-sampling method used in South Africa has resulted in a marked increase in the number of dairy goats participating in milk recording (Hallowell et al., 2000).
Fig. 2. Average lactation milk yield records for the dominant dairy goat breeds in South Africa for the period 1956 to 2002.
rear all replacements of cows or goats in the herd. Dairy goat farmers generally believe that once a year kidding improves productive life. Lactation records were divided from first parity to eighth parity (Fig. 3). The average number of completed lactations for Saanen, British Alpine and Toggenburg dairy goat does was only 2.35, 2.02 and 2.24, respectively. Between 34 and 48% of animals with a first lactation records progressed to a second lactation. The smaller number of lactation records with increasing lactation number is typical of the erosion of animals as is also seen in the dairy cattle industry. Du Toit (1994) and Hallowell (1994) found for Jersey and for Ayrshire cows that first and second lactation records accounted for 57 and 58%, respectively, of all records up to the tenth lactation. A similar pattern has been observed in other countries. Milk and component yields generally increased from first to second and third parity (Fig. 4). This increase was followed with a general decline in yield traits for the later parities. A maximum milk yield of
3.2. Factors affecting milk production in South African dairy goats 3.2.1. Parity The longevity of dairy animals is associated positively with profit margins in dairy herds. A longer average herd life of dairy animals reduces the need to
Fig. 3. The number of animals with second and subsequent lactations, expressed as a proportion of the number of animals with a first lactation record.
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J.J. Olivier et al. / Small Ruminant Research 60 (2005) 83–93 Table 10 The number of records and average production parameters of grade and registered Saanen goats in the national herd
Fig. 4. Means for milk, butterfat and protein yields with an increase in lactation number. Means were derived from analyses conducted by Muller et al. (2002). Vertical bars about the mean represent standard errors on the graph.
approximately 900 kg per lactation was recorded at the second and third parities. Milk production subsequently declined to approximately 800 kg per lactation. During the period from 1981 to 1999 some 75 dairy goat owners had herds with animals with completed lactation records. Unfortunately, more than 56% of these herds were small with fewer than 30 lactation records in each herd. Only nine herds (14% of all herds) had more than 100 lactation records each. One herd had more than 6400 lactation records. Almost 70% of all records in the national database were from three herds only. The implication of this is that a genetic analysis of specifically the Saanen dairy goat breed in South Africa would essentially be based on these three herds. 3.2.2. Length of lactation period Lactation records of Saanen dairy goats varied from zero to 420 days in milk. The general consensus is that all lactation records irrespective of number of days in milk should be included in an analysis to counter any bias in genetic evaluations that may occur (Norman et al., 1985; Rege, 1991). The average number of days in milk for does in the Saanen data set was 246 ± 68 days. The coefficient of variation for days in milk exceeds 28% showing a large variation in number of days in milk. The average production is displayed in Table 10. 3.3. Bucks The value of bucks in a dairy goat herd is much more than getting does pregnant and in production. They could have a positive or negative effect on the genetic merit of a herd. Bucks used in the last three generations
Production parameters
Registered
Grade
Number of records Average number of lactation days Milk (kg) Fat (kg) Protein (kg) Fat (%) Protein (%)
2437 276 940.1 28.4 25.6 3.03 2.73
6354 260 841.1 25.9 22.8 3.14 3.14
of a herd have a genetic contribution of 87% in the current herd. No progeny testing of bucks for use through AI or natural mating is being conducted for dairy goats in South Africa. Generally breeders evaluate their own bucks to be used in their herds, based on pedigrees and probably some production figures. While the environmental conditions of dairy goat production in South Africa can be described very well, little is known of the genetic merit of these animals. To increase the milk yield of goats in both the rural areas and in intensive production systems, the milk yield potential of the bucks intended for mating should at least be known. The selection of dairy goats would also be improved in commercial herds resulting in higher efficiency. 3.4. Genetic parameter estimates for diary goats Heritability (h2 ) estimates for yield traits in dairy goats are in the medium to high range (Table 11), and genetic progress should be achievable in a wellstructured breeding programme. Repeatability (t) estimates were higher still, and current herd gains could thus be expected. Marked variation is found in h2 and t estimates for the percentage traits, figures ranging from less than 0.2 to more than 0.8. Judged from experience with dairy cattle, it is expected that h2 estimates for the percentage traits should exceed those of yield traits. The greater variation in h2 estimates in dairy goats for the percentage traits seems to justify further studies on this topic. This result could possibly be attributed to the small size of goat herds used for parameter estimation. Genetic correlations among yield traits are high, ranging from 0.57 to 0.89 (Morris et al., 1997; Ilahi et al., 2000; Muller et al., 2002). Correlations of milk
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Table 11 Literature estimates of heritability (h2 ) and repeatability (t) for milk traits in dairy goats Trait and breed
h2
Milk yield Alpine and Saanen Murcia-Granada Saanen Alpine and Saanen Alpine Skopelos Saanen
0.35 0.17–0.18 0.23 0.21 0.35 0.14–0.28 0.23
Butterfat yield Alpine and Saanen Alpine Saanen
0.32 0.38 0.22
Protein yield Alpine and Saanen Alpine Saanen
0.36 0.33 0.20
Butterfat % Murcia-Granada Alpine and Saanen Alpine Saanen
0.14–0.16 0.16 0.72 0.21
0.33–0.36
Protein % Murcia-Granada Alpine and Saanen Alpine Saanen
0.22–0.25 0.16 0.75 0.44
0.41–0.47
Protein + Fat % Saanen
0.27
t
Country
Reference
0.41
France Spain New Zealand Slovenia France Greece South Africa
Boichard et al. (1989) Analla et al. (1996) Morris et al. (1997) Breznik et al. (1999) Ilahi et al. (2000) Kominakis et al. (2000) Muller et al. (2002)
0.56 0.38
France South Africa
Boichard et al. (1989) Ilahi et al. (2000) Muller et al. (2002)
0.52
France France South Africa
Boichard et al. (1989) Ilahi et al. (2000) Muller et al. (2002)
Spain Slovenia France South Africa
Analla et al. (1996) Breznik et al. (1999) Ilahi et al. (2000) Muller et al. (2002)
0.82 0.63
Spain Slovenia France South Africa
Analla et al. (1996) Breznik et al. (1999) Ilahi et al. (2000) Muller et al. (2002)
0.27
New Zealand
Morris et al. (1997)
0.36–0.39 0.26 0.54
0.81 0.48
yield with butterfat percentage were mostly negative in sign and moderate to high in magnitude, ranging from −0.12 to −0.89 for butterfat and from −0.24 to −0.65 for protein percentage (Analla et al., 1996; Ilahi et al., 2000; Muller et al., 2002). Butterfat and protein percentages are generally positively related on a genetic level, with estimates ranging from 0.34 to 0.59 (Ilahi et al., 2000; Muller et al., 2002). 3.5. Other traits One of the traits related to lower input costs is disease resistance. In most herds, the reduction of parasitic challenge is important. The h2 of nematode FEC in New Zealand Saanen goats were estimated at 0.15, suggesting a possibility of selection for a reduced input cost in the breed (Morris et al., 1997). Genetic correla-
tions with milk yield (−0.21) as well as butterfat and protein contents (−0.17) were negative, but not significant. Phenotypic correlations were low, at −0.07 in both instances. An increased milk speed is likely to facilitate the milk winning process. Milk speed of Alpine does was highly heritable at 0.65 (Ilahi et al., 2000), with a repeatability of 0.82. Segregation analyses strongly suggested that the trait was likely to be affected by a major gene, accounting for 63% of the total genetic variation in milking speed. The underlying biological mechanism of the major gene effect is, however, still unclear. More research on relationships of milking speed with udder and teat characteristics as well as somatic cell counts is required before this trait can seriously be considered in selection programmes (Ilahi et al., 2000).
