Description of variables used in the Two-Part adoption determinants model

Dependent variables

In the first part of the two part model, the estimation is through probit modeling since the dependent variable is a binary choice dependent variable. Therefore the dependent variables are 1 and 0 dummy variables which indicates whether or not a household adopted any of the cassava technologies respectively. In this regard, the probability of a household adopting the cassava technology is explained and estimated by: the sign, the statistical significance, and the magnitude of the parameter of estimates in the probit adoption model. Adoption of cassava technologies is categorized to cover all the five binary choice adoption forms. Thus the five dummy dependent variables are: (a) 1 if the farmer used improved seed (both certified and uncertified) and 0 if used local seed; (b) 1 if the farmer used improved certified seed and 0 if farmer used uncertified seed (both improved uncertified and local seed); (c) 1 if farmer used improved certified seed and 0 if farmer used improved uncertified seed; d) 1 if farmer used improved certified seed and 0 if farmer used local seed; e) 1 if farmer used improved uncertified seed and 0 if farmer used local seed.

In the second part of the two-part adoption model, estimation is by OLS since the depended variables are continuous variables (area in acres planted with the cassava technology). Thus the probit part in the two part model is complemented with adoption intensity measured by the area planted with cassava technology (in

acres) in an attempt to examine determinants of adoption intensity. The second part of the two-part model is estimated using Ordinary Least Squares (OLS) since the dependent variables take on the continuous form measured in number of acres planted with the cassava technology. Thus another set of continuous dependent variables becomes areas planted (in acres) with the cassava technology that was adopted i.e. the variable that took on the value of 1 in the 1st _{part (probit) of the two}

part model.

Independent variables

The household’s decision on whether or not to adopt cassava technologies and the decision on how much area to plant with the cassava technology (adoption intensity) are hypothesized to be associated with several independent variables. Accordingly, the study classifies the independent variables into five (5) categories: institutional factors; socio-demographics; wealth status, vulnerabilities and shocks, and regional dynamics.

Institutional factors: Access to extension, credit services, all-weather roads

(tarmac) and household membership in an Agricultural Innovation Platforms (AIPs) are dummy variables that take on the value of 1 if the household received extension and credit services; had access to an all-weather tarmac road and was a member of an AIP; and 0 if otherwise. According to literature (Bua, 1998; Khonje et al., 2015; Magrini and Vigani, 2016) both access to extension services and group membership exposes households to more information and learning opportunities and thereby increasing their chances of learning about the new agricultural technologies and their benefits. In deciding whether or not to adopt, households need information about the technologies and on the exact benefits accruable from adopting them. Such information can be attained through agricultural extension services and group memberships.

Credit access may enable farmers to afford costs associated with technology adoption (Bua, 1998; Khonje at al., 2015; Magrini and Vigani, 2016). Access to tarmac all weather roads may influence a farmer’s adoption decision in as far as it reduces transaction costs and enables efficient mobility. For these reasons, this study included these four institutional independent variables to assess whether access to extension and credit services, access to all weather roads and AIP

membership are associated with the household’s decision to adopt new cassava technologies.

Socio-demographic characteristics: Under this category the study includes

average education years of a household, age and gender of household head, and family size. Household average education years is a continuous variable that captures the total number of formal education years of all household members divided by the household size. Education level has implications on decision-making with regard to adoption of new technologies. Productivity in agriculture is higher for those with higher levels of education (World Bank, 2016b). Age of household head is also a continuous variable that may be associated with adoption of technologies because young and old farmers respond differently to innovations. Household head’s gender is included as a dummy categorical variable that takes on the value of 1 if household head is female and o if male. The household head gender variable is included to control for and explain the cultural institutional limitations imposed on women with regard to free association. This study further includes family size as a continuous variable that shows the total number of people in a particular household. Family size has implications on the resources available for the wellbeing of a household and as such is included to assess its influence on the decision to adopt new cassava technologies.

Wealth status variables: This set of explanatory variables include number

and value of livestock animals measured through Tropical Livestock Units (TLUs) owned by a household, asset value (UGX) of a household and total land operated in acres. Availability of resources may determine one’s ability to adopt new agricultural technologies.

Vulnerabilities and shocks: The study includes high input price shock as a

dummy variable that takes on the value of 1 if a household experienced it and 0 if otherwise. Shocks may significantly influence a household’s ability to adopt a new agricultural technology especially if they are related to input access.

Regional dynamics: captured as dummy variables taking on the value of 1 if

a household is domiciled in the Mid-western or Northern regions and 0 for eastern region. Regional dynamics are included to assess the influence of geographical location on household’s agricultural technology decision-making process.

5.8 Empirical results and discussion of determinants of cassava technology