Economic analysis of farmers'
preferences for crop variety traits using a choice experiment approach: lessons
for on-farm conservation and technology adoption in
Sinafikeh Asrat[1]
Abstract
Societies depend on agricultural
innovation processes for food security on local, regional and global scales.
Crop genetic resources, embodied in the seed planted by farmers, are integral
components of these processes.
1. Introduction
Societies depend on agricultural innovation processes for food security on local, regional and global scales. Crop genetic resources, embodied in the seed planted by farmers, are important components of these processes. Farmers, plant breeders, gene bank managers and other crop scientists draw on diverse crop genetic resources to innovate, support, and benefit society at large (Smale, 2006).
In agricultural systems, crop biodiversity is essential to combat the risks farmers face from plant pests, diseases and climatic shocks. Crop biodiversity also underpins the range of dietary needs and services that consumers demand as economies change (Edilegnaw, 2004; Smale, 2006). Crop genetic resources are natural assets that are renewable but vulnerable to losses from either natural or human-made interventions, including the disruptions caused by droughts, floods or wars, as well as the gradual process of social and economic change. Technological changes in agricultural production over the past century, spurred by crop genetic improvement combined with the use of other farm inputs, have transformed rural societies in many parts of the world (Smale, 2006). Not all of these changes have been positive. For example, there is a growing concern about potential loss of crop biodiversity associated with social and economic change. The common challenge now is to develop strategies that enable crop genetic resources to be managed in ways that satisfy the needs of farmers and consumers at present and in the future.
Some countries
with a high amount of unique crop diversity belong to the group of poorest
countries in the world (Von Braun and Virchow, 1996).
The purpose of this study is to contribute to a better understanding of the challenges by providing an insight into Ethiopian farmers’ crop variety attribute preferences and to identify the most important farm household contextual factors that condition their variety attribute preferences. Undertaking on-farm conservation ventures requires understanding farmers’ variety choice and variety attribute preferences. Such an understanding will also help in the areas of research priority setting and targeted adoption of crop varieties.
Since many of
the outputs, functions and services that crop varieties generate are not traded
in the markets; we cannot rely on any type of market data. Instead we will use
the choice experiment (CE) method to assess the preferences of the farmers. In
a choice experiment….. We apply the experiment to four traits of sorghum and teff varieties (the two major crops in
the country) including sale price (marketability of the variety), productivity,
environmental adaptability (resistance to drought and frost occurrences), and
yield stability of the variety despite occurrences of disease and pest
problems. Our empirical analysis of farmers’ preferences for these attributes
is based on primary data collected from 131 teff
and sorghum growing farmers in
2. The Choice Experiment
Survey design
Of the range of environmental valuation approaches, the choice experiment method is most appropriate for valuing crop varieties, considering their multiple benefits and functions. This method enables estimation not only of the value of the environmental asset as a whole, but also of the implicit values of its attributes (Hanley et al. 1998; Bateman et al. 2003).
Crop variety attributes and levels used in this choice experiment are reported in Tables 1 and 2. In this study, the most important crop variety attributes and their levels were identified in consultation with experts in this area (crop breeders and researchers who have previous experience and knowledge on the subject), by reviewing previous studies and historical data from CSA, and by identifying the most important seed selection criteria put forward by the surveyed households during the first leg of the data collection process.
As can be seen from the tables below, the chosen attributes and their definitions are identical between the two crops – only the attribute levels for producers’ price and productivity characteristics are different – indicating that farmers’ concerns towards the two crops under study are similar, in its broadest sense. Apart from their importance to farmers, these attributes are also policy relevant for designing an incentive mechanism to undertake on-farm conservation ventures at least cost (for example, by identifying farmers who are demanding attributes embedded in local varieties) or for successful rural interventions like contextual crop variety development and diffusion.
