In spite of the crucial role livestock plays in the economies of
many sub-Saharan African countries, livestock productivity remains
relatively low in the region. Several studies have shown that factors
ranging from technical, institutional, and infrastructural constraints
related to feeding, animal health, and genotype are the main causes of
the low productivity (Tano et al. 2003). Breed improvement programs
serve as natural entry points for productivity increases. However, as
argued by Tano et al. (2003), the tendency for genetic improvement
programs to concentrate on one aspect, such as meat or milk production,
in isolation from broader livelihood system needs often results in the
substitution of exotic cattle for indigenous breeds. Although indigenous
cattle are often less productive than exotic breeds, when traits such as
milk and beef production are considered in isolation, they may be better
suited to the conditions of the local environment. This has often led to
low adoption rates of exotic or crossbred cows (Abdulai and Huffman
2005). De Haan (1995) points out that livestock technologies have been
the source of puzzling outcomes more frequently than crop technologies,
partly because new technologies that would improve productivity are
simply not adopted.
The low productivity in Africa's livestock has also been
partly attributed to the multiple functions that cattle perform in the
livelihood system (Moll 2005). It is estimated that approximately 80% of
the value of livestock in low-input developing country systems can be
attributed to nonincome, sociocultural functions, while only 20% is
attributable to physical products such as meat, milk, and wool. In
contrast, over 90% of the value of livestock in high-input developed
country production systems is attributable to direct production outputs
(Gibson and Pullin 2005). Some of the important nonincome and
sociocultural functions of cattle in developing countries are embedded
in traits that are not traded in the market, therefore lacking price or
market values. Hence, the utilization of profit functions for derivation
of economic values for cattle traits in such systems would result in
exclusion of such traits from the breeding objective, which may result
in genotypes not capable of fulfilling the multiple objectives of the
cattle enterprise. This calls for the employment of a valuation method
that can also permit calculation of economic values of traits without
market values to be included in the breeding objective.
Few economic studies have attempted to investigate phenotypic trait
preferences for genetic resources using stated preference approaches.
Some authors have used conjoint analysis to assess relative importance
of traits (Sy et al. 1997; Tano et al. 2003). These studies utilizing
conjoint analysis employ ordered probit or multinomial logit models to
model preference behavior. A limitation of these models is that they do
not explicitly account for heterogeneity of preferences among producers,
rendering them less useful for analysis aimed at providing policy
recommendations for different environments and production systems.
Recent studies have increasingly focused on the monetary value of
traits by employing choice experiments and including a monetary cost or
benefit as one of the traits. For example, Scarpa et al. (2003) employ a
mixed logit model, a recent advancement in discrete choice analysis, in
their empirical estimation of choice data to value the phenotypic traits
expressed in indigenous breeds of livestock in a pastoral system in
Kenya. Zohrabian et al. (2003) use a maximum entropy search-theoretic
framework to estimate the value of precommercial germplasm contained in
the U.S. national plant germplasm system. Kontoleon (2003) utilizes a
latent class model to account for preference heterogeneity in GM foods.
Kline and Wichelns (1998) note the significance of accounting for
preference heterogeneity, since preferences often vary among individual
decision makers according to their environment, socioeconomic
characteristics, and tastes.
The objective of this study is to derive the economic values for
cattle traits in Kenya and Ethiopia using a choice modeling approach.
Specifically, a mixed logit model is employed to investigate the
existence of preference heterogeneity, while a latent class model is
used to examine the sources of heterogeneity across segments of cattle
keepers. To the best of our knowledge, no previous attempt has been made
to investigate the existence of endogenous preference segmentation for
cattle traits among cattle keepers in sub-Saharan Africa. Given that
trypanosomosis disease is a major challenge to livestock production in
the study areas, the study was partly designed to provide a better
understanding of farmers' preferences for cattle and to enable a
reasonable assessment of programs aiming at encouraging the adoption and
use of trypanotolerant breeds.
Choice Modeling
The conceptual framework for choice experiments arises from the
consumer theory developed by Lancaster (1966), which postulates that
preferences for goods are a function of the traits or characteristics
possessed by the good rather than the good per se. An important
implication of this theory is that overall utility of a good can be
decomposed into separate utilities for its constituent characteristics
or traits. In terms of the utility function, this translates into using
the characteristics of goods as the arguments of the function. Hence, a
good can be described by the characteristics that generate utility or
disutility to individuals. For cattle breeding, this permits the
analysis of farmers' preferences in terms of the utility they
perceive to result from various cattle traits.
