Developing breeding schemes for pasture based dairy production systems in Kenya

Livestock Production Science 88 (2004) 179 – 192 www.elsevier.com/locate/livprodsci

Developing breeding schemes for pasture based dairy production systems in Kenya II. Evaluation of alternative objectives and schemes using a two-tier open nucleus and young bull system A.K. Kahi a,*, G. Nitter b, C.F. Gall c a Department of Animal Science, Egerton University, P.O. Box 536, 20107 Njoro, Kenya Institute of Animal Breeding and Husbandry, Hohenheim University, 70593 Stuttgart, Germany c Institute of Animal Production in the Tropics and Subtropics, Hohenheim University, 70593 Stuttgart, Germany b

Received 27 December 2001; received in revised form 4 July 2003; accepted 29 July 2003

Abstract A deterministic approach was used to evaluate alternative breeding objectives and schemes in a dairy cattle breed in Kenya. A two-tier open nucleus breeding scheme and a young bull system (YBS) were assumed with intensive recording and 100% artificial insemination (AI) in the nucleus and 35% AI in the commercial sector. The breeding objectives differed in the marketing scenario that each described and whether pasture feed for the cows was limited or not. Two marketing scenarios were distinguished; current (payment of milk is based on volume) and future (payment of milk would be based on volume and fat content). Therefore, four breeding objectives were considered: current no limitation (CUNL), current with limitation (CUWL), future no limitation (FUNL) and future with limitation (FUWL). The breeding schemes differed in the records available for use as selection criteria. The schemes ranged from one that only utilised fertility criteria (scheme 1) to one that incorporated fertility, weights, milk and fat yield (FY) criteria (scheme 5). The annual monetary genetic gain and profit per cow for all schemes varied within breeding objectives but were highest in CUNL. Within each marketing scenario, the annual monetary genetic gain and profit per cow was higher in a no limitation situation than in a situation with limitation on pastures. Within each breeding objective, the annual monetary genetic gain and profit per cow was highest for the breeding scheme with the highest level of investments. In all objectives, the difference in the profit per cow between a scheme that incorporated fertility, weights and milk yield (MY) criteria (scheme 4) and scheme 5 was small (0.4 – 1.2%) indicating that there is little benefit including FY as a selection criterion. Therefore, a breeding scheme that requires records on FY seems not to be reasonable from an economic point of view. This study showed that a well-organised breeding programme utilising an open nucleus, YBS and the smallholder farms as the commercial sector could sustain itself. The practical implications of the results and how sustainable breeding programmes can be established are discussed. D 2003 Elsevier B.V. All rights reserved. Keywords: Breeding objectives; Breeding schemes; Dairy cattle; Selection; Tropics

* Corresponding author. Tel./fax: +254-51-62436. E-mail address: [email protected] (A.K. Kahi). 0301-6226/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.livprodsci.2003.07.015

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1. Introduction The main aim of an animal breeder is to select animals based on a well defined breeding objective which should suit the future production and marketing requirements of the average commercial producer. The annual genetic response of traits in the breeding objective and the profit or net return from invested money are the criteria used in evaluating genetic improvement in any selection scheme. In Kenya, there are no effective genetic improvement programmes for any cattle breed, owing to various constraints, e.g. small herd size, lack of systematic identification, inadequate animal performance and pedigree recording, organisational shortcomings, etc. Nucleus breeding programme can be a good strategy for genetic improvement of cattle in developing countries which lack the money, expertise and structure required for operating an efficient improvement programme based on artificial insemination (AI) and field recording in the whole population (Smith, 1988). Such a programme does not require expensive infrastructure because recording is only done in the nucleus herd. The best males are kept for breeding in the nucleus while the remaining selected males are for mating in the co-operating herds. Cunningham (1980) described an open nucleus programme in the absence of AI and/or milk recording appropriate for subsistence (smallholder) production systems. Bondoc and Smith (1993) recommended the establishment of two-tier open nucleus breeding systems (rather than an unstructured population) to maximise genetic improvement, reduce inbreeding rate and reduce the total cost of recording in smallholder dairy cattle production systems. An open nucleus breeding system is superior to a closed nucleus of the same size because of a higher expected mean genetic value of nucleus replacements and because such a system will integrate farmers resources, reduce overhead costs and encourage more farmer participation (Bondoc and Smith, 1993). Maximising profit rather than genetic gain might be of major interest for establishing breeding programmes in livestock of non-industrialised countries where there is no international competition (Nitter, 1998). Owen (1975) showed that a young bull system (YBS) has lower costs and gives more profit than the conventional old bull system (OBS). In a YBS, bulls

are used to sire both dams in the nucleus and commercial sector and are slaughtered after a limited amount of semen has been collected and stored until daughter records are available to select semen from the best bulls for planned matings (Niebel and Fewson, 1978). Mpofu et al. (1993) made an economic evaluation of breeding strategies for commercial dairy cattle and reported highest net present values in strategies that utilised untested young bulls (YB) after an investment period of 25 years. Nitter (1998) compared the YBS to the OBS and found that it was superior in profit per cow and that it had an advantage when the time horizon considered was short, the resources were limited and interest rates were high. The latter two are the common problems that characterise most of the non-industrialised countries. Using a deterministic approach, this paper examines a two-tier open nucleus breeding scheme using the YBS with a view of evaluating the genetic and economic efficiency of various dairy cattle breeding objectives and schemes. According to the structure of livestock production in Kenya, private large-scale herds or herds owned by parastatal or research organisations are considered to be the nucleus while other smallholder herds considered to be the commercial sector supplied by YB from the nucleus.

