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Improving accuracy of selection of young bulls by Pastoralists
Livestock Science 110 (2007) 141 – 147 www.elsevier.com/locate/livsci
Improving accuracy of selection of young bulls by Pastoralists W.S. Pitchford ⁎ International Livestock Research Institute, PO Box 30709 Nairobi, Kenya Received 20 June 2005; received in revised form 12 October 2006; accepted 27 October 2006
Abstract A key to maximising response to selection in pastoral cattle kept by groups such as the Sub-Saharan Maasai is an accurate selection of young bulls. A breeding objective was developed based on weight, reproductive rate (days to calving), temperament, tick resistance and trypanotolerance. Accuracy of selection was defined as the correlation between the breeding objective and various selection indices. Accuracy was evaluated assuming availability of information on a range of traits (those in objective plus scrotal circumference) from individuals, parents, grand-parents, half-sibs, progeny and genetic markers. Various scenarios that represent what could occur at the village level were tested. Just selecting on weight alone had an accuracy of 0.538. Additional measurements on the individual (including repeated measures) had a large effect on accuracy. Records on relatives were less helpful than expected. Genetic markers for traits which are difficult to measure (days to calving and trypanotolerance) were helpful for improving accuracy. However, they are unlikely to be used in the near future because of cost and availability. An additional output from this study is simple selection indices that could be implemented immediately at the village level. © 2006 Elsevier B.V. All rights reserved. Keywords: Selection index; Pastoral; Cattle; Accuracy; Maasai
1. Introduction One of the characteristics of livestock industries in developing countries relative to those in developed countries is the absence of a clear “stud” sector. Thus, government sponsored open nucleus breeding schemes have been recommended as ideal way forward for livestock improvement in developing countries (Sethi and Jain, 1993). However, very few of these schemes have been successful. One of the limitations to the success of these programs are environmental stresses ⁎ Permanent address: The University of Adelaide, Roseworthy Campus, Roseworthy SA 5371, Australia. Tel.: +61 8 83037642; fax: +61 8 83037972. E-mail address: [email protected]. 1871-1413/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2006.10.016
such as nutrition and disease which lower productivity of the herds and it is difficult to raise productivity levels beyond those of local systems to speed genetic progress. Another limitation is that they have a history of being managed by people who lack the passion for developing and delivering superior livestock to those in need. An alternative to developing nucleus breeding schemes is to establish genetic improvement programs in the production systems themselves. This has the advantages of working directly with those with a passion and dependence on livestock and overcoming the need for dissemination of improved genetics using technologies such as artificial insemination. However, an obvious threat to the success of this strategy is convincing the users of the long-term benefits when there are short-term costs.
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W.S. Pitchford / Livestock Science 110 (2007) 141–147
The steps required when developing a breeding program with the aim of genetic improvement of livestock have been clearly outlined in a number of papers and were reviewed by Harris and Newman (1994). The first step is to gain a thorough understanding of the breeding, production and marketing system. The next step is to formally develop a breeding objective by defining important traits and estimating economic values for them. However, estimation of economic values is often not simple and Amer (2006) recently suggested that formulation of breeding objectives should become less biologically complex and make use market research techniques such as conjoint analysis. The strategy taken in this study is to assume a breeding objective and develop a selection index that could represent large numbers of cattle (over 170 million) across sub-Saharan Africa, a region with approximately 340M people living in rural areas. The down side to the general index is that it is probably not ideal for any of the specific production systems in that region. However, as stated by Covey (2004) “most leaders would agree that they would be better off having an average strategy with superb execution than a superb strategy with poor execution.” One group of people that could use the system as developed herein are the Maasai Pastoralists in Eastern Africa. These people are outstanding stockmen and depend entirely on their stock for livelihoods. They are poor by world standards in that they have limited access to health care and education. They also do not have individual ownership of land but they do own significant livestock resources (cattle, sheep and goats). Herds range from say 50–1000 head and are commonly a few hundred. Control of nutrition is limited except through stock movements. Animals can be treated for diseases such as ticks since they are herded each night for protection from theft and predators. Genetic gain from selection of females is negligible because of low calf survival rates, late age at first calving and long intercalving intervals. Hence, almost all genetic gain comes
through selection of bulls. Cull bulls are castrated at 1.5–2 years and then fattened for slaughter. Thus, the focus of breeding strategies must be to ensure accurate selection of superior young bulls. The aim of this paper is to investigate strategies to improve accuracy of selection of young bulls by formally including various sources of information into a selection index. Accuracy is defined as the correlation between the breeding objective and selection index. 2. Methods 2.1. Genetic and phenotypic parameters Six traits were used for the analysis herein: 18-month weight (Wt), scrotal circumference (SC), reproductive performance (DTC), temperament, tick count (Boophilus microplus) and trypanotolerance (packed red blood cell volume, PCV). Genetic parameters for the first five traits were taken from a large (2557) composite population in sub-tropical Australia (Burrow, 2001). In that trial, reproductive performance was defined as the number of days from when bulls went into the breeding paddock until the cow calved (days to calving, DTC). Thus, DTC is a function of the cow cycling, then conceiving, the foetus surviving and gestation length. However, most of the variance in DTC relates to time to conception. This is difficult measure if bulls constantly run with the cows, but the number of days from one calf (parturition) to the next would be an equivalent measure and quite obtainable. Burrow defined temperament as the time taken for the animal to cover 1.7 m after being released from being weighed. Animals with “better” temperament took longer than those that rushed out. This measure would be impossible without yards, but since the Pastoralists spend so much time with the cattle, they should be able to accurately score the cattle for overall temperament. Even body weights are difficult to record without yards and scales, but calves could be scored or ranked for size within management/age groups. If these scores ranks are accurately assigned, then breeding values can be
Table 1 Description of traits, variances, heritabilities and selection index weights Trait
Acronym
Mean
Variance
Heritability
Economic weight ($/σP)
Economic value ($/unit)
Weight at 18 months (kg) Scrotal circumference (mm) Reproduction rate (days to calving) Temperament (100ths s/1.7 m) Tick (count) Trypanotolerance (%)
Wt SC DTC Temp Tick PCV
313 295 339 1.04 1.25 25
749 758 1578 16.1 0.62 4
0.46 0.41 0.07 0.40 0.23 0.15
20 0 − 30 10 − 20 20
0.73 0 − 0.76 2.49 − 25.40 10.00
PCV values adapted from Trail et al. (1991), others from Burrow (2001).