Choice experiments are based on the assumption that an individual n
receives utility, U, from choosing an alternative A equal to [U.sub.nA]
= U([X.sub.nA]) from a finite set j of alternatives in choice set k, if
and only if, this alternative generates at least as much utility as any
other alternative, with [X.sub.nA] denoting a vector of the attributes
of A. Utility is represented as two components, where one portion is
deterministic and depends on the attributes of the alternative, and the
remainder is stochastic. This can be specified as
(1) [U.sub.nA] = [V.sub.nA] + [[epsilon].sub.nA]
where [V.sub.nA] = g([X.sub.nA]) is the deterministic component and
[[epsilon].sub.A] is a random component of the utility function. The
probability of individual n choosing alternative A can then be specified
as
(2) P(A) = Prob{[V.sub.nA] + [[epsilon].sub.nA] [greater than or
equal to] [V.sub.nj] + [E.sub.nj]; A [not equal to] j, [for all] j
[member of] k}.
Choice experiments and hedonic price analysis are alternative
empirical applications to the Lancaster consumer theory. The strength of
both hedonic pricing and choice experiment techniques is the ability to
decompose revealed preference data, that is, prices of goods in the case
of hedonics and choice of goods' profiles by individuals in the
case of choice experiments, into marginal values (Dalton 2004). A number
of recent studies, such as Barrett et al. (2003) and Green et al.
(2006), have employed hedonic pricing to value livestock traits in
eastern Africa. The use of hedonics is nevertheless limited to the
valuation of existing traits and not prospective traits that may be of
interest to breeders. Choice experiments overcome this limitation since
preferences are measured directly, and then related to utility, making
it possible to estimate economic values of prospective traits. Choice
experiment technique is employed in the present study.
Estimation Techniques and Econometric Models
Discrete choice models are normally used to model the choices made
by the decision makers from the choice experiments. Mixed logit and
latent class models have recently been developed to relax the limiting
assumptions associated with conventional logit and probit models.
McFadden and Train (2000) and Train (2003) describe mixed logit as a
highly flexible model that can approximate any random utility model. It
relaxes the limitations of standard logit by allowing the taste
parameters to vary randomly according to a parametric distribution. In
addition, it allows for unrestricted substitution patterns and
correlation in unobserved factors over time (Train 2003; Hensher and
Greene 2002). (1) We refer the interested reader to the above literature
for a more in-depth description.
The mixed logit model applies the usual framework of random utility
models outlined in the previous section. As indicated earlier, a sampled
individual n faces a choice of selecting the preferred alternative
amongst a set of j cattle profiles, representing different traits and
trait levels in each of the t choice situations. In our case, the number
of choice situations is constant per respondent and we assume a linear
utility function. An individual, n, is assumed to consider the full set
of offered cattle profile alternatives in choice situation t and to
choose the alternative with the highest utility. The utility associated
with each set of j alternatives as evaluated by each individual n in
choice situation t is represented in a discrete choice model by a
utility expression of the general form,
(3) [U.sub.njt] = [[beta].sub.n][x.sub.njt] + [[epsilon].sub.njt],
where [x.sub.njt] is a vector of observed variables that includes
the cattle traits and socioeconomic characteristics of cattle owners.
Coefficient vector [beta] is unobserved for each n and varies in the
population with density f([[beta].sub.n] | [theta]), where [theta] is a
vector of parameters of a continuous population distribution,
[[epsilon].sub.njt] is an unobserved random term that is assumed to be
identically and independently distributed. The focus in mixed logit
shifts from finding estimates of [[beta].sub.n] to finding the estimates
of [theta], the population parameters (e.g., location and spread
parameters), which determine the behavior of [[beta].sub.n]. Conditional
on [[beta].sub.n] the probability that person n chooses alternative i,
in choice situation t, is the conditional logit specification
(4) [ILLUSTRATION OMITTED]
Given that [[beta].sub.n] is unknown to the analyst, the
unconditional probability is usually employed. The unconditional
probability is the
integral of the conditional probability over all possible values of
[beta], which depends on the distribution of [beta]. This integral takes
the form
(5) [P.sub.nit]([theta]) = [integral]
[L.sub.nit]([[beta].sub.n])f([[beta].sub.n] | [theta])d[[beta].sub.n].
