An Economic Assessment of Banana Genetic Improvement and ...

MODEL OF POTENTIAL DEMAND FOR CULTIVARS IN UGANDA 81
elicited from the farmer for the previous
year, by cultivar (Hardaker, Huirne, and
Anderson 1997). It is hypothesized that
farmers have a greater incentive to grow
more plants on their farms if it takes them
longer to reach the market place (because of
poor road quality or lack of adequate means
of transportation), and if the banana bunch
price they receive at the farm gate is higher.
Scientific evidence, as well as previous
participatory research, was used to cull a
subset of the most important attributes and
traits considered by banana farmers when
they choose banana cultivars (Gold et al.
1998). Farmers were asked to rate attributes
according to their importance and each banana
cultivar according to its possession of
the attributes (adapted from Reed, Binks,
and Ennew 1991). Consumption attributes
include cooking quality, measured as a categorical
variable. Expected yield losses
were calculated from subjective yields elicited
as triangular distributions (Hardaker,
Huirne, and Anderson 1997) in the presence
and absence of black Sigatoka and weevils,
and observed incidence of the disease or
pest. To aid farmers in recognizing the trait
or attribute, colored photographs of each
attribute of interest were presented. Physical
differences in the effects of both pathogens
are readily observable and visually distinguishable.
Estimates calculated from subjective
yield distributions compared broadly
with parameters tabulated from existing onfarm
trial data (Gold et al. 2004), although
clearly, farmers’ decisions are made on the
basis of their own perceptions and experience.
An inverse relationship is anticipated
between cultivar demand and the expected
yield loss variables.
Results
Determinants of Demand for Planting
Material of Cooking Cultivars
Following the formulation of our empirical
approach, from the 3,619 observations (517
households × 7 cultivars per household),
Table 6.3 Zero-inflated Poisson (ZIP)
regression results
Variable
Estimated
coefficient
p-value
Constant 2.5113*** 0.000
Experience 0.3590*** 0.000
Education 0.0322*** 0.000
Dependency ratio 0.1534*** 0.000
Household size 0.0732*** 0.000
Extension 0.0176*** 0.000
Assets – 0.0007*** 0.000
Exogenous income – 0.0002*** 0.000
Banana area 0.0402*** 0.000
Planting material – 0.0492*** 0.000
Low elevation 0.4030*** 0.000
Time 0.0289** 0.059
Farm-gate price 0.0377*** 0.001
Cooking quality 0.1701*** 0.000
Loss due to – 0.0053*** 0.000
black Sigatoka
Loss due to weevils – 0.0012** 0.032
N = 1,148 (zero observations = 199)
Log likelihood: –11,859.53
Vuong test of ZIP versus Poisson: z = 9.73 (p-value
= 0.000)
Note: *** and ** indicate statistical significance
at the 1 percent and 5 percent levels, respectively,
according to the hypothesized one-tailed or twotailed
tests.
1,148 household-cultivar observations, belonging
to the set ṽ were used to estimate
cultivar demand. All explanatory variables
are statistically significant for cultivar demand.
The hypothesized directions of the
effects are mostly consistent with expectations
(Table 6.3). here> 6.3near