Chapter 3
Breeding Plans and Structures
In the previous chapter the breeding goal of the community was defined in relation to the
inputs and outputs of the production system and the characteristics of the animal population
to be improved. The breeding program will consider this animal population with its biological
characteristics together with husbandry, infrastructure and other constraints and opportunities
in order to maximize genetic gain and profit for the community.
The most straightforward design is to select in the whole population the best males and
females for replacement. The problem with such a design is that each member of the
community is involved in the selection process. Thus, each farmer will be occupied in tasks
such as performance recording, pedigree recording and rearing of male candidates. This may
not always be practical. Performance recording may be kept at a minimum or performed in
stages. Rearing of male candidates may be centralized at a test station or may be entrusted to
a member of the community. Whole population selection may also not be efficient in terms of
genetic progress because mating of selected animals would be basically at random.
An alternative design would be to have some farmers with “best” animals to fulfill the
specific function of breeding males for the whole population. Such designs with structured
populations are called “nucleus systems”. Nucleus farmers concentrate on maximizing
genetic gains while the remaining “base” farmers can concentrate on production. In this case,
best animals are mated in the nucleus, increasing the probability of having better gene
combinations in the progeny as compared to whole population mating.
The nucleus must be functional, that is: nucleus farmers not only have to make genetic
progress but also have to disseminate the appropriate males in quality and quantity to base
population farmers. Thus, the size of the nucleus, or proportion of total females of a given
population which should be in the nucleus, depends on the number of males needed and on a
desired selection pressure. A minimum effective population size is also required to avoid
inbreeding. For example if we desire an annual rate of inbreeding less than 0.5% in a nucleus
with average generation length of 3 years, effective population size must be greater than 33
(for example 9 males and 100 females).
Open nucleus systems
Rate of inbreeding can be reduced and genetic progress can be increased with so called “open
nucleus” systems. In such systems the nucleus is “open” to best animals detected in the base
population. Typically very good females born in the base replace worst females in the
nucleus. Optimum open nucleus breeding scheme structures in terms of proportion of females
in the nucleus and transfer rates between nucleus and base which maximize rate of genetic
gain can be found by trial and error using the formulae developed by James (1977) and
Mueller and James (1984b), or by using the EBV migration method (Shepherd and Kinghorn,
1992), using the principle that truncation points of expected breeding values of contributing
groups of individuals in nucleus and base should coincide (Hopkins and James, 1978).
Assuming that all animals are selected on the same criterion and generation interval is the
same in nucleus and base, 5-10% of the population should be in the nucleus and half of the
nucleus dam replacements should be transferred from the base to the nucleus. For example a
community with a total of 700 breeding females would need about 70 breeding females in the
nucleus. If 20 nucleus female replacements are needed each year, 10 should be selected from
nucleus progeny and the other 10 from base progeny. The effect of using surplus females
from the nucleus in the base is negligible and seldom base males can qualify for breeding in
the nucleus. Situations which may modify this general rule are: With shorter generation
interval in the nucleus genetic gain is higher, lag is larger and fewer base females qualify for
the nucleus, but genetic gain is more rapid (Hopkins, 1978). If selection accuracy is higher in
the nucleus then less base females qualify for the nucleus (Mueller, 1984). Proportion of
females in the nucleus and proportion of base females going to the nucleus can be smaller if
selection is more accurate in the nucleus or if reproduction rate is high or female to male ratio
is high.
Open nucleus systems require base farmers to do some selection work on their female
progeny. Usually females for the nucleus are supplied in exchange of males, but other
arrangements are possible. A very important feature of open nucleus systems is that
adaptation traits and other breeding objective preferences in the base population are secured
in the males produced since these are born from “best” base females.
We assumed that all animals of nucleus farmers breed males since all their females are
supposed to be “best”. This is not necessarily the case; a nucleus farmer may have inferior
females as well. In that case the nucleus farmer may identify his “best” females and mate
only these with “best” males, or he may mate all his females with “best” males but consider
for selection only the progeny of “best” females. There will be several nucleus farmers with
only some of their females qualifying as nucleus animals. Such a system requires controlled
mating at the nucleus farmer or early castration of male progeny from non-nucleus females.
Sometimes it is difficult for the individual farmer to raise male candidates till final selection.