Although mixed logit accounts for preference heterogeneity by
allowing taste parameters to vary randomly over individuals, it is not
well suited to explaining the sources of heterogeneity (Boxall and
Adamowicz 2002). These sources often relate to the socioeconomic
characteristics and taste of the decision maker. Though it is possible
to account for the socioeconomic characteristics of the decision maker
by interacting key individual characteristics with the traits, this
requires a priori selection of key limited individual-specific
variables.
Latent class models are more suited in explaining the sources of
heterogeneity, since individuals are intrinsically sorted into a number
of latent classes. Each is characterized by homogenous preferences
though heterogeneous across classes (Boxall and Adamowicz 2002).
However, its limitation is the assumption of local independence, where
the choice variables are assumed to be independent of each other,
conditional on latent class membership. Assignment of individuals into
classes is probabilistic based on their sociodemographic
characteristics. In this article we use a mixed logit model to identify
the existence of preference heterogeneity and latent class modeling to
estimate segment or class-specific parameters.
In the latent class logit, the mixing distribution f([[beta].sub.n]
| [theta]), is discrete, with [[beta].sub.n] taking a finite set of
distinct values (Train 2003). In this case, it is assumed that
individuals are intrinsically sorted into a number of classes based on
their tastes. Members of each class have similar tastes. However, the
classes are latent, not observable by the analyst. Within the class, the
individual choices from one choice situation to the next are assumed to
be independent and choice probabilities are assumed to be generated by
the logit model (Greene 2002). For instance, the probability that
individual n chooses alternative i in a given number of choice situation
t, given that he belongs to latent class c is
(6) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [x.sub.nit] is a vector of observable traits associated with
alternative i, and [[beta].sub.c] is a class-specific parameter vector;
t denotes the number of choice situations for person n, while
[[beta].sub.c] is used to capture heterogeneity in preferences across
classes. Since the classes are not observable, class probabilities are
specified by the multinomial logit form
(7) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [z.sub.t] is a set of observable characteristics that enter
the model for class membership. The cth parameter vector is normalized
to zero to ensure identification of the model. This model does not
impose the independence of irrelevant alternatives on the observed
probabilities. For a given individual n, the model's estimate for
the probability of a specific choice is the expected value, over
classes, of the class-specific probabilities. In our case, each person
makes repeated choices for either eleven bull profiles or twelve cow
profiles. The repeated choices permit an examination of how the levels
of various traits influence individual utility and a comparison with a
priori expectations.
Survey Design and Data Description
The choice experiment household surveys were conducted in Narok and
Suba districts in Kenya and the Ghibe Valley in Ethiopia, representing
different livestock production systems in trypanosomosis prevalent
areas. The three areas were selected after spatial mappings of tsetse
fly distribution and cattle densities in Kenya and Ethiopia were done to
target areas with trypanosomosis challenge. A purposive random sampling
procedure to identify cattle-keeping households was employed to select
172 households in three divisions of Narok district, 132 households in
two divisions of Suba district, and 204 households in the lower and
upper Ghibe Valley. Sampling was done along line transects within the
lowest administrative unit in the two countries. The sample size was
calculated as a proportion of the total number of households in the
administrative unit. The survey was administered between September and
December 2004 in Kenya and April to May 2005 in Ethiopia by locally
trained enumerators.
In order to identify the relevant cattle traits to be included in
the choice experiment, informal focus group meetings were held with
cattle keepers in the study sites. Cattle keepers were asked to identify
the cattle traits that they considered important in meeting their
objectives, while taking into consideration their prevailing local and
environmental conditions. Pair-wise ranking technique was then employed
to select the relatively highly preferred traits. A total of seven
preferred traits for cows and six for bulls, each with two to three
levels were identified for inclusion in the design of the choice
experiment. An additional monetary trait, purchase price, was included
to capture the marginal willingness to pay for the traits. The different
purchase price levels considered were based on the prevailing market
prices of bulls and cows. Table 1 presents the traits included in the
choice experiment and their levels.