2. Material and methods The computer programme ZPLAN (Karras et al., 1997) was used for this purpose. Based on the biological, technical and economic parameters, ZPLAN calculates the annual genetic gain for the breeding objective, genetic gain for single traits and return of investment adjusted for costs (profit) using the gene-flow method and selection index procedures. These calculations assume that the parameters and selection strategies are unchanged during the investment period and consider only one round of selection. Reduction of the genetic variance due to selection and inbreeding is ignored. The programme calculates selection indices for breeding animals and applies order statistics to obtain adjusted selection intensities for populations with finite sizes. The calculations used by ZPLAN have been described by Nitter et al. (1994). The programme has been used for various

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pasture feed for the cows. The fourth objective (future with limitation, FUWL) was similar to the third but assumed that there was limitation on pasture feed. CUWL and FUWL assumed that the breed is used in some smallholder herds for milk production where heavier cows are undesirable because feed resources are scarce and their availability is variable (Bebe, 2003). In such a situation, the production potential of the cow cannot be fully expressed. As a consequence, the economic values of traits that influence feed intake would be low (Kahi and Nitter, 2004). The breeding objective comprises breeding values and economic values for traits that directly influence either revenue or cost of the production enterprise. Traits considered in the breeding objectives included milk yield (MY), fat yield (FY), age at first calving (AFC), calving interval (CI), preweaning daily gain (DG), postweaning daily gain (PDG), mature live weight (LW), preweaning survival rate (SR), postweaning survival rate (to 18 months) (PSR) and productive lifetime (PLT). The economic values for these traits in different breeding objectives were taken from the results of another study (Kahi and Nitter, 2004) and are presented in Table 1. These economic values are undiscounted and weighting by discounted expressions is done internally in ZPLAN (Karras et al., 1997).

purposes, recent examples are those to evaluate different genetic improvement schemes by Kasonta and Nitter (1990) for dual-purpose cattle, Graser et al. (1994) for beef cattle, Kominakis et al. (1997) for sheep and Wu¨nsch et al. (1999) for pigs. 2.1. Breeding objectives The evaluation of the efficiency of alternative selection schemes requires first the definition of a breeding objective. Since market demands are normally dynamic and change over time, four possible breeding objectives were examined. The breeding objectives differed in the marketing scenario (whether current or future) that each described and whether pasture feed for the cows was limited or not. The breeding objectives were dual-purpose. The first breeding objective (current no limitation, CUNL) reflected the current scenario where milk is paid on volume, without limitation on pasture feed. The second objective (current with limitation, CUWL) also reflected the current scenario but there was limitation on pasture feed. The third breeding objective (future no limitation, FUNL) reflected a presumed future marketing scenario where payment of milk would be based on volume and fat content. This objective assumed that there was no limitation on

Table 1 Assumed phenotypic standard deviation (rp), heritabilities (h2), economic values (v) for the traits in the breeding objectives and genetic correlations (below diagonal) and phenotypic correlations (above diagonal) among selection criteria and breeding objective traits Traita

rp

1h2

v (KSh) per unitb CUNL CUWL

MY (kg) 1208.46 0.23 FY (kg) 41.30 0.10 LL (days) 53.41 0.12 AFC (days) 448.76 0.33 CI (days) 75.34 0.06 DG (g/day) 19.00 0.29 PDG (g/day) 743.00 0.32 LW (kg) 54.14 0.30 SR (%) 30.00 0.09 PSR (%) 30.00 0.09 PLT (days) 864.90 0.11 a

18.93 2.76 – 2.72 2.65 1.04 3.40 7.95 9.96 45.15 0.07

Genetic and phenotypic correlationsc FUNL FUWL

15.29 16.05 140.46 79.44 – – – – – – – – – – 43.15 – – – – – – –

12.25 62.76 – – – – – 45.03 – – –

MY FY 0.75 0.72 0.17 0.17 0.10 0.10 0.23 0.00 0.00 0.00

LL

0.75 0.72 0.95 0.95 0.10 0.06 0.08 0.15 0.10 0.10 0.10 0.10 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00

AFC 0.20 0.05 0.04 0.21 0.25 0.25 0.15 0.00 0.00 0.13

CI

DG 0.11 0.08 0.15 0.21 0.00 0.00 0.53 0.00 0.00 0.10

0.10 0.10 0.10 0.25 0.00 0.25 0.40 0.06 0.03 0.10

PDG 0.11 0.11 0.10 0.25 0.00 0.49

LW SR – – – – – – –

– – – – – – – –

PSR – – – – – – – – –

0.47 0.03 0.00 0.06 0.00 0.01 0.10 0.27 0.00 0.00

MY, milk yield; FY, fat yield; LL, lactation length; AFC, age at first calving; CI, calving interval; DG, preweaning daily gain; PDG, postweaning daily gain; LW, mature live weight; SR, preweaning survival rate; PSR, postweaning survival rate (to 18 months); PLT, productive lifetime. b See text for the description of breeding objectives. When no values are presented, the economic values did not differ from those in CUNL. c Phenotypic correlations only required among selection criteria.