Monetary
attributes are included in order to estimate welfare changes. Each of the first
two attributes can be used as a direct monetary attribute or as a proxy for
monetary attribute depending on the socio-economic setup of farmers
participating in the choice experiment survey. More specifically, it would be
more appropriate to use producers’ price as direct monetary attribute for
farmers actively participating in the local markets by supplying their teff and/or sorghum produce, and
productivity trait tends to be more fitting to those farmers whose output is
less than or just enough to satisfy the household food consumption needs;
hence, prohibiting them to supply part of their output to local markets.
Table 1 Sorghum
Variety attributes and attribute levels used in the choice experiment
|
Variety Attributes |
Definition |
Attribute Levels |
|
Producers’ Price |
The amount of money the
farmer receives by selling a quintal of the sorghum variety |
110 birr, 150 birr, 200
birr |
|
Productivity |
The amount of yield/hectare
the farmer is able to harvest by planting the sorghum variety on his land. |
14 quintals/hectare, 19
quintals/hectare, 25 quintals/hectare |
|
Environmental Adaptability |
Whether or not the sorghum
variety is resistant/ tolerant to drought and frost occurrences. |
The variety is adaptable
(resistant) Vs the variety is not adaptable (nonresistant) |
|
Yield Stability |
Whether or not the sorghum
variety gives stable yield year-after-year, despite occurrences of crop
disease and pest problems. |
The variety gives stable
yield year-after-year Vs the variety gives variable yield year-after-year. |
Table 2 Teff Variety attributes and attribute
levels used in the choice experiment
|
Variety Attributes |
Definition |
Attribute Levels |
|
Producers’ Price |
The amount of money the
farmer receives by selling a quintal of the teff variety |
210 birr, 270 birr, 330
birr |
|
Productivity |
The amount of yield/hectare
the farmer is able to harvest by planting the teff variety on his land. |
8 quintals/hectare, 15
quintals/hectare, 20 quintals/hectare |
|
Environmental Adaptability |
Whether or not the teff variety is resistant/ tolerant to
drought and frost occurrences. |
The variety is adaptable
(resistant) Vs the variety is not adaptable (nonresistant) |
|
Yield Stability |
Whether or not the teff variety gives stable yield
year-after-year, despite occurrences of crop disease and pest problems. |
The variety gives stable
yield year-after-year Vs the variety gives variable yield
year-after-year. |
The monetary attributes represent Willingness to Accept (WTA) compensation. As compared to Willingness to Pay (WTP), WTA is measured as a benefit rather cost[2].
A large number of unique crop variety profiles can be constructed from this number of attributes and levels[3]. However, in this study, orthogonalisation procedure[4] was used to recover only the main effects, yielding 9 alternatives representing a fractional factorial design or main effects[5] each allocated to different choice sets as explained in the next paragraph. Notwithstanding the statistical advantages possessed by complete factorials, recovering only the main effects was necessary because as the number of possible combinations become large, one is motivated to reduce these combinations into manageable number so that the researcher can undertake a practical work in the field without compromising on the capacity of the reduced combination to capture the most important sources of variation in preferences (Louviere et al., 2000) [6].
The choice sets were created using a cyclical design principle (Bunch, Louviere, and Andersson, 1996). A cyclical design is a straightforward extension of the orthogonal approach. First, each of the alternatives from a fractional factorial design is allocated to different choice sets. Attributes of the additional alternatives are then constructed by cyclically adding alternatives into the choice set based on the attribute levels. That is, the attribute level in the new alternative is the next higher attribute level to the one applied in the previous alternative. If the highest level is attained, the attribute level is set to its lowest level (Carlsson et al., 2007). We, therefore, assigned the 9 alternatives from our fractional factorial design to 9 choice sets and constructed two other alternatives per choice set following the procedure mentioned above. We followed these procedures twice, each used to construct either sorghum or teff profiles. An example of a choice set is presented in Figure 1.