W.S. Pitchford / Livestock Science 110 (2007) 141–147
calculated assuming the trait has actually been measured (Pitchford, 2006). The only published heritability of trypanotolerance is for a small (148) group of N'Dama cattle in Central Africa (Trail et al., 1991). The mean and SD of PCV were not actually reported but were chosen based on experience of ILRI staff in current research projects (Table 1). The heritability estimate for PCV was very high (0.63) when parasitaemia was included in the analysis, but was lower (0.35) when across all animals which would be more likely in the field situation. Both ticks (average 4) and PCV (11) were averaged across a number of measurements, so for this paper the phenotypic variances were scaled up. Additive genetic variances remained constant but heritabilities were scaled down to account for this (Table 1). The phenotypic (0.35) and genetic (0.41) correlation between PCV and average daily gain were quite high. Correlations between PCV and traits used herein are not reported and so have been chosen based on a best-guess (Table 2). 2.2. Assumptions when forming economic weights The aim of this study was not to formally develop breeding objectives. The relative emphasis on the traits in the index was influenced by Tano et al. (2003) who used conjoint analysis to estimate farmer's preferences. These were then modified following discussions with experienced animal scientists at ILRI (M. Okeyo and R.L. Baker pers. comm.). The simple way to assign economic values was to assume a total value of 100% or $100, then partition that according to the appropriate emphasis (Table 1). The production traits were weighted in priority as reproduction, growth and temperament in that order. In addition, two key disease traits in sub-Saharan Africa are parasites (ticks and trypanosomes) and these were weighted equivalent to growth. The dollar value for each trait was converted to the appropriate units by dividing the economic weight (e.g. $20) by the phenotypic standard deviation (Table 1). The simplest value to check if approTable 2 Genetic (below) and phenotypic (above) correlations Trait Wt SC DTC Temp Tick PCV
Wt 0.37 − 0.43 0.00 − 0.13 0.20
SC
DTC
Temp
Tick
PCV
0.40
− 0.02 0.00
− 0.03 0.07 0.01
− 0.10 0.04 0.15 0.05
0.20 0.00 0.00 0.00 − 0.10
0.32 0.11 0.09 0.00
0.15 0.10 − 0.10
0.01 0.00
0.00
PCV values adapted from Trail et al. (1991), others from Burrow (2001).
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priate was 18-month weight where the economic value was $0.73/kg. This value seems reasonable based on US dollars and recent prices paid at livestock markets in the region. Scrotal circumference was given an economic value of zero, but was still included in the analysis because selection is primarily based on young bulls and it is a useful selection criterion for days to calving. 2.3. Matrix calculations All calculations were conducted in Microsoft® Excel (2002). Following standard selection index theory (Hazel, 1943; Cameron, 1997), three matrices were created (P, G and C). The P matrix is a square matrix of phenotypic variances and covariances among the traits used as selection criteria. The G matrix comprises of genetic (co) variances between the selection criteria and breeding objectives. The C matrix is square and comprises genetic (co)variances among the traits in the breeding objective. In addition, there is a vector of input economic weights (a) and a vector of index weights (b) are calculated (Eq. (1)). b ¼ P−1 Ga
ð1Þ
The primary output from the various pieces of information are the standard deviation of the index (Eq. (2)) and the breeding objective (Eq. (3)), both with dollar units. The accuracy of selection was then calculated from these (Eq. (4)). r2I ¼ b VPb
ð2Þ
r2H ¼ aVCa
ð3Þ
sffiffiffiffiffiffi r2I Accuracy ¼ r2H
ð4Þ
In this study, there were 5 traits in the breeding objective and 6 possible traits as selection criteria. However, information from relatives was treated as additional selection criteria appropriately correlated with the original trait. There were 6 pieces of information available with potential for multiple measurements within each of those (listed below). So, with the 6 sources of information and 6 selection criteria, the dimensions of P and G were 36× 36 and 36× 6 respectively. Since the economic value of SC was 0, G was effectively 36× 5. The a and b vectors were of length 6 (effectively 5) and 36 respectively. 1. Individual (number of times recorded) 2. Parents (number of parents, max = 2) 3. Grand-parents (number of grand-parents, max = 4)
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in Maasai production systems. Thus, addition of progeny was not considered as a viable option and hence not reported. Information from a range of sources was evaluated by calculating the accuracy (correlation between index and objective). Traits on the individual were added in order of perceived difficulty: weight, scrotal circumference, temperament, tick resistance and packed cell volume as an indicator of trypanotolerance (Scenario 1, Table 3). Note that days to calving and scrotal circumference are sex-limited traits. The 5 traits measured on bulls could be measured more than once on the same animals, so Scenario 2 evaluated the impact of additional measurements. Scenario 3 assumed that the 5 traits had been recorded once on the young bulls, then their dam information was added in order of increasing difficulty: weight, temperament, tick resistance, trypanotolerance and reproductive performance (DTC). Since it is likely that grand-dam information would be more likely available than sire information, Scenario 4 assessed the impact of traits of the grand-dam in addition to those already recorded on the young bull and the dam. Scenario 5 evaluated the effect of sire information in addition to dam information but assuming no grand-dam information. Scenario 6 evaluated the impact of information (all traits) on a number of grand-parents (0–4) assuming that if these were known, then information on parents would also be known. Scenario 7 evaluated the impact of half-sib information. Half-sibs could be male or female and here it was assumed that a male was added, then female and so on. Scenario 8 evaluated the impact of adding gene markers for trypanotolerance assuming that 4 traits (weight, scrotal circumference, temperament and tick resistance) were recorded once. The markers accounted for 0–50% of the genetic variance. Scenario 9 was the same as 8 except for reproductive performance instead of trypanotolerance. Scenario 10