The identified traits and their levels were then combined according
to an experimental design to create choice sets. A full factorial
design, which includes all possible combinations of the traits, would
yield 864 ([2.sup.5] x [3.sup.3]) possible choice sets for cows and 432
([2.sup.4] x [3.sup.3]) for bulls. Since it is not practically feasible
to work with such a large number of choice sets, a randomized, partially
orthogonal main effects only combinations was created using Choice-Based
Conjoint (CBC) software, which resulted in twelve generic choice sets
for cows and eleven for bulls. (2) These were then used to construct
cards with pictorial profiles describing the differences in traits and
the levels to demonstrate each choice set to survey respondents. The
choice experiment was administered as part of a household-level
questionnaire survey using in-person interviews in respondents'
homes. The rest of the questionnaire covered socioeconomic aspects such
as household and farm characteristics as well as market and resource
access.
The administration of the choice experiment was conducted in the
following manner: respondents were first introduced to the type of
choice task and asked to indicate their preferences between cows and
bulls for purchases. The respondents were then presented with choice
tasks for either bulls or cows and shown two pictorial profiles based on
each choice set. They were then asked to choose the most preferable
animal profile to purchase for rearing. Each of them was presented with
either twelve choice sets in the case of cows or eleven choice sets in
the case of bulls. In each case, a "don't buy" option was
also included for respondents who preferred neither of the two profiles
presented. A total of 253 complete choice experiment interviews were
carried out for bulls and another 253 for cows, yielding 2,783 completed
choice sets for bulls and 3,036 for cows. Twenty-three choices (two
interviews) were discarded due to incomplete information.
Table 2 presents a description of the choice experiment variables
used in estimating the mixed logit and latent class models. To avoid
confoundment with the grand mean, we employed effects coding for the
choice experiment variables to measure nonlinear effects in the trait
levels (Hensher, Rose, and Greene 2005). (3) Effects coding uses values
for codes, which when summed over any given trait column, equal zero.
From an a priori perspective, we expect that the trait levels of
trypanotolerance, high fertility in bulls, high reproduction potential
in cows, and high milk yield should increase an individual's
utility, as should an increase in live weight. A high reproductive
performance and high fertility are expected to have a positive impact on
herd productivity and herd size, while trypanotolerance would increase
the competitiveness of the cattle enterprise by reducing the costs
associated with the treatment of trypanosomosis disease.
Similarly, low watering requirement for the animals should increase
utility, since the watering points are located far from the homesteads
at an average distance of 3 km. The coefficient values for coat color
and traction potential may plausibly take either negative or positive
sign depending on an individual's preference. Coat color is an
important stimulus in attracting tsetse flies to a target and also has a
role in the landing response of the flies. The strongest landing
responses have been found to be on black surfaces relative to lighter
ones. For this reason, cattle keepers in the Ghibe Valley prefer
light-coat-colored cattle in order to minimize the likelihood of
trypanosomosis infection through tsetse fly bites. In the Narok
district, however, there is preference for blackcoat-colored bulls with
white spotted dewlap, features that are important in the selection of
bulls for use in ceremonial functions. Finally, the trait coefficients
associated with monetary expenditure, that is, purchase price of the
animal and the need for purchased feed supplements is expected to have a
negative sign due to the positive marginal utility for income generally
exhibited by most individuals.
Information on the socioeconomic characteristics of the sample
households used in the econometric modeling is presented in table 3.
Fifty percent of the households use tsetse fly control methods, which
include spraying, applying pour-on insecticides, and clearing bush as a
way to control the disease. Generally, illiteracy levels of household
heads are high in the sample population. The average number of years of
schooling for a household head is 4.9 years. In terms of access to
market infrastructure, the households are located relatively far from
market centers, at an average distance of 4.8 km. Average cattle herd
size per household is thirty animals, but is significantly different
between the pastoral, agro-pastoral, and crop-livestock production
systems at the 1% level. In the pastoral system, a household owns an
average of seventy-three cattle, while in the crop-livestock system in
Kenya, households own twelve cattle, mainly comprising the local Zebu
breeds. In Ethiopia, the average cattle herd size is six animals, mainly
comprising male stock, which is used for traction. In pastoral systems,
livestock keeping is a central component of the livelihood system and is
closely linked to the cultural and social lives of the communities,
where livestock numbers are an important means of demonstrating wealth
and a mode of cushioning high livestock losses experienced during major
droughts or disease outbreaks.