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Linear type traits in Saanen and Alpine goats were heritable (Manfredi et al., 2001; Wiggans and Hubbard, 2001). Estimates for thorax diameter in the former study were high (0.41 to 0.50), those for udder and teat traits moderate to high (0.15 to 0.52) and those for feet and leg traits low (0.05 to 0.16). Most correlations between breeding values for milk yield and type traits were low (Manfredi et al., 2001). The exception was unfavourable correlations between milk yield and udder attachment traits. 4. Conclusions From the foregoing, it is clear that progress is being made with the performance testing and recording of meat and milk goats in South Africa. It is also clear that there are still many gaps in the existing knowledge, and that the recording schemes are still deficient in many instances. A successful recording system must be based not only on the selection goal (genetics has long-term results, especially in harsh conditions), but must also foresee extension services as a means to send a technician to the herd and to give advice to the farmers (Moioli et al., 2000). Future developments should focus on the deliverance of an affordable and practical scheme aimed at the requirements of both small scale and commercial goat producers. The effectiveness of such a recording system must be measurable by means of the genetic trend(s) measured in one population for the trait(s) in the defined breeding goal after a certain number of years of activity. Secondly, the success of a recording system can also be measured by the costs stood in for by private farmers. Especially in low to medium-input production systems, the integration of genetic improvement programmes with other livestock improvement activities must include the management of the environment, animal health as well as product preparation for market and practical training (Olivier et al., 2002). References Analla, M., Jim´enez-Gamero, I., Mu˜noz-Serrano, A., Serradilla, J.M., Falag´an, A., 1996. Estimation of genetic parameters for milk yield and fat and protein contents of milk from MurcianoGranadina goats. J. Dairy Sci. 79, 1895–1898. Baker, R.L., Audho, J.O., Aduda, E.O., Thorpe, W., 2001. Genetic resistance to gastro-intestinal nematode parasites in Galla and
small East African goats in the sub-humid tropics. Anim. Sci. 73, 61–70. Ball, A.J., Brown, D.J., Spiker, S.A., Field, S.R., Banks, R.B., 2001. Opportunities for genetic development of the Boer Goat in Australia using Kidplan. Proc. Assoc. Adv. Anim. Breed. Gen. 14, 445–448. Boichard, D., Bouloc, N., Ricordeau, G., Piacere, A., Barillet, F., 1989. Genetic parameters of first lactation dairy traits in tha Alpine and Saanen goat breeds. Genet. Sel. E 21, 205–215. Breznik, S., Kovac, M., Kompan, D., Birtic, D., Barilet, F., Zervas, N.P., 1999. Genetic evaluation of goat milk traits with a test-day model. In: Milking and milk production of dairy sheep and goats. Proceedings of the Sixth International Symposium on Milk. Small Rumin., EAAP Publication No. 95, 427–429, 26 September to 1 October 1998, Athens, Greece. Carr, M.A., Waldron, D.F., Willingham, T.D., 2002. Relationships among weights, ultrasound and carcass characteristics in Boercross goats. Texas Agric. Exp. Sta. Res. Rep., pp. 55–59. Cloete, S.W.P., Gilmour, A.R., Olivier, J.J., Van Wyk, J.B., 2004. Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and live weight in Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric. 44, 745–754. Devendra, C., Coop, I.E., 1982. Ecology and distribution. In: Coop, I.E. (Ed.), World animal science series C1 production system approach: sheep and goat production. Elsevier, Amsterdam, pp. 1–14. Donkin, E.F., Boyazoglu, P.A., 2000. Milk production from goats for household and small-scale farmers in South Africa. In: Proceedings of the Seventh International Conference on Goats, vol 1, Tours, France, pp. 324–328. Du Toit, J., 1994. A genetic evaluation of the South African Jersey breed. M.Sc. thesis, University of the Orange Free State, Bloemfontein, South Africa. FAO, 2004. Available at the following website: http://www.fao.org/ waicent/portal/statistics en.asp. Accessed during February 2004. Frisch, J.E., 1981. Changes occurring in cattle as a consequence of selection for growth rate in a stressful environment. J. Agric. Sci. Camb. 96, 23–38. Hallowell, G.J., 1994. Genetic parameter estimates for the South African Ayrshire breed. M.Sc. thesis, University of the Orange Free State, Bloemfontein, South Africa. Hallowell, G.J., Moore, S.J., Mostert, B.E., 2000. Annual report. South African national dairy animal improvement scheme, vol. 20, 17–18. Ilahi, H., Manfredi, E., Chastin, P., Monod, F., Elsen, J.M., Le Roy, P., 2000. Genetic variability in milking speed of dairy goats. Genet. Res. Camb. 75, 315–319. INRA and CIRAD, 2002. Proceedings of the Seventh World Congress on Genetics Application and Livestock Production, Montpellier. 19–23 August. CD-ROM. INRA, Toulouse. Livestock in Development, 1998. Sustainable rural livelihoods: What contribution can we make? London: Department for International Development. Kominakis, A., Rogdakis, E., Vasiloudis, C., Liaskos, O., 2000. Genetic and environmental sources of variation of milk yield in skopolos dairy goats. Small Rumin. Res. 36, 1–5.