4. Half-sibs (number of half-sibs) 5. Progeny (number of progeny) 6. Genetic markers (proportion of genetic variance) It was assumed that genetic marker information would come from a number, rather than single, QTL or genes so that normality could be assumed. Thus, the assumed phenotypic variance of the marker (for P and G) was the proportion of genetic variance accounted for by the markers. The covariance between the same trait in the objective and selection criteria was also the proportion of genetic variance accounted for by the marker information. The covariance between traits (i and j) based on marker information was a function of the genetic covariance between the traits and the proportion ( pi, j) accounted for by markers for each trait (Eq. (5)). pi;j ¼ 1−ð1−pi Þð1−pj Þ
ð5Þ
Maasai herdsmen know their stock well and, as stated above, should be able to accurately rank them for weight, scrotal circumference, temperament and possibly tick numbers within groups of similarly aged bulls. Traits in this study were generally added in that order with PCV being the most difficult and added last (Table 3). It is also common that the herdsmen accurately know the dam and even grand-dam of a calf, although sire is rarely accurately known because of multiple sires kept with the herd. Trading is very common, resulting in loss of maternal pedigree information but does aid dissemination of superior genetics. Thus, information on relatives was added in the order of dam, grand-dam, sire, other grand-parents, half-sibs and then progeny. Accuracy gained from progeny records would be offset by additional time taken so a number of studies (e.g. Syrstad and Ruane, 1998) have recommended using young bulls rather than formal progeny testing which would also not be possible Table 3 Description of scenarios tested Scenario
No. of additional sources of information 1
2
3
4
5
1. Additional traits each measured once 2. Same traits measured multiple times 3. Add dam information (traits measured once) 4. Add grand-dam (traits measured once) 5. Add sire information (traits measured once) 6. Add grand-parents (all traits at once) 7. Add half-sibs—mixed sex (all traits at once) 8. Gene markers for PCV (+Wt, SC, Temp, Ticks) 9. Gene markers for DTC (+Wt, SC, Temp, Ticks) 10. PCV + DTC markers + multiples (Wt, SC, Temp, Ticks)
Wt 1 Wt Wt Wt 1 1 10 10 1
SC 2 Temp Temp SC 2 2 20 20 2
Temp 3 Tick Tick Temp 3 3 30 30 3
Tick 4 PCV PCV Tick 4 4 40 40 4
PCV 5 times DTC DTC PCV N/A 5 sibs 50% σG 50% σG 5 times
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assumed markers accounting for 50% of the genetic variance in both trypanotolerance and reproductive performance were available and that the remaining 4 traits could be measured on the bulls 0–5 times. 3. Results 3.1. Traits measured on the individual The standard deviation of the breeding objective (σH) was $24.92. Various scenarios were tested (Table 3) and reported as accuracy of selection (Table 4), although equally could have been reported as standard deviation of the index (σI). The simplest system tested was selection on weight alone (Scenario 1, Table 4) where the accuracy of selection was 0.538, corresponding to a dollar value of $13.41 and a reliability of 29%. Adding scrotal circumference, temperament and tick count increased the accuracy of selection to 0.552, 0.581 and 0.605 respectively. These represented an improvement of 3%, 8% and 12% respectively over simply selecting on weight (Fig. 1). The further addition of PCV as an indicator of trypanotolerance had negligible effect on the accuracy (0.606). The next step was for the 5 traits that can be measured on young bulls (Wt, SC, Temp, Tick, PCV), to be measured repeatedly (Scenario 2, Table 3). Each additional measurement added accuracy although the effect was proportionally smaller with more measurements (Fig. 1). With just three measurement times, the accuracy was 0.727 (Table 4). 3.2. Information from relatives The aim of Scenarios 3–7 (Table 3) was to test the impact of information on relatives. As stated earlier, it is
Fig. 1. Effect of additional sources of information on accuracy of selection. See Methods section and Table 3 for description of scenarios tested.
common for the Maasai Pastoralists to know the dam of a calf, and likely also the grand-dam. Each scenario assumed a single measurement of five traits on the individual (Wt, SC, Temp, Tick, PCV). Addition of five traits (Wt, Temp, Tick, PCV, DTC) on the dam increased the accuracy from 0.606 to 0.633 (Table 4). Addition of further relatives had negligible effect. There was a problem with adding greater than 2 grand-parents or halfsibs because the P matrix became non-positive definite. However, the effects were small so Scenarios 4–7 were not included in Fig. 1. 3.3. Effect of genetic markers There were two difficult traits for selection (DTC and PCV), both hard to measure (DTC also sex-limited) and of low heritability (Table 1). Thus, it is possible that genetic markers would be helpful for these traits. Both were tested assuming individual measurements could be made for four easier traits (Wt, SC, Temp, Tick) and accounted for varying proportions (0–50%) of the genetic
Table 4 Accuracy of selection following various scenarios Scenario
1. Additional traits each measured once 2. Same traits measured multiple times 3. Add dam information (traits measured once) 4. Add grand-dam (traits measured once) 5. Add sire information (traits measured once) 6. Add grand-parents (all traits at once) 7. Add half-sibs (all traits at once) 8. Gene markers for PCV (+Wt, SC, Temp, Ticks) 9. Gene markers for DTC (+ Wt, SC, Temp, Ticks) 10. PCV + DTC markers + multiples (Wt, SC, Temp, Ticks) ⁎ denotes P-matrix non-positive definite.
