An Economic Assessment of Banana Genetic Improvement and ...

DETAILS OF SAMPLE SURVEY DESIGN 177
In addition to farmers’ management practices,
which are part of farmer decisionmaking
and thus cannot serve as a control
variable, relative yield advantages depend
on the disease pressure and productivity
potential of the growing environment. In
consultation with INIBAP, IITA, NARO,
and ARDI scientists, elevation was selected
to represent the numerous correlated factors
that affect the incidence and severity of
most pests and diseases of bananas in the
Lake Victoria region (Speijer et al. 1994).
Elevation is also related to soil quality, climate,
and the surrounding vegetation in
these environments (Tushemereirwe et al.
2001). Low elevation was defined as below
1,200 m.a.s.l. and high elevation was defined
as above this level.
The second stratifying variable is institutional:
previous exposure to new banana
varieties (exposed or not exposed). Areas of
exposure were defined as LC3s or wards
where researchers or extension or other program
agents had introduced improved planting
material (banana suckers) in at least one
community. Areas with no exposure are
those where no organized program designed
to diffuse improved planting material has
been conducted, according to personal consultations
with NARO and ARDI, and
KCDP records. Areas included in exposed
strata represent the factual and those included
in the nonexposed strata represent
the counterfactual in predicting impacts of
improved banana varieties.
Four strata were delineated (i = elevation,
j = exposure): (1) low elevation, with
exposure (i = 1, j = 1); (2) low elevation,
without exposure (i = 1, j = 0); (3) high elevation,
with exposure (i = 2, j = 1); and (4)
high elevation, without exposure (i = 2, j =
0). Other factors known to contribute to
variation in adoption probabilities (such as
market access and agroecological zone)
were not used as criteria for stratification,
because large differences in variation were
not readily observable at the level of the
administrative units sampled and there
were no a priori grounds for selection of a
cutoff point. Instead, market access was
measured continuously in the household
survey instruments.
Geo-referenced data about banana production
systems, a digital elevation model,
maps of administrative units, and information
concerning previous diffusion of banana
planting material were used to disaggregate
the domain into the four strata. The
domain and four strata were then mapped
onto the administrative level of ward in Tanzania
and LC3 in Uganda. Wards and LC3s
were designated as high or low elevation,
based on a simple majority proportion of the
unit being above or below 1,200 m.a.s.l.
ARDI knowledge of any exposure in the
area (even if only one instance) classified
the ward or LC3 as exposed, and complete
lack of exposure classified the ward or LC3
as nonexposed.
It is important to note that the administrative
level of ward in Tanzania is a relatively
large area. Although the size of these
units is not necessarily optimal for our sampling
purposes, it is the lowest administrative
level for which we were able to obtain
accurate digitized geographic information.
This constraint results in a much larger area
to be aggregated for purposes of stratification.
Therefore, the possibility of a selected
village in a ward to be of high elevation or
exposed when the ward is classified as low
elevation or nonexposed (or vice versa) is
higher in the Tanzania portion of the survey
area than in the Uganda portion.
Allocation of Primary
Sampling Units to Strata
An efficient allocation of sample to strata in
formal sampling schemes is one that minimizes
variation within the stratum and
maximizes variation between the strata, in
turn minimizing overall sampling error
(Hansen, Hurwitz, and Madow 1953). When
the variances in population parameters are
known, the sample can be allocated opti-