J.J. Olivier et al. / Small Ruminant Research 60 (2005) 83–93 Mandonnet, N., Aumont, G., Fleury, J., Arquet, R., Varo, H., Gruner, L., Bouix, J., Vu-Tien-Khang, J., 2001. Assessment of genetic variability of resistance to gastrointestinal nematode parasites in Creole goats in the humid tropics. J. Anim. Sci. 79, 1706–1712. Manfredi, E., Piacere, A., Lahaye, P., Ducrocq, V., 2001. Genetic parameters of type appraisal in Saanen and Alpine goats. Livest. Prod. Sci. 70, 183–189. Maniates, N., Pollott, G.E., 2002. The impact of data structure on genetic (co)variance components of early growth in sheep, estimated using an animal model with maternal effects. J. Anim. Sci. 81, 101–108. Moioli, B., Astruc J.-M., Sanna S., 2000. Successful establishment of small ruminant recording systems in the Mediterranean countries. In: Development of Successful Animal Recording Systems for Transition and Developing Countries. Proceedings of the FAO/ICAR Seminar, Interlaken, Switzerland, 27 May 2002. pp. 155. Morris, C.A., Wheeler, M., Hosking, B.C., Watson, T.G., Hurford, A.P., Foote, B.J., Foote, J.F., 1997. Genetic parameters for milk yield and faecal egg count in Saanen does. N.Z. J. Agric. Res. 40, 523–528. Muller, C.J.C., Cloete, S.W.P., Schoeman, S.J., 2002. Estimation of genetic parameters for milk yield and composition of South African Saanen goats. In: Proceedings of the Seventh World Congress on Genetics Application and Livestock Production, vol. 29, 18–23 August, Montpellier, France, pp. 259–262. Norman, H.D., Dickenson, F.N., Wright, J.R., 1985. Merit of extending completed records of less than 305 days. J. Dairy Sci. 68, 2645–2654.
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Olivier, J.J., Moyo, S., Montaldo, H.H., Thorpe, W., Valle Zarate, A., Trivedi, K.R., 2002. Iintegrating genetic improvement into livestock development in medium- to low-input production systems. In: Proceedings of the Seventh World Congress on Genetics Applications and Livestock Production, vol. 25, 18–23 August, Montpellier, France, p. 15. Peters K.J., Zambach, B., 2002. Needs for research and development in Livestock Recording Systems (LRS) in transition and developing countries. In Development of Successful Animal Recording Systems for Transition and Developing Countries Proceedings of the FAO/ICAR Seminar held in Interlaken, Switzerland, 27 May 2002, 155. Rege, J.E.O., 1991. Genetic analysis of reproduction and productive performance of cattle in Kenya. J. Anim. Breed. Gen. 108, 412–423. Snyman, M.A., Erasmus, G.J., van Wyk, J.B., 1995. Direct and maternal (co)variance components and heritability estimates for body weight at different ages and fleece traits in Afrino sheep. Livest. Prod. Sci. 44, 229–235. Van Niekerk, M.M., Schoeman, S.J., Botha, M.A., Casey, N., 1996. Heritability estimates for pre-weaning growth traits in the Adelaide Boer goat herd. S. Afr. J. Anim. Sci. 26, 6–10. Waldron, D.F., Thomas, D.L., Stookey, J.M., Nash, T.G., McKeith, F.K., Fernando, R.L., 1990. Central ram tests in the midwestern United States. III. Relationship between sire’s central test performance and progeny performance. J. Anim. Sci. 68, 45–53. Wiggans, G.R., Hubbard, S.M., 2001. Genetic evaluation of yield and type traits in the genetic evaluation of dairy goats in the United States. J. Dairy Sci. 84 (E. Suppl.), E69–E73.
Performance testing and recording in meat and dairy goats夽 J.J. Olivier a , S.W.P. Cloete b,c,∗ , S.J. Schoeman b , C.J.C. Muller b,c b
a Irene Animal Improvement Institute, Private Bag X5013, Stellenbosch 7599, South Africa Department of Animal Sciences, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa c Elsenburg Agricultural Development Centre, Private Bag X1, Elsenburg 7607, South Africa
Available online 11 August 2005
Abstract This contribution reviews the global importance of goat farming in subsistence-based, market-based and high-input production systems and discusses performance recording and performance testing of meat and dairy goats with special reference to the South African environment. Environmental effects, genetic parameters and breeding strategies are considered. Marked progress has been made with performance testing and recording of meat and dairy goats in South Africa, but there is still ample scope for further improvements in the national improvement programmes. © 2005 Published by Elsevier B.V. Keywords: Environment; Growth; Reproduction; Milk yield; Butterfat; Protein; Breeding strategy
1. Background Goats contribute largely to the livelihoods of the livestock-keeping households of low- and medium input farmers, many of whom have few resources beyond their smallholdings and livestock. The keeping of goats is thus mainly concentrated in the developing areas of the world. Of approximately 740 million goats in the world, 95.8% are found in the developing world (Table 1). Of these, 43.6% are found in Asia, 29.2% in Africa, 21.7% in China and 1.3% in Central America. 夽 This paper is part of the special issue entitled Plenary Papers of
the 8th International Conference on Goats, Guest Edited by Professor Norman Casey. ∗ Corresponding author. E-mail address: [email protected] (S.W.P. Cloete). 0921-4488/$ – see front matter © 2005 Published by Elsevier B.V. doi:10.1016/j.smallrumres.2005.06.022
In the regional context, there were 219 million head of cattle, 194 million goats and 189 million sheep in 15 farming sub-systems in Africa. From 1970 to the present, goat numbers grew moderately by 2.3% per annum in this region. An important “improvement” objective of livestock development is thus to improve the stability of production – and thereby reduce risk – in order to increase the food security and well being of producer households (Livestock in Development, 1998). Within production systems where livestock may be communally managed or grazed on common properties, genetic improvement programmes have various constraints, for example low numbers per owner, difficulty to identify contemporary groups, free roaming males and early slaughter of males for cultural/financial purposes. These factors may have a detrimental effect on genetic improvement of production traits. The
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Table 1 Numbers of goats in different regions (FAO, 2004) Region
Goat numbers (millions)
Asia China Africa Central America Developed world
323.64 161.49 217.22 9.57 31.45
Total
743.37
development of the Dorper sheep and Boer goat in South Africa illustrate what can be achieved in the absence of these constraints (Olivier et al., 2002). A working group facilitated by the FAO (INRA and CIRAD, 2002) recognized three broad levels of needs and opportunities for better management and more sustainable breeding programmes in livestock production. These levels do not represent different production environments, but rather identify important differences in access to markets and resources. In discussing performance testing and recording, these different production levels must be recognized. The broader definitions of these levels were as follows: 1.1. Level 1—subsistence-based production The poorest of the world’s poor people comprise hundreds of millions of families existing on less than $1 per day. Input and output markets are not easily accessible, and producers have to rely on household resources. The major objective is thus to attain food security, to contribute to family insurance, and to reduce risk. Genetic improvement options are limited due to the scarcity of input factors, the prevailing production risk, and limited output options (Peters and Zambach, 2002). 1.2. Level 2—market-based production Opportunities for small- to medium-scale commercial animal production reflect global patterns of urbanisation, economic development, and globalisation. Under these conditions of increasing market linkages and external inputs, the livestock enterprise reduces the number of functions and objectives it serves in the system, and tends to specialise. First decisions for reducing diversity have to be made and the livestock system is gradually oriented to market opportunities.
