An Economic Assessment of Banana Genetic Improvement and ...

MODEL OF POTENTIAL DEMAND FOR CULTIVARS IN UGANDA 85
Sensitivity of Farmer Demand
The fitted model is used to analyze the sensitivity
of total demand for host cultivars to
genetic transformation by varying the perceived
yield losses due to weevils and black
Sigatoka fungal disease. Biotic constraints
can be considered individually and jointly,
demonstrating the gains that might be
achieved in use levels through multiple
compared to single gene insertion. Next, the
effects of removing impediments to use
through other public investments in education,
market infrastructure, and extension
are depicted, given the research investment
made in genetic transformation. An increase
in the supply (or farm-gate) price from a
mean value of Ush 2,000 to Ush 4,000 is
assumed as a result of improvement in
bunch size and quality or market efficiency.
The time taken to get to a nearby banana
market is halved from a mean value of 1
hour, reflecting improvements in roads or
better means of transportation. The education
of the banana production decisionmaker
is increased through completion of
primary school (7 years) from a mean value
of 5 years. Finally, more visits by extension
agents are made to improve dissemination
of information and knowledge about banana
production, including understanding of pests
and diseases.
Nakitembe is again used as a representative
example. Figure 6.1 depicts the sensitivity
of farmer demand for planting material
to changes in the effective resistance to biotic
pressures when a single resistance gene
(for black Sigatoka) or multiple resistance
genes (for black Sigatoka and weevils) are
successfully inserted into suitable banana
genotypes, including the effect of supporting
public investments. The demand increases
twofold when full resistance is
achieved, and diffusion of improved planting
material is reinforced through investments
in education, market infrastructure,
and extension.
Sensitivity of cultivar demand to insertion
of multiple resistance genes is greater
than the more gradual effect of reduction in
loss from only one biotic constraint. For a
crop affected by a complex of biotic pressures,
the marginal effect on expected demand
will be much greater (close to a 50
percent increase in the total number of
plants) when insertion and expression of
more than one resistance trait are jointly
achieved.
Note that two fundamental conditions
are necessary for outcomes such as these to
be borne out in the banana groves of Uganda,
and neither of them is sufficient. The first
condition, which is technological, is that
genes must be successfully inserted and
expressed. The second concerns farmers’
perceptions. Even if the gene is inserted and
expressed, farmers may not perceive its effects,
because disease or pest incidence is
variable across a banana plantation, and the
mechanisms generating some effects (such
as weevils) are better understood than others
(such as black Sigatoka, a more recently
introduced disease). Where necessary, perceptions
and understanding of losses from
pests and plant diseases can be enhanced
through educating farmers.
As expected, overcoming impediments
to adoption through public investments in
education, extension, and market infrastructure
augments the magnitude of the effect
on demand induced through scientific (genetic)
innovations. The combined changes
in these variables act more as a shift than as
a pivotal adjustment of the total aggregate
demand for the planting material. About 50
percent of the shift is attributed to farm-gate
price response, with education and extension
having a smaller, though important,
influence on its magnitude. Shortening the
time taken to reach a nearby market has an
offsetting effect (though only by 2 percent)
by enabling some substitution of market
purchase for on-farm production of food.
Improvements in market signals, such that
quality differentials across banana bunches
are captured, could stimulate price responsiveness,
with important implications for