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.