3 Results
3.1 Sample characteristics
The average age of the sampled
household heads was 44 years. Younger farmers were practicing
agroforestry system and AFS farmers were the youngest (Table 2). In
addition, 15% of household heads were illiterate. They were the large
households with seven family members on average, nearly 1.5 times larger
than the average national family size, which is 4.9 people per family
(CBS, 2012). The sample households are dominated by the male heads
(57%), out of which 65% male heads were in the AFS adopting households
and 55% male heads in both ACS and CAS adopting households. Most
respondents (54%) were solely rely on farm income and the rest 46% had
both off-farm and on-farm sources of income.
The results indicated that 44% of the sample households had access to
irrigation. Of them, 62% of the total AFS farmers had access to the
irrigation facility while only 46% and 35% of the farmers adopting ACS
and CAS respectively possessed this facility. Many of these farmers
(56%) were migrated from Nepal’s hilly region and India to the study
area. However, 58% of farmers, who were in the AFS group were native,
while there were only 40% and 41% native farmers in ACS and CAS
respectively.
Out of eleven variables (continuous) tested, five variables i.e.
education, landholding size, livestock herd size, extension service, and
availability of transport means are significantly different in their
mean values (Table 2). The mean values of three variables i.e. household
head’s age, household size (economically active) and crop diversity were
significantly different for CAS and ACS. The statistics suggest that the
households with large holdings and bigger livestock herd size that are
headed by a young and educated male family member receiving more
extension services tend to adopt the tree-based farming (Table 2).
3.2 Association, relative risk and significance of explanatory variables
with regards to the choice of farming systems:
The parameter estimates
(association) and relative risk ratios (RRR) of the MNL model for AFS
and ACS with CAS as a reference group are reported in Table 3. The
coefficients show the direction of explanatory variables, while the RRR
shows the likelihood of adoption/dis-adoption of AFS and ACS by farmers
with respect to CAS. The model was significant at the 1% level. The
log-likelihood ratio (LR) test helps to identify the best models between
two competing models. In this analysis, it is expected the significant
relationship between likelihood of adoption behaviour and the selected
independent variables. This test suggests the effects of independent
variables as a group, rather than individual, even some variables and
RRR were not significant individually.
All other variables had expected signs except for the two variables,
‘irrigation facility’ and ‘origin’. The variable ‘irrigation facility’
is positive and has a significant relationship with the adoption of AFS
and ACS but it is not significant in the case of ACS. The variable
‘Origin’ had a negative sign in the case of ACS suggesting that a
migrated farmer is more likely to prefer ACS to CAS. Out of fifteen
variables tested, twelve variables were significant in the case of AFS
while there were only five variables significantly affecting the
adoption of ACS. Our result suggests that the likelihood of adopting AFS
would increase by a unit of 1.323 if the household head were a male.
Similarly, the AFS was 2.9 times more likely to be adopted by households
having off-farm income sources. Having a private source of irrigation
would increase the likelihood of AFS adoption by 1.73.
There are some variables with negative signs indicating that these
variables decreased the likelihood of adopting AFS and ACS with respect
to CAS. If a farmer were risk-averse, the likelihood of adopting AFS
would decrease by 89%. In other words, a risk-averse farmer is less
likely to adopt an agroforestry system. Similarly, having own source of
transport would decrease the likelihood of AFS and ACS adoption by 50%
and 16% respectively compared to CAS.