If this is the case a useful alternative could be to gather male candidates shortly after weaning
at a “performance testing station”. Such a station may belong to the community itself or may
be facilitated by an external organization.
We also assumed that nucleus animals are run by several farmers so that the nucleus is
“dispersed” amongst them. There are also programs with a single nucleus farmer producing
males for a group of farmers. Such systems are also called “group breeding systems”. The
principles of open nucleus systems apply but considering that 5-10% of the female population
should be in the nucleus, group breeding schemes are typically appropriate for farmers with
more than say 100 breeding females each or for a situation with a nucleus farmer running
himself a large flock.
In the case of smallholder communities large individual flocks are unusual but there are
examples with community single nucleus flocks or cooperative nucleus flocks (Mueller et al.,
2002). Sometimes a single “central” nucleus is run by an external organization such as a
university or a governmental body. In this case the design essentials regarding nucleus size
and gene flow still apply but the control of the community on its breeding program may be
low. Nevertheless there are also examples of community breeding systems which started with
a “centralized” nucleus and developed into a dispersed nucleus system. Such dispersed
nucleuses in turn may develop into reference sire schemes and eventually into population
wide evaluation schemes (Abad et al., 2002).
Clearly there are many alternative breeding structures and tools for its optimization. The tools
are useful for a strategic optimization of a breeding program. In practice, however, many
variables are fixed and rarely a program can start with an optimum structure in terms of layer
size and gene flows. Thus, practical situations need tactical optimization, which means
finding the best solution at each step of the program having a target structure in mind.
Tools for assessing alternative breeding plans
Choosing the best breeding scheme among several alternatives requires yardsticks to measure
the quality of breeding schemes. Developing such yardsticks depends on the availability of
well-defined breeding goals. Quality of a breeding program can be evaluated by selection
response for the breeding goal traits, maintenance of genetic diversity as measured by the rate
of inbreeding, and costs incurred to implement the breeding program. There are two methods
available to design and evaluate breeding programs: stochastic and deterministic.
Stochastic simulation is the easiest way. Breeding program is simulated in detail on a
computer. Its advantage is that one can mimic the true breeding program in detail with more
precision because the individual animal is simulated. Its disadvantages relate to
time/computer power requirement and the user does not gain much insight compared to
deterministic approach. Simulation of a large number of replicates of a large breeding scheme
may take from several hours to days making the approach less suited as an operational tool to
quickly evaluate alternative schemes. Since stochastic simulation does not explicitly model
mechanisms like accuracy, generation interval, etc., the user may not be able to appreciate the
relationship between the determinants. Hence it is difficult to extend results to other breeding
schemes that have not been simulated.
Deterministic method does not mimic the breeding program on the individual animal level
but use deterministic equations and population parameters to predict gain and inbreeding.
Hence it requires more insight into quantitative population genetics than stochastic
simulation. Advantages of deterministic methods are short computation time (many
alternatives can be computed within limited time) and it gives a lot of insight into gain and
inbreeding in breeding programs because the mechanisms are modeled explicitly. However, it
may be difficult to derive accurate deterministic methods (hence less accurate).
To date, ZPLAN is the only comprehensive software package available that offers a detailed
deterministic evaluation of complex breeding programs (Täubert et al., 2010). It was
developed in 1980s at the University of Hohenheim, Germany, and has been used widely
since then. ZPLAN is written in Fortran95 and allows flexible modification of existing
subroutines to model desired breeding scenarios as realistically as possible under both
conventional and genomic breeding programs (König et al., 2009). Using the gene flow
method and selection index procedures, the program enables to simulate different breeding
plans in any livestock species and it can be applied for plans with several sub-populations, for
populations used in a crossbreeding scheme and considers several tiers in the scheme such as
nucleus, multiplier, and production levels (Willam et al., 2008). Important outcomes include
annual monetary genetic gain for the aggregate genotype, annual genetic gain for each single
trait, discounted return and discounted profit for a given investment period (Willam et al.,
2008). For the selection index part, information available for the evaluation of an individual
candidate have to be defined by the number and type of relatives contributing to the index of
an animal as well as records on individual’s own performance (Willam et al., 2002; Willam et
al., 2008). Phenotypic constants (standard deviation for each trait, phenotypic correlation
between each pair of traits and phenotypic correlations between the traits of relatives) and
genetic constants (heritable fraction of the variance in each trait and genetic correlation
between each pair of traits) are also needed to construct indexes. Modeling of breeding
programs with ZPLAN has two main steps: basic run and alternative runs (alternative
breeding schemes). Alternative runs are calculated by varying certain parameters of the basic
run (e.g. population size, proportion of breeding unit, etc.) to predict the outcome of each
scheme with one or more output parameters (e.g. genetic gain per year, return, costs, profit,
rate of inbreeding, etc.) and choose the best to recommend for implementation in practice.
