Rural Development Policies and Sustainable Land Use in the ...

62 CHAPTER 5
the basic grains/livestock/farmworker strategy.
For households pursuing off-farm employment,
farm equipment may yield high
returns by enabling them to spare labor for
more remunerative off-farm opportunities,
as well as possibly contributing to agricultural
employment opportunities off their
own farm (i.e., if the household members
use their own equipment when working for
other farmers). For households pursuing
coffee production, ownership of equipment
such as a sprayer is likely quite important to
the profitability of the enterprise. Our results
concerning the determinants of the
value of perennial crop production (Table
5.9, discussed below), which show large impacts
of external inputs such as fertilizer,
pesticides, and herbicides on perennial production,
support the argument that equipment
to apply such inputs is a profitable
investment in coffee production. Machinery
and equipment appear to be much less remunerative
for households pursuing basic
grains or livestock production only.
In the model with interaction terms, we
find that agricultural training has a significant
positive association with incomes for
basic grains only producers (incomes per
capita of basic grains producers are 1779
Lempiras higher among those who have received
training), though the effect is smaller
than in Table 5.3 and only weakly statistically
significant. We find a very large and
strongly significant positive association of
training with incomes of livestock producers
(+10,447 Lempiras with training). Again, it
is hard to believe that training could have
such a large impact on these producers’ incomes,
and these results are not robust in a
median regression version of the model. 47
Thus, if there are positive impacts of agricultural
training programs, these positive
impacts are greatest for livestock producers.
Adoption of Sustainable
Land Management Practices
The determinants of land management practices
are shown in Table 5.5. Three practices
are analyzed—use of no burning, zero or
minimum tillage, and incorporation of crop
residues. Other land practices, including
use of mulching, manure, and others, were
not sufficiently common to permit reliable
estimation of the parameters of the probit
model specified in equation (4) in Chapter
3. 48 The regressions are estimated using
parcel-level data, because this is the level at
which data on these land management practices
were collected. Subsequent regressions
on external input use, labor use, and crop
47
In the median regression version of the model with interactions, the coefficients of agricultural training and
training interacted with livelihood strategies were statistically insignificant. The only statistically significant coefficient
in that regression was the interaction of equipment/machinery ownership with the basic grains/farmworker
livelihood strategy, for which the coefficient was positive and of similar magnitude to that reported in Table 5.3.
Thus we have confidence that the returns to equipment and machinery are substantially higher for households in
the basic grains/farmworker category, but less confidence in the other interaction results. These regression results
are available from the authors on request.
48
With a relatively small number of positive observations of the dependent variable and many dummy variables
in a probit model, it often occurs that the dependent variable is always zero or always positive for one of the
values of a given dummy variable. In this case, the model cannot be estimated with that explanatory variable included,
and the STATA software automatically drops that variable and the observations for which the response is
perfectly predicted by the dummy variable, and re-estimates a smaller version of the model. Effectively the model
is deterministic for some observations, so a smaller stochastic model is estimated. This procedure resulted in a
large number of dropped observations in the regressions for mulching and manure. Although these regressions
are not reported, they are still used to determine predicted values of the probability of use of these practices,
which are used as instrumental variables in the IV estimation of equation (1) in Chapter 3 (and discussed in section
5.6 below). Some of the predictions from such models are deterministic (i.e., for the observations that were
dropped based on certain values of the dummy variables in the full model). The results of these regressions are
available on request.