4. Discussion
The results of the MNL model suggest that out of fifteen variables,
twelve variables for AFS and four variables for ACS had significant
effects on adoption decision with respect to CAS while the three
variables i.e. education, age of household head, and origin had no
significant effect. The negative sign for the variable, age of household
head suggests that young farmers are more likely to adopt agroforestry
systems. In other words, the likelihood of adopting an agroforestry
system decreases with the increasing age of farmers. This may be because
young people are less risk-averse and have longer planning horizons to
justify investments in tree-based practices (Adesina et al., 2000).
Education (years of schooling) has a positive influence on the adoption
of both AFS and ACS. Education may lead to a better understanding of the
new technology when reviewing the different extension materials (Adesina
and Chianu, 2002).
Seven variables have significant effects in the case of AFS adoption.
The significant and positive sign for the variable, ‘sex of household
head’ implies that male-headed households were more likely to adopt an
AFS practice compared to their female-headed neighbours. This is
expected because the rural society of Nepal is male-dominated, and most
household decisions are made by male members of the family (Tiwari et
al., 2008). Existing literature has also shown that gender plays a
crucial role in decision making when it comes to the adoption of new
practices. For example, in studies carried out in Cameroon and Nigeria,
it was found that male farmers were more likely to use alley farming
than women (Adesina et al., 2000; Adesina and Chianu, 2002; Fabiyi et
al., 1991). In Nepalese mid-hills also, a positive association between
male-headed households and the adoption of agroforestry practice was
found (Neupane et al., 2002). The sign of the coefficient of the
variable ‘household size’ (economically active) indicates that the
likelihood of adopting AFS decreases with the increased household size.
In other words, the chance of adopting agroforestry is higher when the
household size is relatively low. This holds true because tree-based
farming is a less labour-intensive practice in the long-run (Cockfield,
2005), but other agriculture practices are labour demanding for
smallholders (Rai et al., 2018). A recent study by Cedamon et al. (2018)
from Nepal’s mid-hills also reinforces our findings. They argue that the
emerging remittance economy of the country has increased the
outmigration of Nepalese youths resulting in a short supply of labour
force, which made the Nepalese farmers practice less labour-intensive
cultivation practice such as agroforestry.
The results also suggest that large farmers are more likely to adopt the
tree-based farming practice. Landholding size was found to be the most
determining factor of agroforestry adoption in the study area. This may
be because larger-scale farmers are more likely to make higher
investments in new land management practices such as agroforestry. They
can take high risks and can survive crop failure resulting from
unfavourable conditions such as insect and pest outbreaks, hailstone,
and excess rail fall (Amsalu and De Graaff, 2007). Besides, larger farms
offer farmers more flexibility in their decision making, more
opportunity to new practices on a trial basis and more ability to deal
with risk (Nowak, 1987). Having a private source of irrigation is
positively associated with the farmers’ decision of AFS adoption over
conventional agriculture. A similar result was found in a study carried
out in Himachal, India by Sood and Mitchell (2009) and in Burkina Faso
by Ayuk (1997).
The results also suggest that households with off-farm income sources
are more likely to adopt the tree-based farming system such as AFS
compared to their neighbours having agriculture as a major source of
income. The reason may be that the off-farm income helps farmers take a
risk as it may serve as a safety net in case of crop failure resulting
from sudden natural calamities and other unexpected events. Adopting a
tree-based farming practice is a risk because farmers have to wait a
long time to get the return on their investment. Until the tree crop
harvest from the time of establishment, there would be a considerable
loss in farm production, which a farmer with no off-farm income is
hardly able to bear/face the loss. Similar results were found in the
Gunnungkidul region, Indonesia that the farmers having off-farm income
sources were more likely to adopt a tree management practice than those
with no off-farm income (Sabastian et al., 2014).
Similarly, ‘livestock herd size’ was found to positively influence a
farmer’s decision about adopting the tree-based farming system. It
suggests that an increase in livestock herd size results in the
increased likelihood of adopting AFS. In Nepal, trees are grown in the
farmland for fodder, fuelwood and timber. Fodder is a good source of
livestock feed in the study area. Trees provide green fodder during the
dry season of the year, which is very important for the milking
livestock to maintain milk production throughout the year. Table 2
reports that the tree densities and livestock herd size of the farming
systems are statistically different. AFS farmers raised a higher number
of trees and larger livestock herd compared to the ACS farmers. In the
mid-hills of Nepal, the number of livestock was the most significant
determinant of agroforestry adoption (Neupane et al., 2002).
Extension service has also positive impacts on adopting tree-based
farming systems. Farmers having frequent access to extension services
are more likely to adopt AFS compared to the farmers with a less or
minimum number of extension services. The result was not unexpected
because contact with the extension workers and receiving relevant
training allow farmers to learn more about the new practices and helps
them build up the confidence to adopt such technologies. Extension
workers help to clarify if any doubts that farmers may have regarding
the new practices and motivate them to adopt them. This finding
corroborates the existing literature (Adesina and Chianu, 2002; Ison and
Russell, 2007; Lohr and Park, 1994; Paudel and Thapa, 2004).
The result also suggests that farmers living farther from the government
managed forest are more likely to adopt tree-based farming systems than
those living close to the forest. When farmers easily get their daily
needs of fuelwood, fodder, timber and food fulfilled from the nearby
forest, they are reluctant to tree planting on their farmland (Rai et
al., 2017). On the contrary, the distant farmers have to spend more time
in the collection of these products from the forest and therefore they
are inclined to tree planting on their farms.