Empirical Results
The simulated maximum likelihood estimates for the mixed logit
model that allows for correlations between taste parameters are reported
in table 4. (4) Following Hensher, Rose, and Greene (2005), we used a
zero-based, asymptotic t-test for individual parameter standard
deviations to determine the set of random parameters. From this, the
traits trypanosomosis, traction, fertility, live-weight, purchase price,
feeding requirement, and reproduction ability were entered as random
parameters in the mixed logit estimations, while watering frequency and
coat color were assumed fixed. Further, we assumed a normal distribution
for all random parameters, except purchase price, which was assumed to
have been drawn from a triangular distribution.
The model was estimated using NLOGIT software version 3.0
(Econometric Software, Inc. 2002) utilizing 100 Halton draws for the
simulations. Given that the estimation of mixed logit first involves the
estimation of a conditional logit model to derive initial start values
for each of the parameters in the mixed logit model, the relative
performance of the conditional and mixed logit models can be compared. A
likelihood ratio test is performed to test the null hypothesis that the
conditional logit fits the data better than the mixed logit. The sample
values of the likelihood ratios are 941.8 and 793.6 for bulls and cows
traits, respectively, with a critical value of [[chi
square].sub.20,0.01] = 37.6, thus rejecting the null hypotheses. This
finding indicates that the mixed logit model, which allows random taste
variation, fits the data better than the conditional logit model, which
assumes fixed taste parameters.
With the notable exception of the purchase price, the mean
coefficients of the random parameters for bulls in table 4 are all
significant at the 1% level. The model reveals preference for bulls that
are trypanotolerant, cheap, highly fertile, have good traction
potential, and high live-weight. Associated with each of the mean
coefficient estimates of the random parameters are derived standard
deviations calculated over the R draws, indicating the amount of spread
that exists around the sample population. The standard deviation of each
random parameter coefficient is highly significant, indicating that
these coefficients are indeed heterogeneous in the population. The
nonrandom parameter, watering frequency, is positive and highly
significant, implying that there is a preference for bulls that are
drought-tolerant (need to water only once in two days). (5)
Mean coefficient estimates for all random parameters for cow traits
in table 4 are significantly different from zero at the 5% level, and
the standard deviations are also highly significant, implying existence
of preference heterogeneity in the population. The results indicate
preference for cows that are cheap, trypanotolerant, have high milk
yield, and high reproduction ability. Cows that need supplementary
purchased feeds are not preferred, as indicated by the negative
coefficient on feeding requirement. The constant variable in the model
results in table 4 represents the "don't buy" choice
option and is the base for the choice model, as it is associated with
"zero" utility. It takes a value of one if the option is
"don't buy" and zero otherwise. The results indicate a
strong negative preference for this option, implying that the
respondents preferred to select the other two choice options associated
with various trait levels.
The estimated means and standard deviations of the coefficients
provide information on the shares of the population that place a
positive or negative value on the bull and cow traits. (6) Preference
for the trait tolerance to trypanosomosis is skewed to the positive
orthant, with 75% and 73% of the cattle keepers preferring
trypanotolerant bulls and cows, respectively. A bull with good traction
ability is preferred by 88% of the cattle keepers. Similarly, a cow with
high reproduction potential is also preferred by 88% of the population,
while 72% of the population has a negative preference for paying for
feed supplements for cows. The estimated mean and standard deviation
coefficients for the purchase price of cows reveal that 39% of the
distribution is below zero and 61% is above zero. This indicates that
two-thirds of the population display a negative marginal utility for
income, implying that for this group, purchasing a cow may result in an
increase in utility, which outweighs the decrease in utility from
spending monetary resources on the purchase of the animal. This is even
more pronounced for bulls where only 23% of the population displays a
positive marginal utility for income. A plausible interpretation for
these estimates is that cattle keepers may view the cost of purchasing
cattle as a worthwhile expense, due to the multiple benefits associated
with owning a cow or a bull. (7)
Of significant interest is the derivation of the marginal rate of
substitution between the traits and the monetary coefficient (purchase
price in our analysis) as it provides an estimation of implicit prices
for the traits. (8) Hensher and Greene (2002) suggest that to derive
behaviorally meaningful values from mixed logit, the distributions from
which the random parameters are drawn need to be constrained. Although
little is reported in the literature about the best constraint to
implement, Hensher, Rose, and Greene (2005) argue that constraining the
standard deviation parameter estimate to that of the mean of the random
parameter for a triangular distribution guarantees nonnegative implicit
price values. Implicit prices are therefore computed using conditional
constrained parameters. (9) The results are reported in table 5. (10)
The estimates are validated using the household-level survey data
and past studies. Estimates of willingness to pay (WTP) for traits
parameters indicate that a trypanotolerant animal is valued at U.S. $25
more than a trypano-susceptible one. According to the survey data, the
average cost of treatment or control of trypanosomosis per year per herd
varies from U.S. $6 to U.S. $37 in Ethiopia, while in Kenya it is an
average of U.S. $36 in crop-livestock systems but can be as high as U.S.