No. of additional sources of information 0
1
2
3
4
5
0 0 0.606 0.633 0.633 0.667 0.667 0.605 0.605 0.488
0.538 0.606 0.624 0.634 0.649 0.668 0.669 0.620 0.628 0.746
0.552 0.687 0.628 0.635 0.661 0.677 0.674 0.634 0.649 0.803
0.581 0.727 0.630 0.635 0.663 ⁎ ⁎ 0.648 0.670 0.831
0.605 0.751 0.631 0.635 0.666 ⁎ ⁎ 0.661 0.690 0.848
0.606 0.768 0.633 0.635 0.667 N/A ⁎ 0.674 0.709 0.859
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W.S. Pitchford / Livestock Science 110 (2007) 141–147
variance (Table 3). The accuracy increased linearly with increasing proportion of variance accounted for. The effect was slightly greater for DTC than PCV, with the individual measures and markers accounting for 50% of the genetic variance resulting in accuracies of 0.709 and 0.674 respectively. Since the results were similar, only PCV (Scenario 8) was presented in Fig. 1. Combining additional measures with genetic markers for the difficult traits (Scenario 10, Table 3) resulted in accuracies up to 0.859 (Table 4), 60% better than selection on weight alone. The standard deviation of the index was $21.40 with a reliability of 74%. This is far superior to the reliability resulting from simply measuring body weight once on a young bull (29%). 4. Discussion The response from selection on weight alone (accuracy of 0.538) was better than expected. This greater response resulted from the high heritability (0.46, Table 1) as well as correlations with other traits in the objective (Table 2). In most Maasai herds, bulls are always with the cows so calves are born all year around. However, there is likely some seasonality of resulting from seasonality of pasture availability affecting cow body condition and hence, likelihood of cycling and subsequent calving. Accurate calf birthdates are unlikely to be known, so when weighing a group of calves, size would be a function of age as well as growth. The result of this additional environmental variation compared to the study by Burrow (2001), would be to decrease the heritability. In addition, it may not be possible to actually weigh the calves and ranks or scores may be used instead (Pitchford, 2006). The effects of a lower heritability of weight were tested by inputting a value less than one for the number of measurements taken. Even a value as low as 0.5 still had an accuracy of 0.465 (reliability 22%). Thus, the conclusions drawn were relatively insensitive to the heritability of weight. When assigning ranks or scores to calves, the herdsmen are likely take into account how the animals have grown over the past few months, not just their weight on the day. Thus, the ranks or scores may be reasonably accurate representations of the calf's phenotype (Pitchford, 2006). It seems reasonable that this could also be true of traits like scrotal circumference, temperament and tick count. Multiple measures were shown to have a great effect on accuracy of selection. While it may be difficult to formally obtain information from Maasai herdsmen multiple times, their scores may well reflect multiple measures and approach higher accuracies as demonstrated herein (Scenario 2, Table 4 and Fig. 1).
Information on relatives was not that helpful unless there were large numbers of relatives which is not practicable in the Maasai type production systems. However, as DNA parentage systems become cheaper and more widely available, the opportunity to map relationships between animals within and across herds will likely increase accuracy of selection. One of the clear contrasts between these systems and those in the developed world is the lack of existence of a stud sector. If this sector did exist, higher costs of selection could be justified to increase response and the subsequent genetic improvement could then be multiplied by sale of bulls or even semen. However, when all herds are in the same “commercial” tier, it becomes much more difficult to justify additional investment in genetic improvement programs. A tool that could have a significant effect on genetic improvement would be if the markers used for pedigree identification were also providing information on traits in the breeding objective. This would be similar to Scenario 10 (Table 4, Fig. 1) where accuracy reached 0.859 (reliability 66%). In a small herd of 100 cows, there may be 30 bull calves to be genotyped each year, although this could be decreased to say 10 through multiple stage selection systems. With 10% of the herd being tested annually, the test would need to be so cheap that it was greater return on investment than buying additional animals. The results from this paper could form part of a training package for breeding livestock in poor communities. The first component could cover the importance of minimising inbreeding listing simple guidelines as given to Moses 3450 years ago (Leviticus 18, Holy Bible, 1997). The second component could cover the importance of managing stock (health and nutrition) to maximise productivity including reproductive rate which results in increased selection intensity and decreased generation interval. The focus of the current study was maximising accuracy of selection, which could form the third component of a training package. The results of this paper should provide some general principles for other livestock species as well as targets for extension of advice and technical expertise. 5. Conclusions This study has demonstrated selection strategies that could more than double the rate of genetic gain in Maasai type pastoral systems. The simplest gain could be achieved through repeated measurements. Given the assumptions herein, gene markers had a smaller effect than repeated measures but did add valuable additional information. Furthermore, they may simultaneously
W.S. Pitchford / Livestock Science 110 (2007) 141–147
provide information on pedigree. However, without the existence of a clear stud sector, markers would have to be extremely cheap before it would be viable for use to be recommended. An additional output from this study is simple selection indices that could be implemented immediately at the village level. Acknowledgements This paper has resulted from fruitful discussions with senior scientists in the Biotechnology Theme at the International Livestock Research Institute (ILRI). I am thankful for ILRI for providing a stimulating research environment as well as school fees for my children. Lastly, thanks to my home institution, The University of Adelaide, for providing the opportunity for a rewarding sabbatical. References Amer, P.R., 2006. Approaches to formulating breeding objectives. Proc. 8th Wld. Cong. Genet. Appl. Livest. Prod. Communication 31-01. Burrow, H.M., 2001. Variances and covariances between productive and adaptive traits and temperament in a composite breed of tropical beef cattle. Livest. Prod. Sci. 70, 213–233.