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Table 2 Population, biological and technical parameters for the nucleus and the commercial sectora Nucleus Population parameters Number of cows Percentage of cows mated by YB from nucleus Bull to cow ratiob Percentage of cows in nucleus coming from the commercial sector Percentage of bull dams in the nucleus PLT (years) OB for planned matings (groups 1 and 4) YB (group 3) YB from the nucleus used in the commercial sector (groups 7 and 9) Bulls bred in the commercial sector (group 10) Bull dams (group 2) Cows (groups 5 and 6) Cows (groups 8 and 11) AFC (years) OB for planned matings (groups 1 and 4) YB (group 3) YB from the nucleus used in the commercial sector (groups 7 and 9) Bulls bred in the commercial sector (group 10) Bull dams (group 2) Cows (groups 5 and 6) Cows (groups 8 and 11) Survival and reproduction Survival of calves (%) Calves born per cow and year Male calves suitable for breeding Female calves suitable for breeding CI of cows (years) Male calves for planned matings culled

Commercial

2500

47,500 70

1:500 20

1:500, 1:50

Table 2 (continued) Nucleus AI Number of straws per YB reserved for planned matings Number of straws per YB and year used in the commercial sector Number of straws required per pregnancy Wait period for bulls (years)

Commercial

1000 1000

2

2

4

a

5

Selection groups are denoted in Fig. 1. b The bull to cow ratio was 1:500 and 1:50 for AI and natural mating, respectively.

1.3

2.2. Population structure and selection groups

2.6 4.0

4.0

2.6 4.2 5.2

7.6 2.8 2.8

2.8 5.6 2.8 3

93 0.95

85 0.60

0.80

0.75

0.90

0.85

1.10 0.15

1.62

An open nucleus breeding system and a YBS are considered. The breeding system is a two-tier structure comprised of the breeding unit (nucleus) and the commercial (C) sector. The nucleus (N) was the tier that generates genetic gain and where sire selection was the main activity. Population, biological and technical parameters for the nucleus and the commercial sector are presented in Table 2. A total population of 50,000 cows is considered and the size of the nucleus with performance and pedigree recording is 5% (2500 cows) of this population. Hundred percent AI is assumed in the nucleus and MOET is not practised. Fig. 1 shows the breeding structure and selection groups coded from 1 to 11. Selection of old bulls (OB) occurred to produce both bulls and cows used in the nucleus (OB to breed sires, SNOB>SN and OB to breed dams, SNOB>DN) while YB were the sires of dams (YB to breed dams, SNYB>DN). A small collection of semen was stored per YB until the first batch of daughters had been tested. Selection of dams also occurred to differentiate between elite cows (dams to breed bulls, DN>SN) and all cows in the nucleus (dams to breed cows, DN>DN). The proportion of bull dams (DN>SN) was assumed to be 5% of the nucleus. The last selection group was complemented by dams from the commercial sector. This was a separate group to produce cows in the nucleus (DC>DN). Twenty percent of the dams in the nucleus belonged to this group. The bull dams (i.e. the DN>SN selection group) were inseminated with stored semen from the best bulls (i.e. SNOB>SN). Only YB were used in the commercial sector to produce cows (SYB N >DC)

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Fig. 1. The breeding structure and selection groups coded from 1 to 11: SNOB, SNYB and DN, old bulls, young bulls and dams, respectively, in the nucleus; SC and DC, sires and dams, respectively, in the commercial sector.

and to sire bull calves that were kept for use to mate a proportion of the cows in this sector (SYB N >SC). These calves became sires that were used in this sector to produce cows (SC>DC) but their sons were not used for breeding. Seventy percent of cows in the commercial sector were mated to YB from the nucleus. Half of these YB were used for AI. In this sector, the bull to cow ratio was 1:500 and 1:50 for AI and natural mating, respectively. Thirty percent of commercial cows were naturally mated by sons of bulls from the nucleus that were born and reared within the commercial sector (SC>DC). The dams in the commercial sector were used to produce both bulls and cows in this sector (DC>SC and DC>DC). Construction of the transmission matrix in geneflow method requires estimates of PLT, survival rate, AFC and CI for each selection group. These and other values shown in Table 2 are also required to calculate the proportion of selected animals and selection intensities. Variable and overhead costs are shown in Table 3 and include costs that were directly related to performance and pedigree recording. These costs occur exclusively in the nucleus; they are based on Kahi (2000) and experiences. The fixed costs were those incurred in one round of selection and were the overhead costs of running the nucleus of 2500 cows. The average time when fixed costs occur was assumed to be the mean

