Evaluation of alternative selection objectives and schemes for optimisation of village goat improvement programs

R C Bett^*#, A M Okeyo^#, I S Kosgey^**, A K Kahi^** and K J Peters

Department of Animal Breeding in the Tropics and Sub-Tropics, Humboldt University of Berlin, Philippstraße 13, 10115 Berlin, Germany
* Swedish University of Agricultural Sciences (SLU), Department of Animal Breeding and Genetics, P.O. Box 7023, SE-750 07 Uppsala, Sweden
** Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, P. O. Box 536, Egerton 20115, Kenya
# International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya
rawlynce@yahoo.com

Abstract

This study evaluated alternative breeding objectives and schemes for crossbred goats in a village/ community breeding program. A single-tier breeding structure was assumed in the optimisation of this breeding program. Considered were two selection schemes; within-group (WG) and across-groups (AG), and three alternative selection objectives; ALT I- defined based on relative weights (RWs) derived from producers’ preferences, ALT II- based on economic values (EVs) without risk and ALT III- based on risk-rated EVs, at different intensities of buck selection (proportion of bucks selected, P = 0.02 and 0.04) and selection criteria (mass and BLUP).

The genetic gains (ΔG) in the breeding-objective traits, aggregate responses (R_H), total economic response (TER) and the rate of inbreeding (ΔF) per generation varied depending on the scenarios ALT I, II and III, P and the selection criteria. A selection index considering ALT III (Risk-rated EVs) in derivation of ΔG for individual traits, R_H and TER, and ΔF would be appropriate and optimal in both WG and AG selection schemes. However, these responses were higher in the AG scheme compared to the WG selection scheme, and the ΔF more favourable with increase in the number of groups co-operating. Responses under mass selection were also comparable to BLUP with the same rate of inbreeding, restricted to an acceptable level of 0.01. These imply that an AG selection scheme under mass selection would be optimal and logical for implementation in the smallholder low-input goat production systems. However, a minimum of 14 co-operating farmer groups would be required to produce considerable levels of responses and at acceptable levels of inbreeding.

Keywords: Selection objectives and schemes, Crossbred goats, Genetic response

Introduction

Dairy/crossbred goats are important to the livelihoods of many smallholder households in developing countries where they are raised in low-input systems. In Kenya, various bilateral organisations have promoted exotic dairy goat breeds for crossbreeding with the local goats in the smallholder farms (Ahuya et al 2005; Kosgey et al 2006). One major shortfall in these crossbreeding programs, like many others in the tropics (Cunningham and Syrstad 1987; Kahi eta al 2000; Kosgey et al 2006; Musa et al., 2008), is exploitation of the crossbreds without long-term systematic breed development strategies. Utilisation and improvement of the desired crossbred population can be more efficient in situations where breeding programs are well-designed and implemented.

In most of the tropics, there is lack of relevant breed improvement programs for goats, especially crossbreds in the smallholder farms. In Kenya, for example, effective improvement programs for smallholders are scarce owing to various constraints e.g. small population sizes, low levels of organisation and infrastructure, lack of consistent animal identification, inadequate animal performance and pedigree recording, low literacy levels and marketing problems. Village/community breeding programs have been analysed and proposed (Kahi et al 2000; Nimbkar 2000; Sölkner et al 1998), to offset these constraints due to its relative low organisational and operational demands. Sölkner et al (1998) defined village breeding programs as breeding activities carried out by communities of smallholder farmers, often at subsistence level. While Kahi et al (2005) defined these breeding programs as organisations owned by farmers in a community with a common objective of improving livestock through appropriate utilization of genetic resources. These breeding programs can perform functions similar to national genetic improvement schemes (e.g. develop breeding objectives, recording, performance evaluation and selection etc.), and can be organised into breeder and commercial groups. The current smallholder goat improvement programs are more inclined to commercial functions and operate under improved management regimes (Bett et al 2011; Kosgey et al 2008). In addition, they are characterised by a single-tier breeding structures where farmers are simultaneously breeders and producers (van der Werf 2000). This however, contradicts some of the foregoing assumptions.

A key step to the current improvement programs is therefore to identify the existing structures, breeding practices and objectives, and make use of them to build upon a foundation program that will create an opportunity for sustainable genetic improvement (Kosgey and Okeyo 2007; Sölkner et al 1998). The main objective of the current study was to assess the efficiency of village/community breeding programs for crossbred goats in Kenya. Pertinent areas explored in this study are the alternative selection objectives and schemes, and prediction of response to selection, returns to investment and the genetic variability within the desired crossbred population.