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Cameron, N.D., 1997. Selection Indices and Prediction of Genetic Merit in Animal Breeding. CAB International, Wallingford, Oxon, UK. Covey, S.R., 2004. The 8th Habit: from Effectiveness to Greatness. Free Press, New York, USA. Hazel, L.N., 1943. The genetic basis for constructing selection indexes. Genetics 28, 476–490. Harris, D.L., Newman, S., 1994. Breeding for profit: synergism between genetic improvement and livestock production (a review). J. Anim. Sci. 72, 2178–2200. Leviticus 18, Holy Bible, 1997. New International Version. Tyndale House Publishers Inc., Wheaton, Illinois, USA. Microsoft® Excel, 2002. Microsoft Corporation. Pitchford, W.S., 2006. Use of ranks and scores for genetic improvement of village livestock. Proc. 8th Wld. Cong. Genet. Appl. Livest. Prod. Communication 03-74. Sethi, I.C., Jain, J.P., 1993. Closed and open nucleus breeding schemes in the genetic improvement of dairy cattle. Indian J. Anim. Sci. 63, 863–868. Syrstad, O., Ruane, J., 1998. Prospects and strategies for genetic improvement of the dairy potential of tropical cattle by selection. Trop. Anim. Health Prod. 30, 257–268. Tano, K., Kamuanga, M., Faminow, M.D., Swallow, B., 2003. Using conjoint analysis to estimate farmer's preferences for cattle traits in West Africa. Ecol. Econ. 45, 393–407. Trail, J.C.M., d'Ieteren, G.D.M., Maille, J.C., Yangari, G., 1991. Genetic aspects of control of anaemia development in trypanotolerant N'Dama cattle. Acta Trop. 48, 285–291.
Improving accuracy of selection of young bulls by Pastoralists W.S. Pitchford ⁎ International Livestock Research Institute, PO Box 30709 Nairobi, Kenya Received 20 June 2005; received in revised form 12 October 2006; accepted 27 October 2006
Abstract A key to maximising response to selection in pastoral cattle kept by groups such as the Sub-Saharan Maasai is an accurate selection of young bulls. A breeding objective was developed based on weight, reproductive rate (days to calving), temperament, tick resistance and trypanotolerance. Accuracy of selection was defined as the correlation between the breeding objective and various selection indices. Accuracy was evaluated assuming availability of information on a range of traits (those in objective plus scrotal circumference) from individuals, parents, grand-parents, half-sibs, progeny and genetic markers. Various scenarios that represent what could occur at the village level were tested. Just selecting on weight alone had an accuracy of 0.538. Additional measurements on the individual (including repeated measures) had a large effect on accuracy. Records on relatives were less helpful than expected. Genetic markers for traits which are difficult to measure (days to calving and trypanotolerance) were helpful for improving accuracy. However, they are unlikely to be used in the near future because of cost and availability. An additional output from this study is simple selection indices that could be implemented immediately at the village level. © 2006 Elsevier B.V. All rights reserved. Keywords: Selection index; Pastoral; Cattle; Accuracy; Maasai
1. Introduction One of the characteristics of livestock industries in developing countries relative to those in developed countries is the absence of a clear “stud” sector. Thus, government sponsored open nucleus breeding schemes have been recommended as ideal way forward for livestock improvement in developing countries (Sethi and Jain, 1993). However, very few of these schemes have been successful. One of the limitations to the success of these programs are environmental stresses ⁎ Permanent address: The University of Adelaide, Roseworthy Campus, Roseworthy SA 5371, Australia. Tel.: +61 8 83037642; fax: +61 8 83037972. E-mail address: [email protected]. 1871-1413/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2006.10.016
such as nutrition and disease which lower productivity of the herds and it is difficult to raise productivity levels beyond those of local systems to speed genetic progress. Another limitation is that they have a history of being managed by people who lack the passion for developing and delivering superior livestock to those in need. An alternative to developing nucleus breeding schemes is to establish genetic improvement programs in the production systems themselves. This has the advantages of working directly with those with a passion and dependence on livestock and overcoming the need for dissemination of improved genetics using technologies such as artificial insemination. However, an obvious threat to the success of this strategy is convincing the users of the long-term benefits when there are short-term costs.
142
W.S. Pitchford / Livestock Science 110 (2007) 141–147
The steps required when developing a breeding program with the aim of genetic improvement of livestock have been clearly outlined in a number of papers and were reviewed by Harris and Newman (1994). The first step is to gain a thorough understanding of the breeding, production and marketing system. The next step is to formally develop a breeding objective by defining important traits and estimating economic values for them. However, estimation of economic values is often not simple and Amer (2006) recently suggested that formulation of breeding objectives should become less biologically complex and make use market research techniques such as conjoint analysis. The strategy taken in this study is to assume a breeding objective and develop a selection index that could represent large numbers of cattle (over 170 million) across sub-Saharan Africa, a region with approximately 340M people living in rural areas. The down side to the general index is that it is probably not ideal for any of the specific production systems in that region. However, as stated by Covey (2004) “most leaders would agree that they would be better off having an average strategy with superb execution than a superb strategy with poor execution.” One group of people that could use the system as developed herein are the Maasai Pastoralists in Eastern Africa. These people are outstanding stockmen and depend entirely on their stock for livelihoods. They are poor by world standards in that they have limited access to health care and education. They also do not have individual ownership of land but they do own significant livestock resources (cattle, sheep and goats). Herds range from say 50–1000 head and are commonly a few hundred. Control of nutrition is limited except through stock movements. Animals can be treated for diseases such as ticks since they are herded each night for protection from theft and predators. Genetic gain from selection of females is negligible because of low calf survival rates, late age at first calving and long intercalving intervals. Hence, almost all genetic gain comes
through selection of bulls. Cull bulls are castrated at 1.5–2 years and then fattened for slaughter. Thus, the focus of breeding strategies must be to ensure accurate selection of superior young bulls. The aim of this paper is to investigate strategies to improve accuracy of selection of young bulls by formally including various sources of information into a selection index. Accuracy is defined as the correlation between the breeding objective and selection index. 2. Methods 2.1. Genetic and phenotypic parameters Six traits were used for the analysis herein: 18-month weight (Wt), scrotal circumference (SC), reproductive performance (DTC), temperament, tick count (Boophilus microplus) and trypanotolerance (packed red blood cell volume, PCV). Genetic parameters for the first five traits were taken from a large (2557) composite population in sub-tropical Australia (Burrow, 2001). In that trial, reproductive performance was defined as the number of days from when bulls went into the breeding paddock until the cow calved (days to calving, DTC). Thus, DTC is a function of the cow cycling, then conceiving, the foetus surviving and gestation length. However, most of the variance in DTC relates to time to conception. This is difficult measure if bulls constantly run with the cows, but the number of days from one calf (parturition) to the next would be an equivalent measure and quite obtainable. Burrow defined temperament as the time taken for the animal to cover 1.7 m after being released from being weighed. Animals with “better” temperament took longer than those that rushed out. This measure would be impossible without yards, but since the Pastoralists spend so much time with the cattle, they should be able to accurately score the cattle for overall temperament. Even body weights are difficult to record without yards and scales, but calves could be scored or ranked for size within management/age groups. If these scores ranks are accurately assigned, then breeding values can be
Table 1 Description of traits, variances, heritabilities and selection index weights Trait
Acronym
Mean
Variance
Heritability
Economic weight ($/σP)
Economic value ($/unit)
Weight at 18 months (kg) Scrotal circumference (mm) Reproduction rate (days to calving) Temperament (100ths s/1.7 m) Tick (count) Trypanotolerance (%)
Wt SC DTC Temp Tick PCV
313 295 339 1.04 1.25 25
749 758 1578 16.1 0.62 4
0.46 0.41 0.07 0.40 0.23 0.15
20 0 − 30 10 − 20 20
0.73 0 − 0.76 2.49 − 25.40 10.00
PCV values adapted from Trail et al. (1991), others from Burrow (2001).
