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.