Materials and Methods

In Kenya, attempts to overcome socio-economic and structural limitations in dairy/crossbred goat development were targeted at the concerted actions of breeders/farmers associations to coordinate breed improvement activities, particularly in the dissemination of improved stock (Bett et al 2009a; Peacock 2005). An example is the Dairy Goat Association of Kenya (DGAK) program, instigated by the German Technical Cooperation (GTZ) in collaboration with Kenya’s Ministry of Livestock Fisheries and Development in 1992 (Krause 2005). Currently, the DGAK has the largest client base nationwide, comprising 7,660 farmers in 700 farmer groups (DGAK reports, unpublished). In the DGAK organisational model, a sire rotation scheme (buck rotation) was set up to facilitate dissemination of genetically improved goats. Pure German Alpine bucks were imported and distributed among the existing famer groups for natural mating (crossbreeding), at 15-month intervals. It was suggested that a buck serves for four rotations before culling, which is equivalent to a life time of five years. This is a typical example of an old sire scheme (of unproven sires) in small ruminants. Apparently, culling of bucks is mainly based on poor health status or body condition, failure to serve, old age and other abnormalities, and not on any other selection principles, e.g., phenotypic ranking or performance. This decision involves a lot of risk, e.g., slow genetic progress, low reproduction rates or deaths of bucks (Nimbkar 2000; van der Werf 2000). However, it aims at optimally utilising the already available imported bucks, which is confronted by high costs and scarcity of new breeding sires for the crossbreeding program.

Population structures

Design and evaluation of selection schemes relevant to this study were illustrated using parameters for the crossbred goat populations (87.5%) in Kenya (Bett et al 2011). These are crosses between the local Kenyan goat breeds and German Alpine bucks, and subsequent backcrossing to the alpines. The population parameters included the number of does, mating ratios, number of selection candidates per dam and proportion of animals selected. A buck rotation model was assumed in the definition of the population structure. Based on the information earlier mentioned, a farmer group has approximately 11 members, each sharing a buck at every rotation. Assuming an average female flock size per household of 4.7 (Bett et al 2011) one breeding buck would serve 52 breeding does per rotation (which is equivalent to 1:52 sire to dam ratio). The number of candidates available for selection was calculated based on the flock parameters derived using Markov Chain methodology (Bett et al 2011). The approach generated the doe age structure, replacements required and the number of kids born alive per doe based on the breeds’ reproductive, survival and productive performance parameters. Since the interest was on 87.5% genotypes recommended by the program, the number of kids born was taken to be 1.84. It was assumed that all the female offspring were available as selection candidates, which may always be the case.

Breeding objectives and selection schemes

Breeding objectives were elicited from producers’ preferences (Bett 2009; Bett et al 2009a). They consist of the traits: average daily milk yield (DMY, kg); average post-weaning daily gain (ADG, g); mature weight (MW, kg); 12-month live weight (LW, kg); number of kids weaned (NKW); faecal worm egg count (FEC, epg) and mean somatic cell count (SCC, cells/ml). The economic values (EVs) and relative weights (RWs) for these traits are presented in Table 1, and were adopted from Bett (2009) and Bett et al (2011). Briefly, EVs without risk were estimated using simple profit models while the risk-rated EVs were predicted with certainty equivalent profit models. The latter model was derived using (co)variances of input and output prices and price indices (Kenya Shillings- KES, where 1 USD = 70 KES) for the period 2003-2007. The RWs were derived using desired gains selection indices constructed from: first-ranked traits; first and second-ranked traits; first-ranked traits plus trade-offs; and first and second-ranked traits plus trade-offs. Trade-offs includes the traits that were not ranked first or second but were significant and positively correlated to first and second ranked traits (spearman’s rho test).

For most large animals, female fertility is very limited, even though many traits of economic importance are only expressed in females (van der Werf 2000). Consequently, most genetic progress is achieved by progeny testing. However, progeny testing schemes are less appropriate for small fragmented breeding populations (Zumbach and Peters 2007) as, for example, in this case study. Young bucks of 87.5% genotype are, therefore, required for the scheme to take-off. In principle, these bucks would be selected at 12 months of age based on their estimated phenotypic performance (own information sources) and/ or information from the relatives for the traits in the breeding objective. Breeding bucks would be replaced each year or utilised once per generation. However, exceptional bucks (highest ranking) can be mated again, twice or thrice to female flocks owned by other farmer groups through the group-to-group buck rotation or sire exchange scheme already in use.