Options for improving livestock performance and management initially depend on the availability and cost of inputs, but increasingly on skills and information. 1.3. Level 3—high-input production In high input production systems, economic issues of food quality, food safety, and respect for animal welfare and the environment are regarded as the main concerns. A growing market demand for specific livestock products and the associated demands for product quality together with the need to increase labour and land productivity will lead towards more specialised livestock systems. These factors require an increased capital input for yield increasing and labour saving technologies. The general objectives in high-input production systems generally emphasise high production output on an animal basis. However, growing external inputs (investments) increase production risks and demand necessitates the constant improvement of productivity and efficiency. The curbing of inputs costs through the selection of better-adapted livestock is therefore of equal importance. Considering these levels of production, it is mentionable that goat production in the developing countries is mostly aimed on meat production. In the developed countries, on the contrary, dairy production plays an important role (Table 2). More than 30% of the goats in the developed world are classified as dairy/milk, whereas less than 20% of the goats in the developing world are regarded as dairy goats. Dairy production on a per animal basis is correspondingly markedly higher in the developed countries compared to the developing countries (Table 3). It is important to note that most goat producers are unable to influence the price paid for their products through a process of value adding. For sustainable production animals have to produce more and at a lower cost. To achieve this, the hardiness, adaptabilTable 2 Number and percentage of dairy goats in developed countries compared to developing countries (FAO, 2004) Classification
No. of dairy goats (million)
No. of meat goats (million)
Dairy goats as % of total
Developed Developing
9.70 135.62
31.45 711.90
30.9 19.1
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Table 3 Total production and average production per animal in developed and developing countries (FAO, 2004)
Table 4 Least squares means of corrected 100-day weight (kg) of South African Boer goats as influenced by various non-genetic effects
Classification
Effect
Developed Developing a
Total production (million tonnes)
Production per head (kg)
Milk
Meata
Milk
Meata
2.60 9.25
0.19 3.77
267.6 68.2
12.2 12.3
Carcass weight.
ity and survival rate of animals are of the utmost importance. Performance recording programmes must therefore focus on systems to record these traits and to incorporate the information in a short and easily understandable report for primary producers. The number of traits to be measured is determined by the breeding objectives. In this paper only a few traits are considered. Depending on the level of production, other recording systems (in some cases more advanced) may be applicable.
2. Meat goats 2.1. Growth traits A recording system for Level 1 producers is costly and difficult to facilitate and breeding objectives will differ from that of other levels. When a farmer has one or two animals, he/she knows many features of each, and hence a performance report for an individual animal may not add much to what is already known (Olivier et al., 2002). However, if the performance of animals is reported in relation to all other animals in the village, the information becomes very relevant. Development of appropriate software that meets the information requirements of smallholders and a data flow procedure assuring fluent feedback of information relevant at the level of individual farms is thus very important. The genetic improvement of animals in Level 1 is normally through a governmental station where animals are performance tested or distributed by means of a nucleus-breeding scheme. A basic recording system must consist of at least one weight record per animal. Under most production circumstances, a weight recorded at weaning is feasible. As in the case with other weights, weaning weight is effected by various non-genetic factors. The effects of
Ewes
Rams
Birth Status
Singles Twins Triplets
20.02 17.66 18.85
22.58 19.43 19.02
Age of dam
Young Adult
17.54 18.86
19.58 21.06
such factors on 100-day age-corrected weaning weight in 11,679 South African Boer goat kids (descended from 386 sires) in the National Small Stock Improvement Scheme (NSSIS) are illustrated in Table 4. Dam age, gender and rearing status also had important effects on weaning weight. The heritability of weaning weight is comparatively low. Estimates derived from the above data were 0.16 ± 0.02 for direct heritability (h2 ) and 0.15 ± 0.014 for maternal permanent environmental effects (c2 ). The maternal additive variance (m2 ) was not calculated, since a lack of depth in maternal pedigrees was characteristic of the data (Maniates and Pollott, 2002). In the literature, parameter estimates for weaning weight in Boer goats were 0.18 for h2 , 0.05 for m2 , and 0.07 for c2 (Van Niekerk et al., 1996). A corresponding total h2 estimate of 0.32 was reported for weaning weight in Australian Boer goats (Ball et al., 2001). From the direct h2 estimates it can be expected that genetic progress in weaning weight will be slow. Post-weaning weight records of 645 animals were available from the NSSIS database, but this number of observations does not warrant an extended genetic analysis. Estimates of h2 in the post-weaning stage increased to 0.37 and to 0.45 by yearling age in Australian Boer goats (Ball et al., 2001). Direct genetic correlations between these traits exceeded 0.69, and were generally higher for adjacent weight measurements. In South African Afrino sheep, Snyman et al. (1995) found a very high genetic correlation of 0.96 between live weight at weaning weight and at 9 months of age. The direct heritability for these older weights was nearly double that of weaning weight. These data suggest that 7–10 months weight can be recorded and used in performance analysis if post-weaning growth rate is considered as a selection objective. Boer goat data recorded in Australia were in broad agreement
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Table 5 Example of the output document from a central testing programme operated in South Africa Animal ID
Starting weight (kg)
End weight (kg)
Average daily gain (gram per day)
Selection index %
Scrotum circumferencedev (cm)
87-99-0321 59-99-0023 142-99-0213
36.1 34.7 29.1
46.1 42.1 33.9
71.5 52.9 33.7
112 98 76
3 2 −1
Average
37.0
43.9
49.3
with this recommendation (Ball et al., 2001), and it seems reasonable to incorporate it in a basic recording system. Goat breeders are therefore encouraged to record a post-weaning weight if their ultimate objective is to increase direct growth rate in progeny. 2.2. Central testing of males The central testing of males on natural pasture can play an important role in evaluating males for growth traits (referred to as Veld ram tests in South Africa). Growth rate under natural grazing conditions can be associated with some fitness traits (Frisch, 1981). Due to age differences at the start of tests, as well as marked differences in live weight at the commencement of a trial, all tests have to conform to specified criteria. The testing period must exceed 140 days, following an adaptation period of at least 14 days. The difference in the initial live weights of all the males in a group is not allowed to exceed 12 kg (roughly 1 standard deviation), and all must have been born within a 60-day period. The minimum number of animals per test group is 20 and an average daily gain exceeding 50 g has to be achieved over the entire test period. In addition to starting weight and final weight at the end of the test, three additional weights are recorded. These weights are used to derive a regression of live weight on chronological age, depicting the growth of individual animals. Estimated initial weight and final weight are also derived. The average daily gain (ADG) of individuals is derived from these weights. Selection index (SI) theory is then used to calculate a value that combines growth rate and weight at the end of the test. This value is then presented as a percentage from the mean (SI%). At the end of the test, the scrotum circumference is also measured and displayed as a deviation from the mean. An example of the test results is given in Table 5. The correlation between results in a central test (under feedlot conditions) and progeny performance in