The platform used by ZPLAN is no longer up-to-date and rather complicated to use. To
address this issue, Täubert et al. (2010) developed a new web-based version of the software
(ZPLAN+) that can be used to evaluate complex breeding programs. ZPLAN+ has a
comprehensive range of functions and considers all latest developments with special
emphasis on genomic information. Programming is based on modern platforms (objectoriented programming, platform independent software, database driven web-applications
which enable the user to work from everywhere). It has user-friendly interfaces with online
documentation and support. New options implemented in ZPLAN+ include: unlimited
number of information sources and combination with breeding goals, implementation of
genomic information, consideration of reliabilities of partial breeding indexes (as used in
modern BLUP-estimation systems), allocation of an optimal use of resources (e.g. in multiple
stage selection programs), and optimization of side effects and maximization of genetic gain
under constraint (e.g. avoiding too much inbreeding).
SelAction (Rutten et al 2002) predicts response to selection and rates of inbreeding for
practical livestock improvement programs and for breeding programs of companion animals
using deterministic simulation. It requires little computing time and can be used as an
interactive optimization tool. The package enables to evaluate breeding programs both with
discrete or overlapping generations and selection on multiple traits (maximum of 20 traits).
SelAction predicts response to selection on BLUP breeding values using animal model. It
accounts for reduction of genetic variance due to selection (Bulmer effect) and reduction of
selection intensity. An index of information sources is used to predict the genetic merit of
individuals for the breeding goal (the sum of maximum of 20 traits weighted by their
respective economic values). Index traits and breeding goal traits are allowed to be different
traits, which enables prediction of correlated selection response. Variance components of the
traits involved are required as input. Responses are expressed in 3 ways: in trait units, in
economic units, and as a percentage of the total selection response. SelAction is programmed
in Borland Delphi 5.0, runs on Microsoft Windows 95/98/NT and is available free of charge
for non-commercial use.
SixS (Kremer et al., 2006) is a stochastic approach that simulates selection of animal
populations over multiple generations. SixS (6S) version 2 is mainly used for teaching
purposes but can be employed in the optimization of small breeding populations. It uses
windows computing environment and is freely available by emailing to the authors
(Valentine.Kremer@bbsrc.ac.uk).
ADAM (Pederson et al., 2008) is a stochastic simulation method and allows a plethora of
breeding schemes to be evaluated. It uses new methods (MA/GA-selection, selective
genotyping, MOET, and sexed semen) and can be easily expanded to include new methods
and strategies. The program allows the user to define parameters to specify base population,
genetic model, population structure, selection and mating decisions (selection on single or
multiple traits by random, truncation, or optimum contribution selection). Truncation and
optimum selection are based on: phenotype, BLUP (based on phenotype and pedigree
information), MA-BLUP, or GA-BLUP. It provides output options like true and estimated
breeding value (EBV) for each time step, overall and trait wise genetic gain, generation
interval, accuracy of prediction and rate of inbreeding. Operating environment is Fortran95.
EVA (Berg et al., 2006) is aimed at describing inbreeding in a population and to predict
genetic contributions to maximize response to selection given a penalty on rate of inbreeding.
It is freely available upon request from the authors.
GENECONT (Meuwissen, 2002) was primarily developed to control inbreeding in selection.
It advises how many progeny an animal should obtain in a breeding/conservation scheme and
is based on optimum contribution selection maximizing genetic gain at the desired rate of
inbreeding. It considers overlapping generation structures and minimizes rate of inbreeding
(for conservation schemes) and most suited for optimizing nucleus schemes. The program
requires 3 files: GENECONT.INP, data, and pedigree file. Computing environment:
executable version is available for Windows but can be adapted to other PC environments by
compiling the original Fortran90 code. It is availability at a cost of €2273.