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calculated assuming the trait has actually been measured (Pitchford, 2006). The only published heritability of trypanotolerance is for a small (148) group of N'Dama cattle in Central Africa (Trail et al., 1991). The mean and SD of PCV were not actually reported but were chosen based on experience of ILRI staff in current research projects (Table 1). The heritability estimate for PCV was very high (0.63) when parasitaemia was included in the analysis, but was lower (0.35) when across all animals which would be more likely in the field situation. Both ticks (average 4) and PCV (11) were averaged across a number of measurements, so for this paper the phenotypic variances were scaled up. Additive genetic variances remained constant but heritabilities were scaled down to account for this (Table 1). The phenotypic (0.35) and genetic (0.41) correlation between PCV and average daily gain were quite high. Correlations between PCV and traits used herein are not reported and so have been chosen based on a best-guess (Table 2). 2.2. Assumptions when forming economic weights The aim of this study was not to formally develop breeding objectives. The relative emphasis on the traits in the index was influenced by Tano et al. (2003) who used conjoint analysis to estimate farmer's preferences. These were then modified following discussions with experienced animal scientists at ILRI (M. Okeyo and R.L. Baker pers. comm.). The simple way to assign economic values was to assume a total value of 100% or $100, then partition that according to the appropriate emphasis (Table 1). The production traits were weighted in priority as reproduction, growth and temperament in that order. In addition, two key disease traits in sub-Saharan Africa are parasites (ticks and trypanosomes) and these were weighted equivalent to growth. The dollar value for each trait was converted to the appropriate units by dividing the economic weight (e.g. $20) by the phenotypic standard deviation (Table 1). The simplest value to check if approTable 2 Genetic (below) and phenotypic (above) correlations Trait Wt SC DTC Temp Tick PCV
Wt 0.37 − 0.43 0.00 − 0.13 0.20
SC
DTC
Temp
Tick
PCV
0.40
− 0.02 0.00
− 0.03 0.07 0.01
− 0.10 0.04 0.15 0.05
0.20 0.00 0.00 0.00 − 0.10
0.32 0.11 0.09 0.00
0.15 0.10 − 0.10
0.01 0.00
0.00
PCV values adapted from Trail et al. (1991), others from Burrow (2001).
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priate was 18-month weight where the economic value was $0.73/kg. This value seems reasonable based on US dollars and recent prices paid at livestock markets in the region. Scrotal circumference was given an economic value of zero, but was still included in the analysis because selection is primarily based on young bulls and it is a useful selection criterion for days to calving. 2.3. Matrix calculations All calculations were conducted in Microsoft® Excel (2002). Following standard selection index theory (Hazel, 1943; Cameron, 1997), three matrices were created (P, G and C). The P matrix is a square matrix of phenotypic variances and covariances among the traits used as selection criteria. The G matrix comprises of genetic (co) variances between the selection criteria and breeding objectives. The C matrix is square and comprises genetic (co)variances among the traits in the breeding objective. In addition, there is a vector of input economic weights (a) and a vector of index weights (b) are calculated (Eq. (1)). b ¼ P−1 Ga
ð1Þ
The primary output from the various pieces of information are the standard deviation of the index (Eq. (2)) and the breeding objective (Eq. (3)), both with dollar units. The accuracy of selection was then calculated from these (Eq. (4)). r2I ¼ b VPb
ð2Þ
r2H ¼ aVCa
ð3Þ
sffiffiffiffiffiffi r2I Accuracy ¼ r2H
ð4Þ
In this study, there were 5 traits in the breeding objective and 6 possible traits as selection criteria. However, information from relatives was treated as additional selection criteria appropriately correlated with the original trait. There were 6 pieces of information available with potential for multiple measurements within each of those (listed below). So, with the 6 sources of information and 6 selection criteria, the dimensions of P and G were 36× 36 and 36× 6 respectively. Since the economic value of SC was 0, G was effectively 36× 5. The a and b vectors were of length 6 (effectively 5) and 36 respectively. 1. Individual (number of times recorded) 2. Parents (number of parents, max = 2) 3. Grand-parents (number of grand-parents, max = 4)
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in Maasai production systems. Thus, addition of progeny was not considered as a viable option and hence not reported. Information from a range of sources was evaluated by calculating the accuracy (correlation between index and objective). Traits on the individual were added in order of perceived difficulty: weight, scrotal circumference, temperament, tick resistance and packed cell volume as an indicator of trypanotolerance (Scenario 1, Table 3). Note that days to calving and scrotal circumference are sex-limited traits. The 5 traits measured on bulls could be measured more than once on the same animals, so Scenario 2 evaluated the impact of additional measurements. Scenario 3 assumed that the 5 traits had been recorded once on the young bulls, then their dam information was added in order of increasing difficulty: weight, temperament, tick resistance, trypanotolerance and reproductive performance (DTC). Since it is likely that grand-dam information would be more likely available than sire information, Scenario 4 assessed the impact of traits of the grand-dam in addition to those already recorded on the young bull and the dam. Scenario 5 evaluated the effect of sire information in addition to dam information but assuming no grand-dam information. Scenario 6 evaluated the impact of information (all traits) on a number of grand-parents (0–4) assuming that if these were known, then information on parents would also be known. Scenario 7 evaluated the impact of half-sib information. Half-sibs could be male or female and here it was assumed that a male was added, then female and so on. Scenario 8 evaluated the impact of adding gene markers for trypanotolerance assuming that 4 traits (weight, scrotal circumference, temperament and tick resistance) were recorded once. The markers accounted for 0–50% of the genetic variance. Scenario 9 was the same as 8 except for reproductive performance instead of trypanotolerance. Scenario 10