generation interval. Variable and fixed costs only affect the profit but not the genetic response. The returns were discounted at a rate of 5% and costs at 3%. Generally, the appropriate inflation adjusted discount rates in long-term investments such as genetic improvement should always be low—in the 2– 5% range (Bird and Mitchell, 1980). An investment period of 25 years was assumed. 2.3. Genetic and phenotypic parameters Genetic and phenotypic parameters for the selection criteria and the traits in the aggregate genotype are required in order to calculate the composition and the accuracy of selection indices. Ideally such estimates should come from experiments with the particular population used in the breeding system and should include all important traits. This is difficult to achieve and estimates are usually developed from a search of the literature (Koots et al., 1994a,b). Therefore, for this study, estimates were derived from other studies conducted in Kenya (Kahi, 1995; Mackinnon et al., 1996; Musani and Mayer, 1997; Njubi et al., 1992; Ojango and Pollot, 2001; Rege, 1991a; Rege and Mosi, 1989). Where such estimates were missing from the Kenyan studies, other tropical studies were consulted (Abubakar et al., 1986; Campos et al., 1994; Tawo-

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Table 3 The variable and overhead costs of the nucleus (2500 cows) and the average time of cost occurrences Cost elementa Variable costs Animal identification, pedigree recording, electronic data processing, etc. costs per cow Weighing platform costs per animal Weighing scale costs per cow Laboratory costs per cow per lactation (for determination of fat content) Cost for recording MY per cow per year Cost for milk sample taking and recording FY per cow per year Cost for recording LL per cow Cost for recording AFC per cow Cost for recording CI per cow Cost for recording birth weight per calf Cost of recording weaning weight per calf Cost of recording 18 months weight per animal Cost of type assessment of bull calf Cost of type assessment of bull dams Cost per straw of semen collection Cost per straw of semen storage Cost per straw of handling insemination Overhead costs (fixed costs) Livestock manager Secretary Additional staff (e.g. field officer) Data processing (30% of salaries) Transport (20% of salaries) Other costs (20% of salaries) Total a

Costs (KSh)

Years

150

3

7

0

3 1000

3 3

750

3

1000

3

150 125 150 20

3 3 3 0

40

0.25

60

1.48

150 150 50 100 25

0.10 0.10 0.75 1.50 2.25

720,000 360,000 1,100,000 654,000 436,000 436,000 3,706,000

See Table 1 for definition of traits.

nezi, 1989). In addition to these sources, the comprehensive review of Loˆbo et al. (2000) on genetic parameters for beef and dairy cattle in tropical regions was also consulted. In situations where the estimates of phenotypic correlations were found but not genetic correlations, and vice versa, the phenotypic and genetic correlations were assumed to be equal (Koots et al., 1994a,b). Genetic and phenotypic variance/ covariance matrices were checked to ensure they were

positive definite. The genetic and phenotypic parameters used are presented in Table 1. The repeatability estimates for MY, FY, lactation length (LL) and CI were assumed to be 0.55, 0.50, 0.50 and 0.46, respectively (Rege, 1991a,b). 2.4. Breeding schemes, selection criteria and index information Since the main questions for establishing a breeding programme are those on return to investments, breeding schemes were defined which differed in the records available for use as selection criteria. The schemes evaluated were: Scheme 1: Records for AFC and CI (fertility recording). Scheme 2: Scheme 1 criteria plus records for weights at birth, weaning and 18 months which were used to calculate DG and PDG (fertility plus weights recording). Scheme 3: This scheme included selection criteria that are currently recorded by most dairy farms in Kenya (Musani and Mayer, 1997; Ojango and Pollot, 2001) and was, therefore, the base situation. The criteria included scheme 1 criteria plus records on LL and MY (fertility plus MY recording). Scheme 4: Scheme 2 criteria plus records on MY (fertility, weights plus MY recording). Scheme 5: Scheme 4 criteria plus records on FY (fertility, weights, MY plus FY recording). The effect of these breeding schemes was seen in the way they influenced the genetic response, returns and profit. Breeding schemes, which require increased levels of performance recording and genetic evaluation, have increased costs, an effect directly attributed to the scheme. In all schemes, selection was based on the best index of the available criteria. For selection groups 1 –7, information sources included records on individual, sire, dam, dams of the sire and dam, female paternal half sibs (PHS) and female half-sibs of the sire (HSS) and dam (HSD). In addition, selection groups 1 and 4 included records on their daughters (PRO). For these selection groups, it was assumed that PHS, HSS, HSD and PRO had 10 performance records each.

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Cows in selection group 6 were selected subjectively after their first lactation. This was based on an index that included records on the individual’s MY on a particular test day. In some smallholder production systems, technical performance at the individual farm level is low (because of low reproductive rate, high wastage, etc.) resulting in, at best, a stable herd size, and probably a declining herd indicating that the scope for selection amongst replacements is small (Bebe, 2003). The potential for movement of cows from an individual smallholder farm to the nucleus herd may, therefore, be limited. Consequently, it was assumed that the commercial population comprises of village herds each with several individual smallholder farms and, therefore, a considerable number of cows from which replacements can be selected. Selection groups 8, 10 and 11 were selected on an index not correlated with the breeding objective and so did not

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contribute to generating genetic gain and economic returns. 2.5. Variation of the nucleus size and level of usage of young sires in the commercial sector The effect of altering nucleus size on profitability was investigated. The nucleus size was varied from 2% to 99.9% of the total population at the assumed usage level of young sires from the nucleus in the commercial sector (i.e. 70%). In addition, the impact of nucleus size at lower and higher levels of usage of young sires in the commercial sector, was also investigated assuming the overhead costs of the breeding programme to be unaffected. This investigated the influence of nucleus size and the extent to which improved sires are used in the commercial sector on the overall profitability of a breeding programme.