Experiences with breeding structures
Improvements to a conventional breeding structure.
Example with Merino sheep in Argentina. Argentina’s Merino industry is based on 7 million
sheep and about 6 thousand farming units, some of which have large flocks but most are
small family flocks which sell raw wool and surplus animals. Recently an analysis of the
Argentine Merino herd book indicated a total number of pedigree ewes of about 6500.
Assuming effective reproduction rate of 0.25 and selection pressure of 0.5 in the studs and a
multiplier layer in which mating ratio is about 0.04 and replacement ratio about 1/4, it can be
calculated that stud genes reach only about 1 million ewes out of a total of 4 million. This
result helped to mobilize the Breeders Society to search for ways which increase the
availability of improved rams. This was done through a program called “Pure Registered
Merinos” (MPR) which allows for the establishment of open nucleuses with visually selected
ewes mated to pedigree rams or to “outstanding” nucleus born rams. Young candidate rams
are performance tested and those visually accepted, by official inspectors, and above average
selection index get an official MPR tag. Outstanding rams are those with selection index two
standard deviations above average. The program is growing quickly in number of farms,
inspected animals and price premiums paid for MPR rams (AACM, 2009). The system can be
viewed as an improved conventional breeding structure.
Establishment of a village based open nucleus structure.
Example with Angora goats in Argentina. Extensive surveys in the NE of the Patagonian
desert in Argentina showed that about 3000 farmers run a total of about halve a million
Angora goats in low input systems. Some 30 years ago there was no genetic structure in place
and fleece weights were low (1.2 kg) and fiber contamination was high (medulated fiber 710%). A nucleus at an institutional research farm was established by screening good looking
animals amongst several dozens of flocks. Selection in the nucleus was on fleece weight,
fiber sample and visual quality. Villages were asked to choose flocks as multipliers in order
to receive bucks from the nucleus and produce bucks for the rest of the community. The
system developed into 54 multipliers and a large predicted impact (Mueller, 1995). Over time
many multipliers associated and founded the Angora Goat Growers Association. The
structure started to lose strength and efficacy largely because of low Mohair prices and lack
of price differentials for better fiber. In 1998 the “Argentine Mohair Program” (SAGPyA,
2000) was established with the aim of increasing farmer livelihoods by: further improving
fiber quality, implementing a new marketing strategy and adding value. By 2008 there were
13 organizations with 835 members and 8 institutions involved in the program. Together with
the original central nucleus a disperse nucleus was organized in 9 flocks and 71 multipliers,
with 522 females in the nucleus and 1790 females in multiplier flocks (Abad et al., 2002). At
present in nucleus flocks fleece weights range 2.5 to 4 kg, fiber diameter is less than 29 mic
in the first 3 shearings and medulation is below 1% (Lanari et al., 2009). On farm classing,
objective testing and collective offer improved considerably negotiation capacity of farmers.
Program Mohair sells on average at 40-100% higher price than regular Mohair. The program
attracted national and international support (Debenedetti et al., 2010) and contributed
substantially to an increased national Mohair production. The system started with a village
based open nucleus and is now an efficient pyramidal structure.
Three tier cooperative open nucleus structure.
Example with sheep in Peru. A survey conducted in the highlands of the Sierra Central
identified 3 types of sheep production systems: (1) individual family flocks, (2) communal
flocks belonging to villages and (3) multi-communal flocks managed by cooperatives
involving several villages in a region (Flores et al., 2007). Systems (1) and (2) do not have a
planned breeding plan. Only system (3) includes a breeding structure consisting of three
weakly linked tiers. The survey identified two major requests of farmers related to breeding:
the need of suitable rams at the individual flock level and the need for training in breeding
techniques. After extensive discussions, in 1995 village leaders agreed on the establishment
of a breeding structure and providing land and labor for a central nucleus and the University
of La Molina supplying technical support. The nucleus site serves also as demonstration farm
and harbors the “Research and Peasant Training Center” (CICCA) were farmer courses are
held on selection, reproduction, health, wool classing and artificial insemination. The nucleus
was created by mating imported and locally produced top rams with 50 of the “best” females
of each of 9 communal and multi-communal flocks. Twenty-five of these 50 ewes returned
pregnant to the donors and the other 25 started a central nucleus to provide improved rams to
(2) and (3) which in turn agreed in providing rams to (1) (Mueller et al., 2002). Incidentally
the progeny of local rams proved to be better suited to the local market conditions than the
progeny of imported rams. The breeding program grew in members and is now run by the
“Cerro de Pasco Corriedale Breeders Society” (Gutiérrez, 2010, pers. com.). Farmer
organization and farmer training is the backbone of this successful village based sheep
breeding program.