$219 in pastoral systems, depending on the herd size and number of
treatments per year. The choice experiment estimate of U.S. $25,
therefore, appears plausible, although more information can be gained by
assessing the distribution of the estimate in the population. An
adaptive bounded kernel density approach has been utilized to examine
the distribution of the WTP for the various traits that have values that
are inherently bounded from below by zero. We employ Gaussian kernels
with variable-bandwidth density estimator that supports boundary
correction for variables with bounded domain (Jann 2005). (11) However,
WTP trait values for traction potential may plausibly take positive and
negative values, since it is a highly valued trait in cropping systems,
yet most pastoral communities view use of cattle for draft power as a
taboo and therefore exhibit negative preference for the trait.
Similarly, cattle keepers have a negative preference for cows that
require supplementary purchased feeds to boost milk production. This is
probably because most of the systems in the study area are subsistence
and not market oriented and additional costs associated with production
result in a decrease in utility. These distributions are presented in
figure 1. The distribution of WTP values for trypanotolerance presented
in figure 1(a), shows a bimodal distribution having two peaks at U.S.
$19 and 36. This indicates presence of heterogeneity in trypanotolerance
trait valuations. Although little is reported in the literature about
valuation of trypanotolerance using stated preference methods, Tano et
al. (2003) show that disease resistance can be ranked very highly under
production systems of similar types to those covered in this study.
The kernel density plot in figure l(b) for traction potential also
provides evidence of heterogeneous preferences for traction potential in
bulls, indicated by the substantial negative values and the bimodal
distribution density having two peaks above zero. The mean of the WTP
distribution is U.S. $58 for a bull with good traction potential. This
finding is consistent with the finding by Tano et al. (2003) who found
that fitness to traction was the highest ranked trait for bulls in West
Africa.
[FIGURE 1 OMITTED]
Live-weight increase, which is associated with meat production, is
valued at U.S. $1.05 per kg. This is comparable to the average slaughter
weight value of U.S. $1.02 per kg found in Scarpa et al. (2003) for a
pastoral system in Kenya. A bull that needs to be watered only once
every two days, used as a proxy for drought tolerance, is valued at U.S.
$7 more than one that needs to be watered twice per day. This is not
surprising, since water is a constraint in the study areas especially
during the dry seasons. The trait is, however, not significant for cows.
The kernel density distribution of WTP values for low watering frequency
in figure l(c), is tight with values concentrated around the mean,
indicating homogeneity in preference for this trait.
An important attribute in cows is the ability to calve every year
instead of once in two years. The Zebu cattle breeds, kept by most of
the respondents in the study sites, tend to have relatively longer
calving intervals of about 1.8 years. A calving interval trait of one
year is valued at U.S. $9.4, which is even higher than the value of U.S.
$8.1 for a cow with high milk production. The kernel density
distribution of WTP for reproduction ability in figure 1 (f), indicates
large variance for positive values. The value of purchased supplementary
feeds for cows that cattle keepers would be willing to accept as
compensation for utility reduction is U.S. $10.7. Cattle keepers are
reluctant to raise cows requiring purchased supplementary feeds to boost
milk production. Due to scarce financial resources, the cattle keepers
prefer cattle that do not require externally acquired inputs. The kernel
distribution associated with the implicit price for supplementary feeds
in figure 1(d), has a mean substantially in the negative range
indicating significant aversion to this trait, though there is a small
fraction in the positive orthant.
The maximum likelihood estimates for the latent class model for
cows and bulls are reported in tables 6 and 7. To identify the number of
latent classes to be used in the analysis, we employed the Bayesian
Information Criterion (BIC) proposed by Boxall and Adamowicz (2002).