Two selection schemes were considered; within-group (WG) and across-groups (AG) selection schemes. In the WG scheme, evaluation and selection of replacement animals was carried out in flocks within a farmer group while in the AG schemes, this was done in flocks across several farmer groups, consequently, offering a larger pool of candidates for selection. Three alternative selection objectives were considered in these schemes;

ALT I: defined based on RWs derived from producer’s preferences. This scheme was chosen to validate the conclusions of the analysis of Bett (2009) that; a) RWs could complement the EVs derived using bio-economic models particularly for smallholder production systems where information on true economic parameters are limited and b) preference information elicited from the producers can be used to develop breeding objectives that are relevant from a practical point of view.

ALT II: based on EVs without risk. This scheme reflects a breeding system in the short-term, where improvement in traits is based on the current production circumstances. This scheme was assumed to reflect stable current conditions while avoiding seasonal and cyclical irregularities in marketing and purchasing (Harris et al 1984).

ALT III: based on risk-rated EVs. This scheme was included to examine the effect of future production circumstances on the gains and returns in the breeding schemes. Long-term EVs from improving traits rather than short-term profit has been emphasised (Olesen et al 2000). The scheme considers the fact that knowledge is imperfect and economic circumstances are dynamic in time (Kulak et al 2003).

In each selection objective and scheme the effects of the following were examined:

(i)        The proportion of male selection candidates. The proportion of breeding bucks selected corresponds to the current flock structure (sire to dam ratio, 1:52) and the initially proposed 1:25 sire to dam mating ratio by the program. The proportion of bucks (P) selected were P= 0.02 and P= 0.04, respectively.

(ii)       Selection criteria for male and female candidates. Schemes were compared under mass selection (own information sources) and BLUP (includes information from relatives). Sex limited traits (e.g., DMY for males) or traits expressed after selection (after one year, e.g., NKW) were included as correlated traits under mass selection. This means that males had no information on their own performance, but a female’s individual performance was included in the index.

Genetic and phenotypic parameters

Table 2 shows the assumed phenotypic standard deviations, heritabilities and genetic and phenotypic correlations. These estimates are from the crossbred goats in Kenya (Ahuya et al 2009). Where not available, averages in literature from the tropics were consulted (Baker 1995; Els 1998; Neopane and Pollot 1998; Goncalves and Wechsler 2000; Queiroz et al 2000; Ribeiro et al 2000; Barillet et al 2001; Barillet 2007).  

Prediction of genetic gains and rate of inbreeding

Genetic gains and rate of inbreeding were predicted using deterministic simulation methods using the SelAction program (Rutten et al 2002). Reduction of variance due to the “Bulmer” effect (Bulmer 1971) and correction for finite population sizes and the correlation between index values of family members (Meeuwissen 1991) were taken into account. In this study, flock populations were considered discrete to allow for prediction of inbreeding rates because of the limitation in the SelAction program for overlapping generations. Selection response was predicted for the Bulmer equilibrium situation (Rutten et al 2002). Responses were estimated both for the different breeding-objective traits and the aggregate genotype. Aggregate responses (R_H) were approximated as the proportion of the total economic response (TER) and the genetic standard deviation of aggregate genotype (s_H) i.e., (TER/s_H).

Results

The genetic gains (ΔG) in the breeding objective traits, the R_H, TER and the rate of inbreeding (ΔF) per generation for WG selection scheme are presented in Table 3. These parameters varied depending on the scenarios (ALT I, II and III) and the selection criteria (mass and BLUP) considered. Under mass selection, the ΔG for ADG, MW, LW, NKW and FEC derived from RWs (ALT I) were relatively higher than the corresponding gains derived based on EVs (ALT II and III). These gains reduced as the proportion of sires increased from 0.02 to 0.04. The same trend was also noticed with the other breeding objective traits.

In ALT I, MW had the highest gains while SCC the lowest. The ΔG in this scenario portrays the priority trait preferences of the smallholder farmers and the consequences of application of RWs on genetic improvement in the village/community breeding program. The ΔG for traits in ALT II and III, were relatively similar both under P= 0.02 and 0.04 with a subsequent reduction in the latter. Although losses in responses were predicted, it means that gains are less affected by EVs estimated in the long-term (risk-rated EVs). However, this might be specific to the breeding system and circumstances considered in the current study. Comparison of this set of responses to those derived in ALT I showed a large variation in the traits DMY and FEC, a sign that suboptimal gains are likely to occur in some of the breeding objective traits on the basis of the method or trait weights used. The economic response was highest in ALT II, e.g., KES 66.416 and 60.514 whereas its equivalent aggregate response was lowest to modest. The R_H reduced from 0.5987 in ALT I to 0.4421 in ALT III while the ΔF were 0.09412 (ALT II), 0.09406 (ALT III) and 0.09402 (ALT I), with a P of 0.02. The rate of inbreeding reduced with an increase in P from 0.02 to 0.04.