4. Half-sibs (number of half-sibs) 5. Progeny (number of progeny) 6. Genetic markers (proportion of genetic variance) It was assumed that genetic marker information would come from a number, rather than single, QTL or genes so that normality could be assumed. Thus, the assumed phenotypic variance of the marker (for P and G) was the proportion of genetic variance accounted for by the markers. The covariance between the same trait in the objective and selection criteria was also the proportion of genetic variance accounted for by the marker information. The covariance between traits (i and j) based on marker information was a function of the genetic covariance between the traits and the proportion ( pi, j) accounted for by markers for each trait (Eq. (5)). pi;j ¼ 1−ð1−pi Þð1−pj Þ
ð5Þ
Maasai herdsmen know their stock well and, as stated above, should be able to accurately rank them for weight, scrotal circumference, temperament and possibly tick numbers within groups of similarly aged bulls. Traits in this study were generally added in that order with PCV being the most difficult and added last (Table 3). It is also common that the herdsmen accurately know the dam and even grand-dam of a calf, although sire is rarely accurately known because of multiple sires kept with the herd. Trading is very common, resulting in loss of maternal pedigree information but does aid dissemination of superior genetics. Thus, information on relatives was added in the order of dam, grand-dam, sire, other grand-parents, half-sibs and then progeny. Accuracy gained from progeny records would be offset by additional time taken so a number of studies (e.g. Syrstad and Ruane, 1998) have recommended using young bulls rather than formal progeny testing which would also not be possible Table 3 Description of scenarios tested Scenario
No. of additional sources of information 1
2
3
4
5
1. Additional traits each measured once 2. Same traits measured multiple times 3. Add dam information (traits measured once) 4. Add grand-dam (traits measured once) 5. Add sire information (traits measured once) 6. Add grand-parents (all traits at once) 7. Add half-sibs—mixed sex (all traits at once) 8. Gene markers for PCV (+Wt, SC, Temp, Ticks) 9. Gene markers for DTC (+Wt, SC, Temp, Ticks) 10. PCV + DTC markers + multiples (Wt, SC, Temp, Ticks)
Wt 1 Wt Wt Wt 1 1 10 10 1
SC 2 Temp Temp SC 2 2 20 20 2
Temp 3 Tick Tick Temp 3 3 30 30 3
Tick 4 PCV PCV Tick 4 4 40 40 4
PCV 5 times DTC DTC PCV N/A 5 sibs 50% σG 50% σG 5 times
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assumed markers accounting for 50% of the genetic variance in both trypanotolerance and reproductive performance were available and that the remaining 4 traits could be measured on the bulls 0–5 times. 3. Results 3.1. Traits measured on the individual The standard deviation of the breeding objective (σH) was $24.92. Various scenarios were tested (Table 3) and reported as accuracy of selection (Table 4), although equally could have been reported as standard deviation of the index (σI). The simplest system tested was selection on weight alone (Scenario 1, Table 4) where the accuracy of selection was 0.538, corresponding to a dollar value of $13.41 and a reliability of 29%. Adding scrotal circumference, temperament and tick count increased the accuracy of selection to 0.552, 0.581 and 0.605 respectively. These represented an improvement of 3%, 8% and 12% respectively over simply selecting on weight (Fig. 1). The further addition of PCV as an indicator of trypanotolerance had negligible effect on the accuracy (0.606). The next step was for the 5 traits that can be measured on young bulls (Wt, SC, Temp, Tick, PCV), to be measured repeatedly (Scenario 2, Table 3). Each additional measurement added accuracy although the effect was proportionally smaller with more measurements (Fig. 1). With just three measurement times, the accuracy was 0.727 (Table 4). 3.2. Information from relatives The aim of Scenarios 3–7 (Table 3) was to test the impact of information on relatives. As stated earlier, it is
Fig. 1. Effect of additional sources of information on accuracy of selection. See Methods section and Table 3 for description of scenarios tested.
common for the Maasai Pastoralists to know the dam of a calf, and likely also the grand-dam. Each scenario assumed a single measurement of five traits on the individual (Wt, SC, Temp, Tick, PCV). Addition of five traits (Wt, Temp, Tick, PCV, DTC) on the dam increased the accuracy from 0.606 to 0.633 (Table 4). Addition of further relatives had negligible effect. There was a problem with adding greater than 2 grand-parents or halfsibs because the P matrix became non-positive definite. However, the effects were small so Scenarios 4–7 were not included in Fig. 1. 3.3. Effect of genetic markers There were two difficult traits for selection (DTC and PCV), both hard to measure (DTC also sex-limited) and of low heritability (Table 1). Thus, it is possible that genetic markers would be helpful for these traits. Both were tested assuming individual measurements could be made for four easier traits (Wt, SC, Temp, Tick) and accounted for varying proportions (0–50%) of the genetic
Table 4 Accuracy of selection following various scenarios Scenario
1. Additional traits each measured once 2. Same traits measured multiple times 3. Add dam information (traits measured once) 4. Add grand-dam (traits measured once) 5. Add sire information (traits measured once) 6. Add grand-parents (all traits at once) 7. Add half-sibs (all traits at once) 8. Gene markers for PCV (+Wt, SC, Temp, Ticks) 9. Gene markers for DTC (+ Wt, SC, Temp, Ticks) 10. PCV + DTC markers + multiples (Wt, SC, Temp, Ticks) ⁎ denotes P-matrix non-positive definite.