This criterion is low and more intuitive for the model with three
classes for bulls and cows. (12) Therefore, we estimated three-class
latent class model (LCM) for cows and bulls. The LCM results indicate
significant heterogeneity in preferences across latent classes as
revealed by the differences in magnitude and significance of the utility
function parameter estimates. For instance, table 6 indicates strongly
positive trypanotolerance trait coefficient for class 3 in comparison to
the other two classes while the traction coefficient is strongly
positive for class 1. The class membership coefficients for the third
class were normalized to zero in order to be able to identify the
remaining coefficients of the model. The class membership coefficients
for bulls and cows estimations indicate that the probability of being in
a class is significantly related to the cattle keepers' production
system. Table 6 shows that 54% of the respondents who participated in
the choice experiment for bulls have a fitted probability to belong to
class 1, which is strongly significant for agro-pastoralists. On the
other hand, 19% and 27% of the respondents have a fitted probability to
belong to class 2 and 3, respectively. Class 2 membership coefficients
indicate that members of this class are likely to be pastoralists,
younger household heads with low levels of education and high income
relative to class 3. Class 3 could subjectively be associated with
crop--livestock farmers. Class 1 for cow traits in table 7 is strongly
significant for agro-pastoralists while class 2 is significant for
crop--livestock farmers, who have a lower income, relative to class 3,
and practice some tsetse fly control measures to control trypanosomosis.
Class 3 could subjectively be associated with being a pastoralist.
Calculations of implicit prices of the traits across the latent
classes show marked differences in preference structure as presented in
table 8. Cattle keepers in class 1 attach a high value to good traction
potential in bulls, which is even higher than the value they attach to
trypanotolerance and high fertility in bulls. This is not totally
unexpected since agro-pastoralists, the most likely members of this
class, consider draft power capacity in bulls for ploughing and
threshing grains an important reason for keeping cattle. This is further
supported by the fact that their cattle herd contains more males
(average of 2.2) than females (average of 1.7). Reproductive potential
in cows is also an important trait for this class, while milk production
is not statistically significant, suggesting that the trait is not
important for this class.
Class 2, mainly associated with pastoralists (for bull traits),
exhibits a different preference structure from the other two classes. In
this class, high fertility in bulls is a highly valued trait, while
trypanotolerance and live-weight have the same weight. Traction fitness
is not highly valued in this class, a finding that is in line with
expectations, since pastoralists do not use cattle for traction. They
are more concerned with obtaining larger herd sizes, which is linked to
high fertility trait in bulls. This class is also associated with
crop-livestock farmers for cow traits who exhibit negative preferences
for cows that need supplementary purchased feeds. High milk production
is an important trait for this class, though reproduction potential was
not found to be significant. Class 3, comprising mainly the
crop-livestock farmers (for bull traits), displays a high-preference for
trypanotolerant animals and bulls with good traction potential. High
fertility in bulls is not a significant trait in this class. In this
system, bulls are used for ploughing crop fields. For cows, high milk
yield and high reproduction potential are valued by the pastoralists.
Conclusions
This article has employed mixed logit and latent class models to
examine farmers' preferences for cattle traits in Kenya and
Ethiopia, using choice experiment data for bulls and cows. The empirical
results provide several insights to understanding cattle keepers'
choice behavior. The results from the mixed logit model revealed
significant preference heterogeneity among cattle owners, based on the
environment and production system. Good traction potential, fertility,
and trypanotolerance were found to be the most preferred traits in the
model of bull preferences. The most valued traits in the cow preference
models were trypanotolerance and reproductive performance.
Quite significant and interesting was the finding that traits
related to beef and milk yield were ranked below the other factors such
as traction, fertility, and resistance to trypanosomosis. This is
probably because the cattle keepers in these systems are subsistence
oriented and consequently place less emphasis on meat and milk
production for the market. These findings are particularly interesting
because traditional economic analyses on livestock and cattle breeding
programs often focus on raising milk and meat productivity, with little
emphasis on the nonincome traits such as traction and disease
resistance. For example, the National Sahiwal Stud of Kenya, which was
established with the objective of improving the breed for milk and meat
production in marginal areas, focuses its selection criteria for the
breeding stock on traits associated with meat and milk production such
as lactation milk yield, age at first calving, calving interval, and
growth rate without consideration of adaptation traits, which may be
useful for marginal areas (Mpofu and Rege 2002). However, the results
clearly suggest that breeding programs, as currently practiced in East
Africa may be focusing on