An Economic Assessment of Banana Genetic Improvement and ...

16 CHAPTER 2
transgenic bananas currently being developed.
Chapter 6 examines the potential for
transgenic cooking cultivars in Uganda,
considering endemic cultivars and elite
farmers’ cultivars as the counterfactual.
Chapter 9 investigates the adoption of FHIA
hybrids in Tanzania, compared to endemic
cultivars and farmers’ exotic cultivars.
We addressed the problem of selection
bias through a combination of sampling
methodology and application of a treatment
model (Chapter 9). The sample survey design
is summarized next, with a more complete
description in Appendix D.
Sample Survey Design
The population domain was selected to
cover areas specializing in banana production,
including those with decreasing, unchanging,
and increasing levels of production.
These correspond roughly to the central
and southwest geographical zones in Uganda,
and the Kagera Region of Tanzania.
A geo-referenced map of East Africa
indicating principal banana growing areas
was provided by the International Network
for the Improvement of Banana and Plantains
(INIBAP) to delineate the domain.
Because there are trade-offs in precision as
the number of stratifying variables increases
with a fixed sample size, we confined the
number to two.
The first is environmental. A critical
parameter for the adoption of cultivars
with improved resistance to pests and disease
is the yield advantage attained relative
to other banana cultivars. Other than farmers’
management practices, which are part
of farmer decisionmaking (and thus, cannot
serve as a control variable), relative
yield advantages depend on the biotic
stresses and productivity potential of the
growing environment. In consultation with
INIBAP, International Institute of Tropical
Agriculture (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
(Tush emereirwe et al. 2001). Elevation
was delineated at 1,200 m above sea
level (m.a.s.l.), defining low elevation as
below 1,200 m.a.s.l. and high elevation as
above this value.
The second stratifying variable was previous
exposure to new banana cultivars
(exposed; not exposed). Areas of exposure
were defined as a local council level 3 (LC3,
or subcounty) or ward where researchers,
extension agents, or other program agents
had introduced improved planting material
(banana suckers) in at least one community.
Areas with no exposure were defined as
those where no organized program designed
to diffuse improved planting material had
been conducted, according to personal consultation
with NARO and ARDI, records of
the Kagera Community Development Programme
(KCDP), and other program 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
cultivars.
Our intention in defining “exposure” at
a broader geographical scale than the village
was to ensure that we accounted for the
possible transmission of planting material
and information among as well as within
villages. Clearly, although we expect to
have reduced the selection bias at the household
level, we have not eliminated the bias
associated with the decision to promote new
planting material in one administrative area
rather than another (“program placement”).
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 a total of four strata
(i = elevation, j = exposure): (1) low elevation,
with exposure (i = 1, j = 1); (2) low elevation,
without exposure (i = 1, j = 0); (3)