Table 4 Accuracy of selection (correlation between index and breeding values), genetic superiority and total return of the major selection groups for the breeding objectives addressing the current (CUNL and CUWL) and future (FUNL and FUWL) marketing scenarios; base situation Charactera

Selection groupsb 1 SNOB>SN

Number of tested animals Number of selected animals Generation interval (years)

2 DN>SN

3 SNYB>DN

4 SNOB>DN

5 DN>DN

6 DC>DN

7 SNYB>SC

9 SNYB>DC

38

595

44

38

994

10,296

884

884

3

48

38

3

476

119

91

91

7.83

6.59

3.70

7.83

5.06

5.06

4.54

4.54

0.68 0.67 0.67 0.65

0.40 0.40 0.39 0.38

0.46 0.45 0.45 0.44

0.68 0.67 0.67 0.65

0.38 0.37 0.37 0.36

0.46 0.44 0.46 0.44

0.46 0.45 0.45 0.44

0.46 0.45 0.45 0.44

Genetic superiority (KSh) CUNL 12,314.79 CUWL 8215.16 FUNL 11,236.91 FUWL 6940.30

8589.64 5742.99 7791.00 4810.18

1420.48 949.26 1291.02 797.53

12,314.79 8215.16 11,236.91 6940.30

3657.30 2446.65 3312.83 2045.74

0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

Total return (KSh) CUNL CUWL FUNL FUWL

1208.41 809.75 1097.88 681.23

101.57 68.10 92.51 57.54

25.78 17.29 23.59 14.72

185.41 124.37 168.30 104.57

165.75 109.24 152.92 94.20

437.40 292.84 398.01 246.82

2778.27 1859.57 2527.64 1566.65

Accuracy of selection CUNL CUWL FUNL FUWL

a b

1495.06 1001.47 1367.22 851.15

See text for description of breeding objectives. See text for description of selection groups.

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3. Results and discussion 3.1. Accuracy of selection, genetic superiority and returns of the major selection groups Results for seven selection groups with parents reared in the nucleus and one selection group with parents reared in the commercial sector but introduced into the nucleus (group 6) are summarised in Table 4. The other selection groups are not relevant since they do not contribute to the genetic response and do not incur breeding costs. The number of tested and selected animals for each selection group is calculated from the parameters shown in Table 2. The longest generation intervals and the highest selection accuracies were realised by OB in the nucleus (groups 1 and 4), which also had the highest genetic superiority for the breeding objective. The values of these two bull groups were equal because they were all born in the nucleus and are selected based on the same information. This emphasises the importance to genetic response of placing more emphasis on sire selection for the nucleus. The genetic superiority for the breeding objective for groups 6, 7 and 9 was zero. These groups were born in a tier that was different from where they were used. Group 6 was subjectively selected in the commercial sector. Groups 7 and 9 were pre-selected groups where the better proportion of males selected in the nucleus is used within the nucleus. The remainder of the selected males is used for mating in the commercial sector and, therefore, these males did not contribute to the genetic superiority in the nucleus. For all selection groups, the genetic superiority and returns were highest in the breeding objectives that assumed no limitation on pasture feed for the cows (CUNL and FUNL). Group 9 had the highest and group 4 the lowest contribution to the total return. The highest returns in group 9 emphasise the need for a commercial sector to make breeding programmes economically efficient. 3.2. Annual monetary genetic gain, returns and profit The annual monetary genetic gain, returns and profit per cow of the population are shown in Table 5 for the different breeding objectives and schemes. The annual monetary genetic gain, returns and profit per cow for all

Table 5 Annual monetary genetic gain, returns and profit of breeding objectives addressing the current (CUNL and CUWL) and future (FUNL and FUWL) marketing scenarios Breeding objectivea

Breeding Annual Total return Profit per schemea monetary genetic per cow cow gain (KSh) (KSh) (KSh)

CUNL

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

CUWL

FUNL

FUWL

162.73 503.20 1187.24 1308.44 1337.21 153.08 321.46 792.90 857.75 885.88 123.59 470.35 1080.14 1195.45 1217.76 124.79 284.03 667.04 723.45 744.22

1000.41 3465.42 6453.87 7193.57 7395.00 874.67 2424.60 4285.94 4821.81 4929.93 812.81 3306.80 5884.27 6626.35 6725.72 756.55 2248.22 3622.38 4164.36 4255.32

564.93 3024.35 5984.93 6719.03 6776.34 439.21 1983.53 3816.99 4347.28 4401.27 377.33 2865.73 5415.33 6151.82 6177.06 320.55 1807.15 3153.43 3689.83 3706.66

a

See text for the description of breeding objectives and schemes. 1 US dollar = 60 KSh.