Under BLUP, responses for breeding-objective traits in the selection schemes, and with P= 0.02 and 0.04 were relatively larger, but similar in pattern to those predicted under mass selection. When compared with the mass selection, ΔG for most traits, R_H, TER and ΔF increased under the BLUP criteria.

Table 4 shows the response for individual traits, R_H, TER and ΔF under AG selection schemes. Trait responses increased with increase in the number of groups cooperating from two to three. The aggregate and total economic responses were higher, and the ΔF more favourable with increase in the number of groups co-operating. For instance, the R_H increased from 0.728 to 0.743 trait units while ΔF reduced from 0.0611 to 0.0442 (ALT I and P = 0.02). Generally, better ΔG, R_H, TER and ΔF were predicted under BLUP selection and when P was set equivalent to 0.02.

Figure 1: Influence of the number of farmer groups co-operating on the aggregate response (RH) and rate of inbreeding (ΔF), under mass selection, P = 0.02 and alternative selection objectives (ALT I, II and III), in Within-group (WG) and Across-group (AG) selection schemes

The inbreeding levels in the present study were higher than the recommended acceptable levels, in the range of 0.01 to 0.005. Therefore, comparisons were made for aggregate responses under mass selection and BLUP, with the rate of inbreeding constrained to an acceptable level of 0.01 (Figure 1). Only results for mass selection and P = 0.02 are presented because the trend was similar for all the selection criteria and schemes. A minimum of 14 cooperating farmer groups would be required to obtain responses at the acceptable level of inbreeding (0.01) (Figure 1). Additionally, the aggregate responses under mass selection were comparable to BLUP selection at the same rates of inbreeding (0.01). For mass selection, however, a negligible efficiency loss (about 0.002%) in aggregate response was predicted. The maximum achievable R_H would be about 12%, 16% and 11%, respectively, in ALT I, II and III (Figure 1) more than when a WG selection scheme is implemented at P =0.02 under mass selection (Table 3).

Discussion

Many factors play an important role in the optimisation of breeding strategies; of foremost importance are the rate of response, rate of inbreeding and size of the nucleus (Gibson and Jeyaruban 1993; Falconer and Mackay 1996). The first two determine the technical success of the nucleus, while the latter is a principal determinant of cost. The aim of the current study was to assess the alternative selection objectives and schemes for optimisation of village/community breeding programs. Village/community breeding programs were considered for this study because of their relevance to the smallholder dairy/crossbred goat farmers (Sölkner et al 1998; Kahi et al 2000; van der Werf 2000). The key issue was to technically examine a) a single-tier structure with a mixed population of breeding and participating flocks and b) breeding population sizes required to produce acceptable levels of inbreeding in a village/community breeding program. The WG and AG selection schemes were defined and evaluated for optimal response levels, economic response and inbreeding rates. Three selection objectives were considered and defined based on relative weights derived from producers’ preferences and EVs without and with risk. This in essence was meant to demonstrate the effect of breeding objectives in the short and long-term (current and future circumstances) and in conditions where economic variables are scarce.

An important consideration in assessing WG and AG selection schemes is the maximisation of response to selection while restricting the rate of inbreeding. Maximising genetic gain while constraining the rate of inbreeding will change the layout of selection schemes compared to maximising gain alone (van Arendonk and Bijma 2003). In addition, inbreeding and genetic gain in the short-term have an unfavourable relationship. Maximising short-term response by selecting fewer parents reduces long-term response and involves enormous risk (Verrier et al 1993). To balance the short- and long-term response, a restriction on the rate of inbreeding is required (Quinton and Smith 1995). In this study, aggregate responses under mass selection were comparable to BLUP selection at the same rates of inbreeding (0.01). Generally, the results suggest that committing more groups of farmers to bucks in a sire exchange scheme increased the rates of gains for individual traits, total economic responses and aggregate responses and, subsequently, reduced the rate of inbreeding (Tables 3 and 4, and Figure 1). This agrees with the findings of Lewis and Simm (2000).

It is also noticeable that the WG selection scheme would result in a considerable genetic progress, but increase in responses also resulted in increased inbreeding rates. This means that the AG selection scheme under mass selection would be optimal and reasonable for implementation in the smallholder farms. However, it would be overambitious to set up an AG selection scheme with at least 14 cooperating farmer groups, owing to many constraints, e.g., organisational and institutional complexities, and shortage of financial and logistical resources.