No. of additional sources of information 0
1
2
3
4
5
0 0 0.606 0.633 0.633 0.667 0.667 0.605 0.605 0.488
0.538 0.606 0.624 0.634 0.649 0.668 0.669 0.620 0.628 0.746
0.552 0.687 0.628 0.635 0.661 0.677 0.674 0.634 0.649 0.803
0.581 0.727 0.630 0.635 0.663 ⁎ ⁎ 0.648 0.670 0.831
0.605 0.751 0.631 0.635 0.666 ⁎ ⁎ 0.661 0.690 0.848
0.606 0.768 0.633 0.635 0.667 N/A ⁎ 0.674 0.709 0.859
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variance (Table 3). The accuracy increased linearly with increasing proportion of variance accounted for. The effect was slightly greater for DTC than PCV, with the individual measures and markers accounting for 50% of the genetic variance resulting in accuracies of 0.709 and 0.674 respectively. Since the results were similar, only PCV (Scenario 8) was presented in Fig. 1. Combining additional measures with genetic markers for the difficult traits (Scenario 10, Table 3) resulted in accuracies up to 0.859 (Table 4), 60% better than selection on weight alone. The standard deviation of the index was $21.40 with a reliability of 74%. This is far superior to the reliability resulting from simply measuring body weight once on a young bull (29%). 4. Discussion The response from selection on weight alone (accuracy of 0.538) was better than expected. This greater response resulted from the high heritability (0.46, Table 1) as well as correlations with other traits in the objective (Table 2). In most Maasai herds, bulls are always with the cows so calves are born all year around. However, there is likely some seasonality of resulting from seasonality of pasture availability affecting cow body condition and hence, likelihood of cycling and subsequent calving. Accurate calf birthdates are unlikely to be known, so when weighing a group of calves, size would be a function of age as well as growth. The result of this additional environmental variation compared to the study by Burrow (2001), would be to decrease the heritability. In addition, it may not be possible to actually weigh the calves and ranks or scores may be used instead (Pitchford, 2006). The effects of a lower heritability of weight were tested by inputting a value less than one for the number of measurements taken. Even a value as low as 0.5 still had an accuracy of 0.465 (reliability 22%). Thus, the conclusions drawn were relatively insensitive to the heritability of weight. When assigning ranks or scores to calves, the herdsmen are likely take into account how the animals have grown over the past few months, not just their weight on the day. Thus, the ranks or scores may be reasonably accurate representations of the calf's phenotype (Pitchford, 2006). It seems reasonable that this could also be true of traits like scrotal circumference, temperament and tick count. Multiple measures were shown to have a great effect on accuracy of selection. While it may be difficult to formally obtain information from Maasai herdsmen multiple times, their scores may well reflect multiple measures and approach higher accuracies as demonstrated herein (Scenario 2, Table 4 and Fig. 1).
Information on relatives was not that helpful unless there were large numbers of relatives which is not practicable in the Maasai type production systems. However, as DNA parentage systems become cheaper and more widely available, the opportunity to map relationships between animals within and across herds will likely increase accuracy of selection. One of the clear contrasts between these systems and those in the developed world is the lack of existence of a stud sector. If this sector did exist, higher costs of selection could be justified to increase response and the subsequent genetic improvement could then be multiplied by sale of bulls or even semen. However, when all herds are in the same “commercial” tier, it becomes much more difficult to justify additional investment in genetic improvement programs. A tool that could have a significant effect on genetic improvement would be if the markers used for pedigree identification were also providing information on traits in the breeding objective. This would be similar to Scenario 10 (Table 4, Fig. 1) where accuracy reached 0.859 (reliability 66%). In a small herd of 100 cows, there may be 30 bull calves to be genotyped each year, although this could be decreased to say 10 through multiple stage selection systems. With 10% of the herd being tested annually, the test would need to be so cheap that it was greater return on investment than buying additional animals. The results from this paper could form part of a training package for breeding livestock in poor communities. The first component could cover the importance of minimising inbreeding listing simple guidelines as given to Moses 3450 years ago (Leviticus 18, Holy Bible, 1997). The second component could cover the importance of managing stock (health and nutrition) to maximise productivity including reproductive rate which results in increased selection intensity and decreased generation interval. The focus of the current study was maximising accuracy of selection, which could form the third component of a training package. The results of this paper should provide some general principles for other livestock species as well as targets for extension of advice and technical expertise. 5. Conclusions This study has demonstrated selection strategies that could more than double the rate of genetic gain in Maasai type pastoral systems. The simplest gain could be achieved through repeated measurements. Given the assumptions herein, gene markers had a smaller effect than repeated measures but did add valuable additional information. Furthermore, they may simultaneously
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provide information on pedigree. However, without the existence of a clear stud sector, markers would have to be extremely cheap before it would be viable for use to be recommended. An additional output from this study is simple selection indices that could be implemented immediately at the village level. Acknowledgements This paper has resulted from fruitful discussions with senior scientists in the Biotechnology Theme at the International Livestock Research Institute (ILRI). I am thankful for ILRI for providing a stimulating research environment as well as school fees for my children. Lastly, thanks to my home institution, The University of Adelaide, for providing the opportunity for a rewarding sabbatical. References Amer, P.R., 2006. Approaches to formulating breeding objectives. Proc. 8th Wld. Cong. Genet. Appl. Livest. Prod. Communication 31-01. Burrow, H.M., 2001. Variances and covariances between productive and adaptive traits and temperament in a composite breed of tropical beef cattle. Livest. Prod. Sci. 70, 213–233.
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