schemes varied within breeding objectives. They were higher in CUNL than in the other breeding objectives since the production system for this objective included higher value for MY and LW. Therefore, basing payment of milk on volume and fat content has little advantage to the overall profitability of the breeding programme. The high value of FY in FUNL did not compensate for the reduction in the value of MY and LW in this objective. The low annual monetary genetic gain, returns and profit per cow in CUWL and FUWL was expected because under these objectives the genetic improvement of MY, FY and LW would be less profitable. In that case, the production potential of the cow cannot be fully expressed. Generally, within each objective the annual monetary genetic gain, return and profit per cow was highest for the breeding scheme with the highest level of investments. In all objectives, there was a big difference in the annual monetary genetic gain, return and profit per cow between schemes 1 and 2. Within each breeding objective, the large difference

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(12 – 17%) between schemes 3 (base situation) and 4 emphasises the importance of including the weight measurements as selection criteria in dual-purpose breeding objectives. The cost of measuring weights is offset by the benefits that accrue. In Kenya, very few dairy farms record weights of their animals (Kahi et al., 2000). Therefore, as a recommendation towards the establishment of effective and sustainable breeding programmes for dairy cattle, dairy farms that could likely be nucleus herds supplying smallholder farmers with superior genetic material should be encouraged to record weights. A comparison of the profit per cow of schemes 4 and 5 in all objectives shows very little difference (0.4 – 1.2%) indicating that there is little benefit including FY as a selection criterion. This was due to the high costs incurred for milk sampling, laboratory determination of fat content and recording FY. The high costs associated with a breeding scheme should not be ignored, as this can be a significant barrier to the adoption of any beneficial technology in practical breeding programmes. Results demonstrated that breeding objectives that have a dual-purpose nature would represent an efficient and realistic objective for the improvement of dairy cattle in Kenya. In order to achieve this under the current production conditions, selection should be based on all the traits represented in the breeding objective but with no measurement of FY. A breeding scheme that requires records on FY seems not to be justified from an economic point of view especially in situations where these records are to be obtained from all cows in the nucleus, which was assumed in this study. To reduce the overall costs of recording FY in the nucleus, simplified recording methods can be adopted, for example by reducing the number of cows tested and/or the samples taken from each cow per lactation period. While the possibility of payment based on milk volume and fat content and hence of widespread use of fat recording in Kenya seems unrealistic for the next several years, this suggestion could serve as a guideline for future breeding activities. Further studies, however, are required to examine the relationship between cost and accuracy of selection, and the overall effect on response to selection as the number of measurements of fat content decreases.

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3.3. Annual genetic gain in individual traits The annual genetic gain in individual traits of the breeding objectives and schemes are shown in Table 6. The pattern in the annual genetic gain in different breeding schemes was similar in all breeding objectives. With utilisation of additional criteria, the response in MY was increased. Recording of AFC and CI in scheme 1 increased the gains in these traits. Similarly, inclusion of DG and PDG in scheme 2 markedly increased genetic response in DG, PDG, LW and PLT. With the introduction of MY in scheme 3, there was a profound effect on the annual genetic gain of the milk production traits (MY, FY and LL). Generally, in the schemes (schemes 3 – 5) that included MY as an additional criterion, the genetic response in milk production, fertility (AFC and CI) and growth traits (DG, PDG and LW) was positive. This was as a result of the positive genetic correlations existing between these traits and MY. High yielding animals are normally associated with more days nonpregnant and hence longer CI, are older at first calving (Campos et al., 1994; Ouweltjes et al., 1996) and heavier at maturity. Increasing AFC, CI and LW is undesirable, particularly in production systems in which there is high demand for pregnant or lactating heifers and feed resources are scarce and their availability is variable, as in Kenya. Scheme 2 can be compared with scheme 4 because they only differ in whether MY was included as a selection criterion or not; correspondingly, scheme 4 can be compared to scheme 5. A comparison of schemes 2 and 4 showed that utilisation of extra criteria will lead to higher gains in MY, FY, LL, AFC and CI and to lower gains in DG, PDG, LW and PLT in scheme 4 than in scheme 2. There were very small differences in the annual genetic gain in most traits between scheme 4 and 5. These small differences reflect the generally low genetic correlation between these traits and FY. In all the objectives, the availability of additional selection criteria had little effects on the annual genetic gain in SR and PSR, a small gain occurring only with the availability of DG and PDG records (schemes 2, 4 and 5). These small changes reflect the generally low genetic correlations existing between these traits and the available criteria. Of interest is the comparison of schemes across the different breeding objectives. As expected,

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Table 6 Annual genetic gain for individual traits in selection criteria and breeding objectives addressing the current (CUNL and CUWL) and future (FUNL and FUWL) marketing scenarios Breeding objectivea

Breeding scheme a

Traitsb

CUNL

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

11.71 15.30 63.16 64.20 66.27 9.19 14.21 62.73 62.64 64.83 9.55 14.32 62.74 63.22 65.24 5.50 12.58 62.14 62.77 62.95

CUWL

FUNL

FUWL

a b

MY (kg)

FY (kg) 0.02 0.17 0.90 0.91 0.91 0.02 0.17 0.85 0.85 0.84 0.02 0.18 0.94 0.94 0.94 0.07 0.18 0.90 0.88 0.87