Figure 1 Sample choice set
Survey procedure
Data are drawn
from two Peasant Associations (PAs) in
the North Eastern part of the country (
Stratified multi-stage sampling was adopted to identify Zones, Districts, PAs, villages, and farm households. Overall, a total of 131 farmers were selected and interviewed from two PAs found in Guba Lafto district of North Wollo zone. Enumerators explained, using the local language, the context in which choices were to be made; that attributes of crop varieties had been selected as a result of prior research and were combined artificially; and defined each attribute using visual aids to ensure uniformity; and that completion of the exercise would help agricultural policy makers in the design of variety development and local variety conservation interventions. Overall, a total of 1179 choices were elicited from a total of 131 farm households.
Econometric model
Consider a farm household’s choice of a crop variety, and assume that utility depends on choices made from a set C, which includes all the possible options of different crop varieties. This list of all options that are available to the farm household is referred to as the choice set. The farm household is assumed to have a utility function of the form
(1)
where for any
farm household i, a given level of
utility will be associated with any alternative crop variety j.
In this model, the utility of a choice is comprised of a systematic
(explainable or deterministic) component, 
, and an error (unexplainable or random) component, 
, which is independent of the deterministic part and follows
a predetermined distribution. Utility
derived from any of the crop variety alternatives depends on the attributes of
the crop variety, 
, and the social and economic characteristics of the farm
household, 
, since different households may receive different levels of
utility from these attributes. The choice experiment was designed with the
assumption that the observable utility function would follow a strictly
additive form (Birol, 2004). The model was specified so that the probability of
selecting a particular crop variety was a function of attributes of that
variety. That is, for the population represented by the sample, indirect
utility from crop variety attributes takes the form
(2)
Where 
refer to the vector of
coefficients associated with the vector of attributes describing crop variety
attributes. In the above specification the constant term, referred to as
“alternative specific constant”, or ASC in the literature, is dropped from the
indirect utility function because our choice sets do not include a status quo
or an opt-out option (Bateman et al.,
2003 pp. 7.5).
Even though segment analysis and use of social and economic characteristics help to recognize conditional heterogeneity, these methods do not detect for unobserved heterogeneity. It has been demonstrated that heterogeneity can be present in significant residual form even when conditional heterogeneity is accounted for (Garrod et al., 2002). Unobserved heterogeneity in preferences across respondents can be accounted for by using the random parameter logit model, which, unlike conditional logit is not based on the IIA assumption.
The random utility function in the random
parameter logit model is given by
(3)
where
respondent i receives utility U from choosing alternative j from choice set C. Like the case of conditional logit model, utility
is decomposed into a non-random component (V)
and a stochastic term (e). Indirect utility is assumed to be a function
of the choice attributes Z (as well as of social and economic characteristics
S, if included in the model) with parameters 
, which due to preference heterogeneity may vary across
respondents by a random component 
. By specifying the
distribution of the error terms e and
, the probability of choosing j in each of the choice sets can be derived (Cameron and Trivedi,
2005). Accounting for unobserved heterogeneity, this probability becomes
(4)
Since this model does not require IIA
assumption, the stochastic part of utility may be correlated among alternatives
and across the sequence of choices via the common influence of(Birol, 2004). Treating preference parameters as random
variables requires estimation by simulated maximum likelihood. Procedurally, the maximum likelihood
algorithm searches for a solution by simulating ‘m’ draws from distributions with given means and standard
deviations. Probabilities are calculated by integrating the joint simulated
distribution (Cameron and Trivedi, 2005).
Recent applications of random parameter logit model have shown that this model is superior to conditional logit model in terms of overall fit and welfare estimates (see for example Birol, 2004; Birol and Rayn, forthcoming; Kontoleon, 2003). The results of the random parameter logit estimations for sorghum variety choices are reported in Table 5. All the parameters except for producers’ price and productivity attributes were specified to be independently normally distributed and distribution simulations were based on 500 draws.
