2. Methods
2.1 Study area descriptions
Dhanusha district, which is in the southern part of Nepal and
shares a border with India, was selected for this study. About 60% of
the land comes under agriculture out of the total area of the district,
119000 ha. Data were collected from May through August 2015. Like other
parts of Nepal, agriculture is the major economy of the district where
about 90% of people are actively engaged in the cultivation of wheat,
rice, and sugarcane (Dhakal et al, 2015). After the state
federalization, the district now falls in the province no. 2. Located
approximately 95 m above the sea level, the district is one of the
hottest districts of Nepal with the average annual rainfall being 2199
mm. The meteorological data shows that April is the warmest month with
the average temperature being 39.60 C (maximum) while
January is the coldest with the average temperature of
21.40 C (maximum) (CBS, 2012).
Administratively the district consists of one sub-metropolitan city,
eleven urban municipalities and six rural municipalities. The Terai
Private Forest Development Association (TPFDA), a local NGO, has worked
to promote a tree-based farming practice in then nine Village
Development Committees (VDCs11Now, VDCs are a part of either
urban or rural municipalities after restructuring the state.)
covering 10,500 hectares (Figure 1). Therefore, these nine VDCs were
selected as the study site. After the state is restructured, some parts
of the study site fall in the urban municipality while most parts are
still VDCs, now known as rural municipalities.
Figure 1: Study Area
2.2 Household survey
A two-stage sampling approach was adopted for this study. First, one
ward22Ward is the lowest administrative unit. from each VDC was
selected through purposive sampling. This means a total of nine wards
were selected. Second, thirty-two households from each ward were
selected randomly. This means 288 sample households were selected.
In-person interviews were conducted with the head of the sample
households using a structured questionnaire.
The questionnaire covered the household data- the demography and
socio-economic conditions of the sampled households. Also included in
the questionnaire were the institutional and biophysical variables which
we hypothesized as adoption determinants and constraints. A total of 18
households were dropped out of the analysis since these households were
practicing a combination of two or more agroforestry practices,
agroforest/woodlot, boundary plantation and alley cropping.
2.3 Analytical model
Logistic regression model is the best fit when the outcome (dependent)
variables are unordered and categorical. When there are only two outcome
variables, the binary choice model (binary regression model) is the best
fit. In our case, the outcome variable is different types of farming
practices adopted by the study area farmers. Since we have more than two
farming practices i.e. (i) agroforest/woodlot system (AFS), (ii) alley
cropping system (ACS), (iii) combination of two or more AF practices,
and (iv) conventional agricultural system (CAS), the binary choice model
is not suitable. Out of the four practices, the third practice was
dropped off from the analysis since the practice was very rarely
practiced in the study area. Since we still have more than two practices
even after dropping one practice off, we chose Multinomial Regression
Model (MRM) for our analysis. Most commonly used multinomial regression
models are probit model and logit model. We chose the Multinomial logit
(MNL) model for our study since it gives more precise parameter
estimation (Kropko, 2007). The MNL model estimates the likelihood of
adoption of non-reference categories against a reference (base) category
in terms of relative risk ratio (RRR) (Miheretu and Yimer, 2017). The
other reason for choosing this model is that this has been more commonly
used in recent studies (Lin et al., 2014; Luus et al., 2015; Paton et
al., 2014; Miheretu and Yimer, 2017). Having three farming practices in
place, farmers can choose the one they prefer the most from the three
alternatives. That means their choice is mutually exclusive.
We assumed farmers follow the random utility theory, while making the
choice out of the three farming practices available. Therefore, we used
a random utility model while determining the farmers’ choice of farming
practices, as given by Greene (2003).
\begin{equation}
Y_{\text{ij}}=\beta_{j}X_{\text{ij}}+\varepsilon_{\text{ij}}\ldots\ldots\ldots\ldots\ldots\ldots.\ (1)\nonumber \\
\end{equation}where Yij denotes the utility of farmer iobtained from farming choice j , Xijdenotes all the factors affecting farmers’ decision to adopt a farming
practice j and βj is the parameter that
reflects changes on Yij due to changes inXij. We assumed the error terms to have an
independent and identical distribution (iid) (Cheng and Long, 2007).
According to profit maximisation, farmer i will, thus, only
choose a specific alternative j if Yij> Yik for all k ≠ j . This choice ofj depends on a number of predictor (independent) variables as
denoted by Xij in the above equation. IfYi is a random choice that a farmer can make, the
MNL model can be expressed as:
\(\text{Prob\ }\left(Yi=j\right)=\frac{e^{\beta_{j}x_{i}}}{\sum_{j=1}^{j}e^{\beta_{j}x_{\text{i\ }}}}\ldots\ldots\ldots\ldots\ldots(2)\)j = 0, 1, 2,…….., j
The above equation estimates
probabilities for j+1 farming choices i.e. three practices for farmers
with a number of independent variables, Xij. Here, we
are to estimate the probabilities of two non-reference farming
practices, agroforest system and Alley cropping system against the
reference category i.e. conventional agriculture and this can be done by
assuming β0 = 0 and expressed as follows:
\begin{equation}
\text{Prob}\ \left(\text{Yi}=j\right)=\frac{e^{\beta_{j}x_{i}}}{1+\sum_{j=1}^{j}e^{\beta_{j}x_{i\ }}}\ldots\ldots\ldots\ldots\ldots(3)\nonumber \\
\end{equation}\begin{equation}
\text{Prob}\ \left(\text{Yi}=0\right)=\frac{e^{\beta_{j}x_{i}}}{1+\sum_{j=1}^{j}e^{\beta_{j}x_{i\ }}}\ldots\ldots\ldots\ldots\ldots(4)\nonumber \\
\end{equation}2.4 Variables defined
The dependent variable is the
adoption of farming practices by farmers as denoted byYi . For MNL model, the outcome (dependent)
variable was denoted as:
Yi = 0 if a household adopts conventional agriculture system
(CAS) -reference category- (j = 0);
Yi =1 if a household adopts agroforest system (AFS)-
non-reference category- (j = 1);
Yi = 2 if a household adopts alley cropping system (ACS)-
non-reference category-( (j = 2).
Before the model is run, all the hypothesized independent variables were
tested for multicollinearity using the variance inflation factor (VIF).
We found the VIFs of the independent variables below 10 (1.09– 2.03),
indicating there is no issue of multicollinearity.
The estimation of the MNL model for this study was undertaken by
selecting CAS as the base category. The odds of two other farming
systems namely AFS and ACS against the CAS were estimated in this study.
Since the CAS was the base category, it was hypothesized that most
predictor variables will positively impact the adoption of the
tree-based farming practices i.e. one unit increase in an“ independent
variable will increase the likelihood of AFS and ACS adoption.
2.5 Variables used in the model
The three farming practices were
the dependent variables out of which farmers chose the one they
preferred the most. We extensively reviewed the contemporary literature
on adoption to identify and determine independent (explanatory)
variables. The explanatory variables included socio-economic,
biophysical and institutional characteristics (Table 1). However, some
variables were excluded in the model. The variable ‘farmers’ perception
on agroforestry’ was dropped off the model because studies suggest it
had no relationship with adoption (Alavalapati et al., 1995; Anley et
al., 2007; Carlson et al., 1994; Thangata and Alavalapati, 2003) and
also the methodological challenge we faced to precisely measure the
perception made us drop this variable off the model (Roberts et al.,
1999). The ‘slope gradient’ is another variable we ignored because of
little altitudinal variation across the sampled households. The third
variable ‘access to credit facility’ was also excluded because of no
financial guarantee from the financial institutions for agroforestry
promotion in the study area.