LL (days) 0.23 0.40 1.23 1.28 1.35 0.21 0.38 1.15 1.17 1.26 0.21 0.38 1.28 1.31 1.39 0.16 0.35 1.21 1.21 1.30

AFC (days) 12.55 0.78 8.49 5.93 9.05 4.51 2.15 7.94 5.31 9.00 5.55 3.00 7.40 4.65 7.52 4.64 4.87 6.06 3.17 6.60

CI (days) 0.83 0.17 0.30 0.27 0.30 1.04 0.32 0.39 0.35 0.38 1.02 0.17 0.30 0.27 0.29 1.15 0.34 0.41 0.36 0.39

DG (g/day)

PDG (g/day)

0.16 0.49 0.07 0.26 0.25 0.08 0.29 0.07 0.17 0.16 0.09 0.50 0.08 0.27 0.26 0.01 0.27 0.09 0.17 0.16

6.39 55.84 2.76 27.93 27.52 3.22 57.07 2.75 30.59 29.94 3.64 57.49 3.26 29.84 29.47 0.54 59.01 3.57 34.05 33.45

LW (kg) 0.64 2.11 0.87 1.78 1.68 0.85 1.79 0.82 1.70 1.59 0.82 2.11 0.84 1.77 1.70 0.98 1.73 0.76 1.69 1.59

SR (%)

PSR (%)

0.00 0.05 0.00 0.02 0.02 0.00 0.04 0.00 0.02 0.02 0.00 0.05 0.00 0.02 0.02 0.00 0.04 0.00 0.02 0.02

0.00 0.08 0.00 0.03 0.03 0.00 0.08 0.00 0.04 0.04 0.00 0.08 0.00 0.04 0.04 0.00 0.08 0.00 0.04 0.04

PLT (days) 0.61 3.53 0.47 1.19 1.01 0.65 3.58 0.27 1.33 1.13 0.50 3.84 0.34 1.39 1.22 1.94 3.93 0.03 1.68 1.49

See text for the description of breeding objectives and schemes. See Table 1 for description of traits.

breeding schemes in FUWL had the lowest genetic gains in MY and LW while those in CUWL the lowest gains in FY. A large increase in FY and LW of cows was of course not desired in CUWL and FUWL because of the negative economic value of these traits (Table 1). It appears that the mean market value and/or the amount of genetic variation for FY and LW need to be larger for changes in these traits to impact positively in dairy breeding programmes when there is limitation on the availability of pastures for the cows. 3.4. Variation of the nucleus size and level of usage of young sires in the commercial sector Fig. 2 presents the impact of varying the size of nucleus on profit per cow at the assumed usage level of young sires from the nucleus in the commercial sector (i.e. 70%). Only results for scheme 4 of CUNL and FUNL are presented since the trend was similar in the other schemes and objectives. While increasing the size of the nucleus

resulted in similar trends in profit per cow in all the breeding objectives, the optimal size was different. For example, for CUNL the nucleus size was optimal at 20% of the total population, while this was so at 15% of the population for FUNL. This is contrary to the observation made by Kasonta and Nitter (1990) for the Mpwapwa cattle in Tanzania and Kominakis et al. (1997) in the Karagouniki dairy sheep in Greece. In those studies the optimal nucleus size was 5% of the total population. This study suggests that the amount of recording and hence the optimal size of the nucleus changes with the breeding objective. A large nucleus is always advantageous in breeding programmes that are already established and those in which genetic gain is more crucial than costs. Large nucleus sizes would provide the whole programme with bulls of superior genetic merit because of a high intensity of selecting bull dams, a large testing capacity of YB and minimal inbreeding rates. In breeding programmes that are beginning, larger nucleus sizes might be limited by the associated higher costs.

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Fig. 2. Impact on profit per cow of varying the size of the breeding nucleus under different levels of usage of improved young sires in commercial sector for scheme 4 of breeding objectives addressing the current (CUNL) and future (FUNL) marketing scenarios (proportion of bull dams = 0.05 of the nucleus, degree of opening the nucleus = 20% and level of usage of young sires: = 20%; = 40%; X = 60%; E = 70%; n = 80% and x = 100%).

.

Fig. 2 also presents results for the impact on profit per cow of varying the size of the breeding nucleus under different levels of usage of young

sires in commercial sector. Profit per cow in the population increased markedly with increase in the usage level, the effect initially increasing with