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a commercial environment was less than 2% for Suffolk in the Midwestern United States (Waldron et al., 1990). Concern that the same situation would prevail for Dorper rams tested in Veld ram clubs required further consideration. The progeny of three males with an SI of 22% higher than the mean of their contemporaries were compared to those of three low index contemporaries under extensive pasture conditions. They were mated to a total of 300 commercial females (150 to high and 150 to low index males), and the progeny were raised in a single flock. At weaning, the progeny of the high index males were 2.14 kg (9%) heavier than those of the low index ones. The average performance of Boer Goats in three different testing stations is given as illustration in Table 6. 2.2.1. Selection for reproduction rate Net reproduction rate, defined as total weight of kid weaned accumulated over the lifetime of a doe, is by far the most economically important trait in meat goat farming. Despite its importance, this trait is usually ignored during selection of replacements. On buck sales, conformation and breed standards are regarded by the broader industry as the most important traits. The reason for this is mainly due to the sex-limited character of reproduction, expression of this trait late in productive life, as well as the complex distribution of the trait. Moreover, low h2 estimates are commonly reported for net reproduction rate, although it does exhibit high levels of phenotypic variation. Marked variation exists in Table 6 Average weights of Boer goats tested in three different farms under natural grazing conditions for at least 140 days Test location
Initial weight (kg)
Final weight (kg)
ADG (g/day)
Farm 1 Farm 2 Farm 3
31.4 33.0 34.2
45.2 44.6 51.9
81.2 63.3 71.4
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Table 7 Reproductive performance of the best and poorest third of the available ewes on two farms, as measured over four productive years Farm Aa
Trait recorded
Farm Ba
Best (28 Ewes) Number of lambs born per ewe per productive year Number of lambs weaned per ewe per productive year Total weight of lambs weaned per ewe per productive year Average weaning weight index of lambs a
1.41 1.40 42.2
Poor (28 Ewes) 0.75 0.41 12.4
102
Best (23 Ewes)
Poor (23 Ewes)
1.29 1.23 32.6
101
0.80 0.61 15.1
105
102
Farm A: rams with ewes constantly, Farm B: mated for 6 weeks annually.
net reproduction rate, with coefficients of variation generally exceeding 40%, as illustrated in Table 7 using sheep as an example. In comparison to the poorest ewes, the best had more lambs, which survived better and weighed more at weaning. The economical superiority of the best females over their poorest contemporaries is obvious. The bottom line is: there are not only some superior females in every flock, but also some very uneconomical ones. In a Merino flock divergently selected for multiple rearing-ability, since 1986 Cloete et al. (2004) reported an annual increase of 0.392 kg (or 1.8% of the overall mean) for weight of lamb weaned per ewe joined in a line selected for the reproductive performance of their dams (H line). The corresponding reduction in the performance of the corresponding line selected in the downward direction (L line) amounted to 0.265 kg (1.3% of the overall mean). These responses led to respective phenotypic means after 17 years of selection of 25 kg of lamb weaned per ewe joined in the H line, compared to 12 kg in the L line. The H line ewes had more multiple lambs, but also lower levels of preweaning mortalities (31% in L line compared to 23% in H line). Ewes in the H line also weaned significantly heavier lambs. These responses to a designed experiment accorded with the results presented in Table 7. Stemming from this, it is argued that net reproduction rate can be increased in a selection programmes despite its low heritability, and that a reasonable response of up to 2% per annum (similar to other production traits) could be obtained in small ruminants.
Marked within flock variation is available which can be utilized for current and future generation gains. In the South African NSSIS, weaning weight is used to calculate total weight of lamb or kid weaned. In addition, the females that did not lamb or kid or that had stillbirths also need to be recorded. Weaning weight is corrected for age and sex and then accumulated over a females’ lifetime. Then the total weight of lambs weaned is regressed for number of parities and expressed as a deviation from the mean value of her contemporaries. In addition, the average weaning weight of all her lambs is also calculated. An example of a reproduction output is given in Table 8. The first ewe listed had a good performance, with seven lambs weaned with an average weaning index of 104. The second ewe is also prolific and had nine lambs in total. The average weaning index of 81 for her lambs suggested poor mothering ability, resulting in the weaning of inferior lambs. The last ewe listed weaned four lambs in total, but under-performed relative to her contemporaries. 2.2.2. Other traits Improvement of muscling and reducing fat in goat carcasses have not received the attention it warrants, especially for level 3 high-input production systems. The value of ultrasound technology to predict carcass characteristics, as an example, is widely used in beef, pork and lamb. Carr et al. (2002) reported a positive correlation (0.67) between live weight and muscle measurements in Boer goat crosses. Carr et al. (2002) concluded that prediction of carcass fat by means of
Table 8 Example of output for reproductive performance from the South African national Small Stock Improvement Scheme Ewe ID
Productive years
Number of lambs born
Number of lambs weaned
Ewe productivity index
Mean index of lambs
ABF-96-0231 ABF-99-0104 ABF-99-0630
5 5 5
7 9 7
7 7 4
17 1 −14
104 81 89
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ultrasound was not useful. This technology should be further tested and investigated. Improvement in growth and live weight seems to be positively correlated to muscle measurements. As illustrated in Table 2, the majority of goats are found in the developing countries. With limited resources, the adaptability and hardiness of goats are very important traits. Goats are susceptible to gastrointestinal nematode infection, but evidence of their genetic resistance is limited. Baker et al. (2001) reported that small East African goats were more resistant to nematode parasites than Galla kids, as shown by their significantly lower faecal egg count (FEC) in the post-weaning period (8- to 14-month-old kids) and lower mortality from birth to 14 months of age. Heritability estimates for records taken at 4.5 and 8 months of age from a repeated measures analysis were 0.18 (s.e. 0.08) for PCV and 0.13 (s.e. 0.07) for logarithmtransformed FEC (Baker et al., 2001). Mandonnet et al. (2001) reported a heritability of 0.37 ± 0.06 for transformed FEC at weaning in Creole kids.