Village based open nucleus structure
Example with dairy goats in Mexico. Hans may summarize this example.
Examples with llamas and alpacas in Bolivia and Peru. The highland of Eastern Bolivia
(4,000 masl) is a llama breeding area. Llamas are vital in providing meat but also fiber, hides,
manure and transport. To breed better animals, farmers in the surroundings of the town of
Turco, organized a village breeding system (Rodríguez and Quispe, 2007) with similarities to
that described by Wurzinger et al. (2008). Llama males are selected by the group of farmers
amongst young males born in the flocks of the different households and then taken to a
“Centro de Machaje” were they are run together isolated from females and shepherded by
members of the village. Males are taken to the individual households in turns of about 15
days each and mated to local females. This is an extremely simple system with three
interesting points. First, breeding objective is decided collectively as it is the village members
who decide which young males qualify for the “Centro de Machaje” and which males should
stay there as they grow. Second, selection is based on visual appraisal only. This may be
somewhat uncomfortable for a geneticist but it must be recognized that the correlation of
visual ranking of animals for a particular breeding objective is larger than zero. In fact,
experiences with sheep classers in Australia show that the correlation of visual ranking on
clean fleece weight or fiber diameter with measured ranking ranges from 0.30-0.71 (Napier
and Jones, 1979). This means that genetic progress may be only halve of what would be
achieved with measurements but may be preferable to fleece weighing and sampling, in
particular in a country without a fiber testing facility. Third, the “Centro de Machaje” is an
effective system to ensure planned mating and avoid inbreeding. In Huancavelica, Peru a
similar system is operating with alpacas. In this case male alpaca candidates are separately
performance tested and then distributed amongst smallholders (Quispe et al., 2009). In
Macusani, Peru the local village cooperative established two alpaca nucleuses where sires are
visually stratified in 4 classes. Individual farmers take their females alpacas for mating to the
nucleus sites. These females are also classed so that mating will be with males of the next
superior class (Choquehuanca, 2007, pers. com.). Local knowledge and creativity made these
village-based open nucleus systems possible.
Dispersed open nucleus structure
Example with sheep in Ethiopia. Tadele may summarize this example.
Within flock open nucleus structure
Example with cashmere goats in Iran. During spring and summer nomads in the region close
to the city of Baft, Province of Kerman, SE of Iran, run most of the 850,000 Raeini cashmere
producing goats of the region. In autumn the nomads migrate with their goats south to winter
rangelands close to the Persian Gulf. A breeding program is being developed to improve
cashmere quantity and quality. The program started with a baseline study conducted amongst
30 randomly chosen nomad families. Fleece weights were taken and 720 cashmere samples
were collected for analyses. Eight flocks were selected to start the breeding program. Three
options were discussed: (1) Select best looking kids amongst all born, performance test them
and select best as replacement of old bucks; (2) Select best looking females, identify their
male kids, performance test them and select best as replacement of old bucks; (3) Establish a
nucleus within the flock with best looking females, mate to best males and select male
replacements amongst its progeny on performance test. The third option was accepted. Each
nomad farmer selected his best 40 females and best 2 males as nucleus animals. The main
issue was control of mating and identification of progeny. Visual identification of nucleus
progeny is relatively easy if nucleus dams are somehow identified and if the task is done
early in live when kids remain close to their dams. However controlled mating is much more
difficult to achieve. Again three option were discussed: (a) Mating of selected animals in a
fenced area; (b) Mating of selected animals by separate grazing; (c) Artificial insemination of
selected females. Clearly, each option has advantages and disadvantages. Finally 3 farmers
preferred separate grazing and 5 preferred to build a small fenced corral (Ansari-Renani,
2010, pers. com.). Hand mating or subdivision of these corrals allows progeny testing of the
two bucks, so that each year the worst performing buck can be replaced by a top performing
young candidate. Next best young bucks are used in the base flock. Progeny testing is a very
effective way for accurate buck replacement, particularly in this situation where direct
measurement of cashmere quality is difficult and repeatability (and heritability) of quality
traits is probably low. This within flock open nucleus structure with progeny testing just
starts and its effectiveness has to be seen. Participation of farmers and local extension officers
in the design of the program has been crucial.