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nucleus size and then decreasing as nucleus size approached whole population recording. When the whole population is recorded and sires are sourced from within this population, the intensity of selecting these sires decreases because of the need for more bulls to service the population. Profit per cow was low at lower levels of usage of young sire in the commercial sector. This indicates that a breeding programme requires the commercial sector through which genetic improvement can be spread to make an intensive recording and selection programme a viable investment. 3.5. Establishment of a breeding programme In this study, the methodology of an open nucleus breeding scheme and YBS was used to evaluate alternative breeding objectives and schemes in dairy cattle. Since market demands are dynamic and change over time, the four breeding objectives examined various possible market and production scenarios while the breeding schemes differed in the extent of recording selection criteria and hence in the level of investments. In most of the non-industrialised countries, production and marketing conditions are unstable, interest and inflation rates are high and, therefore, assumptions about the breeding objectives, selection criteria and other aspects may change. Consequently, for a breeding programme under the conditions of a non-industrialised country breeding programme, an investment period of 25 years (equivalent to about five generations in cattle) is considered very long. This study has demonstrated that an well-organised two-tier open nucleus breeding programme and YBS would be profitable and result in overall improvement of production in dairy cattle. The question would be how this programme would be established and introduced without severe organisational problems. This would require interaction between possible stakeholders in the breeding programme (Rege et al., 2001). To avoid repeating the same mistakes, organisational lessons can be learnt from the experiences of the few examples of nucleus breeding schemes in the tropics (Dempfle and Jaitner, 2000; Penna et al., 1998; Rege and Wakhungu, 1992; Yapi-Gnaore´ et al., 1997). The first step would be to introduce a nucleus in a number of existing private large-scale herds or

herds owned by parastatal or research organisations. The number of these herds will have to be determined by their respective herd sizes, infrastructure, the total population size and their willingness to join the breeding programme and co-operate with the other participating nucleus herds. Bringing together private large-scale herds and making them to cooperate rather than compete would be difficult. However, with incentives such as; less taxation (would require government intervention), a percentage of profit accrued from the sale of semen from a bull bred and raised in the herd, long-term contracts for the supply of bulls to farmers for natural mating, discounts on electronic data processing and breeding advice, etc. many herds will be willing to participate in the breeding programme. Since the objective of these herds is to maximise profit, it should be demonstrated to them through a cost –benefit analysis as to how their participation will influence their objective. There are usually many unplanned benefits from a coordinated breeding programme involving data collection in that participants learn much more about the actual performance of their animals and use that to boost their technical expertise and profits. The nucleus can also be established in the form of a private enterprise owned by farmer co-operatives or individuals, and can initially be funded by loans from financial institutions or governments and/or farmers willing to join. Co-operatives normally work well when the membership is large and where incentives exist, in this case, affordable superior genetic material. The selection work in such an enterprise would be relevant and practical by involving breeders and farmers in its operation or even in owning the stock, as in group breeding schemes (Smith, 1988). Increased revenues from sale of milk, culls and genetic material would absorb the costs of such an enterprise. The second step would be to open the nucleus by finding farmers who are willing to join the scheme whose specific needs and social circumstances as well as ecological constraints must be incorporated in the breeding objective (So¨lkner et al., 1998). Use should be made of existing advisory services, structures and ways of transfer of knowledge to further select farmers based on certain criteria. These could include property right on land, ease of access to farm by car or motorcycle, access to water source, size of herd,

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ability to be able to keep at least some records, to follow prophylactic programmes and to supplement their animals during critical periods, willingness to allow their best animals be introduced in the nucleus or be used for elite mating with semen from the nucleus, etc. Most farmers may be reluctant to provide their best animals but various ways of incentives may be taken into account. For example, this might be that profit accrued from the sale of animal’s milk is transferred to the farmer or that these animals are incorporated in the nucleus on a temporary basis or on lease, although disease prevention may be prohibitive. Alternatively, farmers could retain their best animals and elite ‘nucleus’ mating done in the commercial population with migration of semen from the nucleus to these herds. Such an organisation would rely on good pedigree and performance information, which the farmer would be expected to keep under the supervision of the nucleus. For the programme to sustain itself, a compromise would have to be reached on what would be the cost of this genetic material (YB or semen) leaving the nucleus. In situations where natural mating is preferred, the bulls would be a community property. They would be in the care of a bull keeper who would be remunerated by the farmers through payments for natural mating. It would be the responsibility of the bull keeper to maintain records of services for payment and parentage to check the inbreeding levels. Bulls in natural service would be used for a certain period (e.g. 3 years) and then replaced with YB from the nucleus. Associated veterinary services will be required, however, to control breeding diseases.

4. Conclusions For the first time in Kenya, different breeding objectives each with varying levels of investments in terms of the amount of information available as selection criteria were compared. Reference was made of the use of YB in a two-tier open nucleus breeding system. Such knowledge is a prerequisite for successful establishment of breeding programmes with dual-purpose objectives in any cattle breed in Kenya. However, the response would be much lower for lower yielding breeds. There is evidence that under the current production and marketing condi-

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tions, an well-organised nucleus utilising the smallholder as the commercial population could sustain itself. However, before any breeding programme is established on a large-scale, pilot selection schemes should be developed first and shown to work. Complex breeding schemes and technologies to increase genetic gain and profitability can then be introduced later. During all the establishment stages, the needs and interests of the producers as well as the ecological conditions should seriously be taken into consideration. There is, therefore, the need for further studies on how this can be realised and how improved genetic material can be delivered to cattle owners, especially smallholder producers.

Acknowledgements The helpful comments and suggestions of reviewers are gratefully acknowledged. We thank the German Academic Exchange Service (DAAD, Bonn, Germany) and Hohenheim University (Stuttgart, Germany) for funding the work and Egerton University (Njoro, Kenya) for provision of facilities. Thanks to J. Gibson, W. Thorpe and C. Wollny for reviewing an earlier version of this manuscript.

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