3. Dairy goats 3.1. History of dairy goat production and milk recording in South Africa Next to dairy cattle, sheep and goats are the most important milk-producing animals in both the temperate and tropical environments of the world (Devendra and Coop, 1982). The population of dairy goats in the world has increased over the last three decades. Dairy goats were mainly kept for household purposes in the past. The reason for keeping dairy goats, however, has changed to a large extent. In South Africa there is a growing demand for goat milk products mainly for the tourist industry. There is also a large demand for dairy goat milk products for people with health problems. The dairy goat industry in South Africa probably started before the arrival of Europeans in this country. Goats similar to those of Nubian or Egyptian origin were encountered among the Namaqua people in the vicinity of the Olifants River. During the early years very little was done to improve the genetic quality of the local dairy goats. The Cape Agricultural Department imported three Saanen bucks and 12 does from (probably) Switzerland in 1898. Most of the present-
Fig. 1. Number of lactation records in the South African milk goat recording scheme according to year of kidding from 1956 until 2001.
day Saanen goats in South Africa originated from two bucks and 15 does that were imported from Switzerland in 1903. Dairy goats in South Africa are included with dairy cattle in the National Dairy Animal Improvement Scheme. Thus, the milk yield of does is measured over a 300-day lactation period from 10 monthly tests of the amount of milk produced at two or more milking per 24-h period. Milk samples are collected from each doe and analysed for fat and protein content. Initially only the fat content of the milk was determined at these monthly tests. Milk production records (Fig. 1) of dairy goats are available from the 1957/58-production year when a number of mostly first lactation does were recorded. For all practical purposes, the 1981/82-production year is regarded as the first year of milk recording for dairy goats, as the number of lactation records was low and variable before that year. At present, milk recording is done every 5 weeks and eight tests are needed for a lactation record of the milk, fat and protein yield, as well as fat, protein and lactose content for each doe. The milk yield and milk composition of dairy animals are influenced by a large number of factors. In South Africa registered and non-registered dairy goats produce milk under a wide range of climatic conditions. Goats are both grazers and browsers, making them well adapted to survive and produce under a wide range of production systems. Donkin and Boyazoglu (2000) noted that Saanen goats could produce more than 700 kg of milk over a lactation period of 288 days, while the milk yield of local (or indigenous) goats could be as little as 23 kg over a lactation period of less than 100 days. Indigenous animals, however, produce milk
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Table 9 Number of lactation records available for all production years and number of records removed from the data sets of the different breeds Parameters
Dairy goat breeds Saanen
British Alpine
Toggenburg
Number of lactations
14688
560
900
Records removed Milk = 0 Lactation period (>304 days) Lactation period (<60 days) Lactation number >8 No lactation record Lactation record >2500 kg Usable records
594 72 262 33 3558 5 10164
25 4 2 1 174 – 354
50 1 12 2 372 – 463
from the natural herbage that is of poor quality for most of the year, while also rearing one or more kids. The number of records available for the different dairy goat breeds is presented in Table 9. The data set of the Toggenburg breed had 372 records (41% of all does) with no lactation information, while the data set of the British Alpine breed had 174 records (31% of all ewes) with no lactation information. The number of usable records for the British Alpine and Toggenburg breeds was 63 and 51% of the original number of lactations respectively. In the Saanen breed 24% of all lactations (3557 of 14,688 does) had no lactation records. Almost 70% (10,164) of Saanen lactations had production records conforming to the required criteria. Production parameters for Saanen, British Alpine and Toggenburg dairy goats are very similar in the national database, although fat content of Saanen goat milk is generally lower than that of the other two breeds. The large variation in the number of records between the different breeds also makes comparisons very difficult (Fig. 2). The owner-sampling method used in South Africa has resulted in a marked increase in the number of dairy goats participating in milk recording (Hallowell et al., 2000).
Fig. 2. Average lactation milk yield records for the dominant dairy goat breeds in South Africa for the period 1956 to 2002.
rear all replacements of cows or goats in the herd. Dairy goat farmers generally believe that once a year kidding improves productive life. Lactation records were divided from first parity to eighth parity (Fig. 3). The average number of completed lactations for Saanen, British Alpine and Toggenburg dairy goat does was only 2.35, 2.02 and 2.24, respectively. Between 34 and 48% of animals with a first lactation records progressed to a second lactation. The smaller number of lactation records with increasing lactation number is typical of the erosion of animals as is also seen in the dairy cattle industry. Du Toit (1994) and Hallowell (1994) found for Jersey and for Ayrshire cows that first and second lactation records accounted for 57 and 58%, respectively, of all records up to the tenth lactation. A similar pattern has been observed in other countries. Milk and component yields generally increased from first to second and third parity (Fig. 4). This increase was followed with a general decline in yield traits for the later parities. A maximum milk yield of
3.2. Factors affecting milk production in South African dairy goats 3.2.1. Parity The longevity of dairy animals is associated positively with profit margins in dairy herds. A longer average herd life of dairy animals reduces the need to
Fig. 3. The number of animals with second and subsequent lactations, expressed as a proportion of the number of animals with a first lactation record.
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J.J. Olivier et al. / Small Ruminant Research 60 (2005) 83–93 Table 10 The number of records and average production parameters of grade and registered Saanen goats in the national herd
Fig. 4. Means for milk, butterfat and protein yields with an increase in lactation number. Means were derived from analyses conducted by Muller et al. (2002). Vertical bars about the mean represent standard errors on the graph.