Discussion
The analysis of ongoing breeding plans as well as the design of new plans involves
consideration of critical aspects such as breeding objective, selection criteria, mating system
and genetic dissemination. There is a comprehensive set of quantitative genetic tools
available to the breeder to find an optimum arrangement of all aspects to fit his particular
case. The tools are useful for a strategic optimization of a breeding program. In practice,
however, many variables are fixed and rarely a program can start with an optimum structure
in terms of layer size and gene flows. Thus, practical situations need tactical optimization,
which means finding the best solution at each step of the program having a target structure in
mind.
The size and genetic quality of a nucleus may start different from optimum and this can have
a substantial effect on genetic gains in the early generations of the program. If a nucleus is
started from scratch it can be shown that initial screening effectiveness can have a large effect
on the base population for many years before reaching a steady state rate of gain. To a lesser
extent initial age structures and gene flows also affect the early years, and yet those years are
crucial for farmers and investors alike who will be eager to see progress. Visible results in
short time are difficult to achieve. Environmental effects will usually have a very large effect
compared to genetic changes. Farmer commitment and leadership are particularly important
at this stage.
It is often useful to think about breeding structures in terms of open nucleus systems in its
various versions and combinations. As we have seen from the above examples and elsewhere
(Kiwuwa, 1992) open nucleus breeding structures proved to be suitable in many situations.
Single open nucleus structures easily evolve towards efficient pyramidal structures. When
performance recording is feasible in base flocks and full or partial pedigree can be collected
then the nucleus serves as a genetic link between flocks and the system can benefit from
BLUP evaluations. In that case selection is straightforward and the search for optimum gene
flow is largely superfluous. Dispersed open nucleus structures can be particularly relevant to
low input systems, such as the within village and between village systems described by
Gizaw et al. (2009). These systems easily evolve into pyramidal structures with part of the
farmers producing males for themselves and for others.
In any case the key for a successful structure is the involvement of the farmers and villages in
the design and operation of the program with its breeding structure. This will need a
minimum of farmer organization and technical support. Informal breeding programs based on
local knowledge are often effective but can also benefit from knowledge of other programs
and from technical support. Advisors with the necessary theoretical background and
experience are important to avoid repeating mistakes and to multiply positive experiences.
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Table 1. Summary of important features of the programs/packages
Program/Package
Feature
ZPLAN
ZPLAN+ GENCONT SelAction
ADAM
EVA
Approach
Deterministic Deterministic
???
Deterministic Stochastic Stochastic
Species
All livestock All livestock
???
All livestock
All
???
applied to
species
species
species
livestock
species???
Number of Max. 50??
Unlimited
MultiMax. 20
???
???
traits
in
trait???
breeding
objectives
and selection
criteria
Definition of
Yes
Yes
???
Yes
Yes
???
population
structure
Allowing for
Yes
Yes
???
Yes
???
???
genetic and
economic
gain
Level
of
No
Yes
Yes
Yes
Yes
Yes
inbreeding
Cost-benefit
Yes
Yes
???
???
???
???
analysis
Consideration
Yes
Yes
Yes
Yes
Yes
???
of
overlapping
generations
Allowance
Yes
Yes
???
???
Yes
???
for
new
technologies
(e.g.
MA/GAselection,
genomic
selection)
Operating
Written in
Web-based Windows
Windows Written in Written in
environment
Fortran95
Fortran95 Fortran95
(user
friendliness)
Availability
Free
???
Commercial Free for non???
Freely
(€2273)
commercial
available
purposes
upon
request
SixS
Stochastic
???
???
Yes
???
Yes
???
???
???
Windows
Freely
available
upon
request