Genetic improvement programs are constantly adding more traits in the breeding objective and their inclusion driven by what producers desire and sustainability (Olesen et al 2000; Tozer and Stokes 2002; Nielsen et al 2005 and 2006; Nielsen and Amer 2007). Methods have been designed to objectively assign economic weights for marketable and non-marketable traits or to information elicited from producers (Olesen et al 2000), for use in animal breeding selection indices where traditional approaches such as profit functions and bio-economic models are not very practical (Nielsen and Amer 2007). In the example in the current study, ALT I can be an alternative basis of evaluation and improvement of breeding objective traits for the crossbred goats because, generally, economic data can be poor and costs of measurement and recording can be high for smallholder farmers. This has important consequences towards the establishment of a sustainable and optimal village/community breeding program for crossbred goats because improvement of the producer’s priority traits is possible and with limited investment in trait measurement and recording. What remains as a biggest challenge, however, is the accuracy of the predicted responses for some of the important traits, e.g., DMY and FEC. Use of this selection scenario (ALT I) was with sacrifices in DMY, loosing up to 66% in efficiency in comparison to ALT II and III. In addition, selection response in the scheme shifted from dairy to meat traits, which was surprising for dairy goat improvement. Positive responses in FEC were also obtained. This could be undesirable if the objective is to minimise high disease challenges (Gicheha et al 2005 and 2007). Studies indicate that incorrect use of economic weights may result in an incorrect selection criterion and, consequently, sub-optimal direction of selection (Vandepitte and Hazel 1977; Kulak et al 2003). This implies that these results can only be used as a guide for practical purposes in the low-input dairy/crossbred goat systems.

A major justification for use of non-objective (ALT I) rather than objective (ALT II and III) methods in derivation of economic weights, and subsequent use of the values in selection indices was the insufficient knowledge on economic and biological parameters to model the most relevant aspects involved in a production system (Groen et al 1997). An advantage of using an objective method in the derivation of EVs is, however, the possibility of applying different prices, levels and sizes of a production system (Groen et al 1997). In this study, EVs used reflect stable current conditions while avoiding seasonal and cyclical irregularities (ALT II) and, takes into consideration that knowledge is imperfect and economic circumstances are dynamic in time (ALT III). A comparison of ALT II and III revealed that selection indices applying EVs considering future productions circumstances (incorporating risk) resulted in higher aggregate responses but not necessarily more gains in individual traits, which were equivalent to those in ALT II (Tables 3 and 4). These findings imply that there would be no reason not to consider long-term profits in genetic improvement of breeding objective traits when designing optimal village/community breeding programs.

This study has shown that design of village/ community breeding programs would involve pursuing different breeding objectives and schemes, and would be very much determined by ‘what is possible’ and ‘what is optimal’ (van der Werf 2000). The question would be how this program would be established and introduced, without severe organisational and institutional problems (Kosgey and Okeyo 2007). Organisational lessons can be learnt from the experiences of the few examples of village/community breeding programs in the low-input systems (Nimbkar 2000; van der Werf 2000; Kahi et al 2005; Kosgey et al 2006; Bett et al 2009a) to avoid repeating the same mistakes. It is also expected that the single-tier structure would evolve over time to a distinct two-tier structure made up of farmers who are better able to select and utilise the best breeding stock, “elite breeders”, and others who are not “participating flocks”. Elite breeders would be favoured by the market demand for breeding stock and, consequently, a nucleus structure will emerge (van der Werf 2000). Further studies are required on how this can be realised, including the dissemination of improved genetic material to crossbred goat keepers, especially for smallholder producers in a two-tier structure. Similarly, economic efficiency of such a system needs to be taken into consideration.

Conclusions

• A selection index considering risk-rated economic values in derivation of individual trait gains, economic responses, aggregate responses and rates of inbreeding would be appropriate and optimal for a village/community breeding program utilising crossbred goats under within-group and across-groups selection schemes. However, only in circumstances where input and output economic parameters are scarce or difficult to collect, information elicited from farmers can be used to predict relative weights for use in optimisation of these breeding programs. At the same levels of inbreeding, results predicted in mass selection were comparable to BLUP. Mass selection can, therefore, be used as a basis of evaluation and, consequently, reducing the costs of recording selection criteria (i.e., pedigree recording). This implies that across-group selection scheme under mass selection would be optimal and logical for implementation in the smallholder low-input goat production systems.

Acknowledgements

Thanks to Dr. Pieter Bijma for the program SelAction and the initial assistance on the use of the program. This paper was finalized when the corresponding author was a PostDoc at Swedish University of Agricultural Sciences (SLU) and also a Visiting Scientist at International Livestock Research Institute (ILRI).

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Received 5 November 2011; Accepted 5 December 2011; Published 4 January 2012

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