approximately 900 kg per lactation was recorded at the second and third parities. Milk production subsequently declined to approximately 800 kg per lactation. During the period from 1981 to 1999 some 75 dairy goat owners had herds with animals with completed lactation records. Unfortunately, more than 56% of these herds were small with fewer than 30 lactation records in each herd. Only nine herds (14% of all herds) had more than 100 lactation records each. One herd had more than 6400 lactation records. Almost 70% of all records in the national database were from three herds only. The implication of this is that a genetic analysis of specifically the Saanen dairy goat breed in South Africa would essentially be based on these three herds. 3.2.2. Length of lactation period Lactation records of Saanen dairy goats varied from zero to 420 days in milk. The general consensus is that all lactation records irrespective of number of days in milk should be included in an analysis to counter any bias in genetic evaluations that may occur (Norman et al., 1985; Rege, 1991). The average number of days in milk for does in the Saanen data set was 246 ± 68 days. The coefficient of variation for days in milk exceeds 28% showing a large variation in number of days in milk. The average production is displayed in Table 10. 3.3. Bucks The value of bucks in a dairy goat herd is much more than getting does pregnant and in production. They could have a positive or negative effect on the genetic merit of a herd. Bucks used in the last three generations
Production parameters
Registered
Grade
Number of records Average number of lactation days Milk (kg) Fat (kg) Protein (kg) Fat (%) Protein (%)
2437 276 940.1 28.4 25.6 3.03 2.73
6354 260 841.1 25.9 22.8 3.14 3.14
of a herd have a genetic contribution of 87% in the current herd. No progeny testing of bucks for use through AI or natural mating is being conducted for dairy goats in South Africa. Generally breeders evaluate their own bucks to be used in their herds, based on pedigrees and probably some production figures. While the environmental conditions of dairy goat production in South Africa can be described very well, little is known of the genetic merit of these animals. To increase the milk yield of goats in both the rural areas and in intensive production systems, the milk yield potential of the bucks intended for mating should at least be known. The selection of dairy goats would also be improved in commercial herds resulting in higher efficiency. 3.4. Genetic parameter estimates for diary goats Heritability (h2 ) estimates for yield traits in dairy goats are in the medium to high range (Table 11), and genetic progress should be achievable in a wellstructured breeding programme. Repeatability (t) estimates were higher still, and current herd gains could thus be expected. Marked variation is found in h2 and t estimates for the percentage traits, figures ranging from less than 0.2 to more than 0.8. Judged from experience with dairy cattle, it is expected that h2 estimates for the percentage traits should exceed those of yield traits. The greater variation in h2 estimates in dairy goats for the percentage traits seems to justify further studies on this topic. This result could possibly be attributed to the small size of goat herds used for parameter estimation. Genetic correlations among yield traits are high, ranging from 0.57 to 0.89 (Morris et al., 1997; Ilahi et al., 2000; Muller et al., 2002). Correlations of milk
J.J. Olivier et al. / Small Ruminant Research 60 (2005) 83–93
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Table 11 Literature estimates of heritability (h2 ) and repeatability (t) for milk traits in dairy goats Trait and breed
h2
Milk yield Alpine and Saanen Murcia-Granada Saanen Alpine and Saanen Alpine Skopelos Saanen
0.35 0.17–0.18 0.23 0.21 0.35 0.14–0.28 0.23
Butterfat yield Alpine and Saanen Alpine Saanen
0.32 0.38 0.22
Protein yield Alpine and Saanen Alpine Saanen
0.36 0.33 0.20
Butterfat % Murcia-Granada Alpine and Saanen Alpine Saanen
0.14–0.16 0.16 0.72 0.21
0.33–0.36
Protein % Murcia-Granada Alpine and Saanen Alpine Saanen
0.22–0.25 0.16 0.75 0.44
0.41–0.47
Protein + Fat % Saanen
0.27
t
Country
Reference
0.41
France Spain New Zealand Slovenia France Greece South Africa
Boichard et al. (1989) Analla et al. (1996) Morris et al. (1997) Breznik et al. (1999) Ilahi et al. (2000) Kominakis et al. (2000) Muller et al. (2002)
0.56 0.38
France South Africa
Boichard et al. (1989) Ilahi et al. (2000) Muller et al. (2002)
0.52
France France South Africa
Boichard et al. (1989) Ilahi et al. (2000) Muller et al. (2002)
Spain Slovenia France South Africa
Analla et al. (1996) Breznik et al. (1999) Ilahi et al. (2000) Muller et al. (2002)
0.82 0.63
Spain Slovenia France South Africa
Analla et al. (1996) Breznik et al. (1999) Ilahi et al. (2000) Muller et al. (2002)
0.27
New Zealand
Morris et al. (1997)
0.36–0.39 0.26 0.54
0.81 0.48
yield with butterfat percentage were mostly negative in sign and moderate to high in magnitude, ranging from −0.12 to −0.89 for butterfat and from −0.24 to −0.65 for protein percentage (Analla et al., 1996; Ilahi et al., 2000; Muller et al., 2002). Butterfat and protein percentages are generally positively related on a genetic level, with estimates ranging from 0.34 to 0.59 (Ilahi et al., 2000; Muller et al., 2002). 3.5. Other traits One of the traits related to lower input costs is disease resistance. In most herds, the reduction of parasitic challenge is important. The h2 of nematode FEC in New Zealand Saanen goats were estimated at 0.15, suggesting a possibility of selection for a reduced input cost in the breed (Morris et al., 1997). Genetic correla-
tions with milk yield (−0.21) as well as butterfat and protein contents (−0.17) were negative, but not significant. Phenotypic correlations were low, at −0.07 in both instances. An increased milk speed is likely to facilitate the milk winning process. Milk speed of Alpine does was highly heritable at 0.65 (Ilahi et al., 2000), with a repeatability of 0.82. Segregation analyses strongly suggested that the trait was likely to be affected by a major gene, accounting for 63% of the total genetic variation in milking speed. The underlying biological mechanism of the major gene effect is, however, still unclear. More research on relationships of milking speed with udder and teat characteristics as well as somatic cell counts is required before this trait can seriously be considered in selection programmes (Ilahi et al., 2000).