The choice experiment method is consistent with utility maximization and demand theory (Bateman et al., 2003). When parameter estimates are obtained, welfare measures can be estimated from the conditional logit model using the following formula:
(5)
Where CS is the
compensating surplus welfare measure, 
is the marginal utility of income (generally represented by
the coefficient of the monetary attribute in the choice experiment) and 
and 
represent indirect
utility functions before and after the change under consideration. For the linear utility index the marginal
value of change in a single attribute can be represented as a ratio of coefficients,
reducing equation (5) to
(6)
This part-worth (or implicit price) formula represents the marginal rate of substitution between income and the attribute in question, or the marginal welfare measure (willingness to pay or willingness to accept) for a change in any of the attributes.
Site and household description
Description of
the main study site characteristics for the two PAs surveyed in this study is
reported in Table 3 below. The
PAs share both similar and differing features concerning their main
characteristics. For instance, teff,
sorghum, and maize are among the most important food crops in both PAs.
Agro-ecologically, however, temperate agro-ecology is the dominant agro-ecology
in Woinye PA covering 83%; whereas, 95% of Ala Weha PA is covered with low land
agro-ecology. This diverse agro-ecology should increase the representative-ness
of our surveyed farm households since our sample is comprised of farmers who
came from the three major agro-ecologies in
Table 3
Summary of main study site characteristics
|
Study site characteristics |
Woinye PA |
|
|
Agro-ecological coverage |
Temperate
– 83%, Highland – 10%, and Lowland – 7%. |
Temperate – 5%, and
Lowland – 95% |
|
Most important food crops |
Teff, sorghum, dagusa, maize, wheat, and barley |
Teff, sorghum, maize, and cow
beans. |
|
Livestock assets owned by
an average household in the PA |
1 ox, 1 cow, 2 calves, 3
sheep, and 3 goats. |
2 oxen, 2 cows, 2 calves,
and 4 goats. |
Source: Agricultural bureaus
in Woinye and Ala Weha PAs.
Farm household characteristics in
The average
characteristics of the surveyed households and farm decision makers in
1)
gender of the household head (denoted as Sex in the model estimation, where
a value of 1 is for male)
2)
the number of household members who share the same food stock (denoted as
Household size)
3)
experience of the household head in years (denoted as Experience)
4)
whether or not any member of the farm household works off-farm (denoted
as Off-farm work)
5)
whether or not the farm household
has been participating in the agricultural extension package program (denoted
as Agri. Ext
Participation)
6)
average of walking distance (in minutes) the household head takes to
reach electricity, piped water, telephone, primary
school, secondary school, all weather roads, and irrigation infrastructures
(denoted as Access
services)[7]
7)
whether or not the household
head considers land shortage as the primary problem the household faces
(denoted as Land shortage)
8)
total land size operated by the household in hectares (denoted as Total land size)
9)
total value of livestock, in birr, that is currently owned by the
household (denoted as Livestock value)
10) whether or not the household head considers
his/her household to be at least self-sufficient in relation to other
households in the area (denoted as Poverty
status, where a value of 0 means the household considers itself poor or
very poor), and
11) number of dependents with no labor or money
contribution in the household (denoted as No.
dependents).
The average
characteristics suggest that a typical farm household in
Table 4. Descriptive statistics of farm household
contextual characteristics and their hypothesized effects on the demand for
attributes of crop varieties
|
Characteristics |
Mean (SD) N= 131 |
Producers’ Price |
Productivity |
Environmental adaptability |
Yield stability |
|
|
Household characteristics |
|
|||||
|
Gender (the household head
is a male) |
90.1% |
+, - |
+,- |
+,- |
+,- |
|
|
Household size |
5.38
(2.04) |
+ |
+ |
+ |
+ |
|
|
Experience |
25.38 (11.64) |
+ |
+ |
+ |
+ |
|
|
Off-farm work |
32.3% |
+ |
+ |
- |
||