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Linear type traits in Saanen and Alpine goats were heritable (Manfredi et al., 2001; Wiggans and Hubbard, 2001). Estimates for thorax diameter in the former study were high (0.41 to 0.50), those for udder and teat traits moderate to high (0.15 to 0.52) and those for feet and leg traits low (0.05 to 0.16). Most correlations between breeding values for milk yield and type traits were low (Manfredi et al., 2001). The exception was unfavourable correlations between milk yield and udder attachment traits. 4. Conclusions From the foregoing, it is clear that progress is being made with the performance testing and recording of meat and milk goats in South Africa. It is also clear that there are still many gaps in the existing knowledge, and that the recording schemes are still deficient in many instances. A successful recording system must be based not only on the selection goal (genetics has long-term results, especially in harsh conditions), but must also foresee extension services as a means to send a technician to the herd and to give advice to the farmers (Moioli et al., 2000). Future developments should focus on the deliverance of an affordable and practical scheme aimed at the requirements of both small scale and commercial goat producers. The effectiveness of such a recording system must be measurable by means of the genetic trend(s) measured in one population for the trait(s) in the defined breeding goal after a certain number of years of activity. Secondly, the success of a recording system can also be measured by the costs stood in for by private farmers. Especially in low to medium-input production systems, the integration of genetic improvement programmes with other livestock improvement activities must include the management of the environment, animal health as well as product preparation for market and practical training (Olivier et al., 2002). References Analla, M., Jim´enez-Gamero, I., Mu˜noz-Serrano, A., Serradilla, J.M., Falag´an, A., 1996. Estimation of genetic parameters for milk yield and fat and protein contents of milk from MurcianoGranadina goats. J. Dairy Sci. 79, 1895–1898. Baker, R.L., Audho, J.O., Aduda, E.O., Thorpe, W., 2001. Genetic resistance to gastro-intestinal nematode parasites in Galla and
small East African goats in the sub-humid tropics. Anim. Sci. 73, 61–70. Ball, A.J., Brown, D.J., Spiker, S.A., Field, S.R., Banks, R.B., 2001. Opportunities for genetic development of the Boer Goat in Australia using Kidplan. Proc. Assoc. Adv. Anim. Breed. Gen. 14, 445–448. Boichard, D., Bouloc, N., Ricordeau, G., Piacere, A., Barillet, F., 1989. Genetic parameters of first lactation dairy traits in tha Alpine and Saanen goat breeds. Genet. Sel. E 21, 205–215. Breznik, S., Kovac, M., Kompan, D., Birtic, D., Barilet, F., Zervas, N.P., 1999. Genetic evaluation of goat milk traits with a test-day model. In: Milking and milk production of dairy sheep and goats. Proceedings of the Sixth International Symposium on Milk. Small Rumin., EAAP Publication No. 95, 427–429, 26 September to 1 October 1998, Athens, Greece. Carr, M.A., Waldron, D.F., Willingham, T.D., 2002. Relationships among weights, ultrasound and carcass characteristics in Boercross goats. Texas Agric. Exp. Sta. Res. Rep., pp. 55–59. Cloete, S.W.P., Gilmour, A.R., Olivier, J.J., Van Wyk, J.B., 2004. Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and live weight in Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric. 44, 745–754. Devendra, C., Coop, I.E., 1982. Ecology and distribution. In: Coop, I.E. (Ed.), World animal science series C1 production system approach: sheep and goat production. Elsevier, Amsterdam, pp. 1–14. Donkin, E.F., Boyazoglu, P.A., 2000. Milk production from goats for household and small-scale farmers in South Africa. In: Proceedings of the Seventh International Conference on Goats, vol 1, Tours, France, pp. 324–328. Du Toit, J., 1994. A genetic evaluation of the South African Jersey breed. M.Sc. thesis, University of the Orange Free State, Bloemfontein, South Africa. FAO, 2004. Available at the following website: http://www.fao.org/ waicent/portal/statistics en.asp. Accessed during February 2004. Frisch, J.E., 1981. Changes occurring in cattle as a consequence of selection for growth rate in a stressful environment. J. Agric. Sci. Camb. 96, 23–38. Hallowell, G.J., 1994. Genetic parameter estimates for the South African Ayrshire breed. M.Sc. thesis, University of the Orange Free State, Bloemfontein, South Africa. Hallowell, G.J., Moore, S.J., Mostert, B.E., 2000. Annual report. South African national dairy animal improvement scheme, vol. 20, 17–18. Ilahi, H., Manfredi, E., Chastin, P., Monod, F., Elsen, J.M., Le Roy, P., 2000. Genetic variability in milking speed of dairy goats. Genet. Res. Camb. 75, 315–319. INRA and CIRAD, 2002. Proceedings of the Seventh World Congress on Genetics Application and Livestock Production, Montpellier. 19–23 August. CD-ROM. INRA, Toulouse. Livestock in Development, 1998. Sustainable rural livelihoods: What contribution can we make? London: Department for International Development. Kominakis, A., Rogdakis, E., Vasiloudis, C., Liaskos, O., 2000. Genetic and environmental sources of variation of milk yield in skopolos dairy goats. Small Rumin. Res. 36, 1–5.
J.J. Olivier et al. / Small Ruminant Research 60 (2005) 83–93 Mandonnet, N., Aumont, G., Fleury, J., Arquet, R., Varo, H., Gruner, L., Bouix, J., Vu-Tien-Khang, J., 2001. Assessment of genetic variability of resistance to gastrointestinal nematode parasites in Creole goats in the humid tropics. J. Anim. Sci. 79, 1706–1712. Manfredi, E., Piacere, A., Lahaye, P., Ducrocq, V., 2001. Genetic parameters of type appraisal in Saanen and Alpine goats. Livest. Prod. Sci. 70, 183–189. Maniates, N., Pollott, G.E., 2002. The impact of data structure on genetic (co)variance components of early growth in sheep, estimated using an animal model with maternal effects. J. Anim. Sci. 81, 101–108. Moioli, B., Astruc J.-M., Sanna S., 2000. Successful establishment of small ruminant recording systems in the Mediterranean countries. In: Development of Successful Animal Recording Systems for Transition and Developing Countries. Proceedings of the FAO/ICAR Seminar, Interlaken, Switzerland, 27 May 2002. pp. 155. Morris, C.A., Wheeler, M., Hosking, B.C., Watson, T.G., Hurford, A.P., Foote, B.J., Foote, J.F., 1997. Genetic parameters for milk yield and faecal egg count in Saanen does. N.Z. J. Agric. Res. 40, 523–528. Muller, C.J.C., Cloete, S.W.P., Schoeman, S.J., 2002. Estimation of genetic parameters for milk yield and composition of South African Saanen goats. In: Proceedings of the Seventh World Congress on Genetics Application and Livestock Production, vol. 29, 18–23 August, Montpellier, France, pp. 259–262. Norman, H.D., Dickenson, F.N., Wright, J.R., 1985. Merit of extending completed records of less than 305 days. J. Dairy Sci. 68, 2645–2654.
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Olivier, J.J., Moyo, S., Montaldo, H.H., Thorpe, W., Valle Zarate, A., Trivedi, K.R., 2002. Iintegrating genetic improvement into livestock development in medium- to low-input production systems. In: Proceedings of the Seventh World Congress on Genetics Applications and Livestock Production, vol. 25, 18–23 August, Montpellier, France, p. 15. Peters K.J., Zambach, B., 2002. Needs for research and development in Livestock Recording Systems (LRS) in transition and developing countries. In Development of Successful Animal Recording Systems for Transition and Developing Countries Proceedings of the FAO/ICAR Seminar held in Interlaken, Switzerland, 27 May 2002, 155. Rege, J.E.O., 1991. Genetic analysis of reproduction and productive performance of cattle in Kenya. J. Anim. Breed. Gen. 108, 412–423. Snyman, M.A., Erasmus, G.J., van Wyk, J.B., 1995. Direct and maternal (co)variance components and heritability estimates for body weight at different ages and fleece traits in Afrino sheep. Livest. Prod. Sci. 44, 229–235. Van Niekerk, M.M., Schoeman, S.J., Botha, M.A., Casey, N., 1996. Heritability estimates for pre-weaning growth traits in the Adelaide Boer goat herd. S. Afr. J. Anim. Sci. 26, 6–10. Waldron, D.F., Thomas, D.L., Stookey, J.M., Nash, T.G., McKeith, F.K., Fernando, R.L., 1990. Central ram tests in the midwestern United States. III. Relationship between sire’s central test performance and progeny performance. J. Anim. Sci. 68, 45–53. Wiggans, G.R., Hubbard, S.M., 2001. Genetic evaluation of yield and type traits in the genetic evaluation of dairy goats in the United States. J. Dairy Sci. 84 (E. Suppl.), E69–E73.