CHAPTER 7 THE POTENTIAL OF INNOVATION PLATFORMS AND ICTS IN
7.2 Research methodology
7.2.1 Study area description
In order to explore the nexus between innovation platforms, ICTs and the adoption of CSA practices the study targeted two smallholder dairy production project sites in Rusitu and Gokwe.
The Rusitu smallholder dairy project is located about 440 kilometres east of Harare in Manicaland Province and falls within latitude 200 02’ S and longitude 330 48’ E. The scheme is located in agro-ecological region I, characterized by high rainfall, low temperatures, well-drained
Page 159 of 231 soils and provides a perfect environment for dairying. The Gokwe smallholder dairy scheme, on the other hand, is located 338 kilometres west of Harare in the Midlands Province and falls within latitude 180 13’ S and longitude 280 56’ E. The scheme is located in agro-ecological regions III and IV characterized by low rainfall, fairly severe mid-season dry spells and is, therefore, marginal for dairying.
7.2.2 Sampling procedure and sample size
Multistage sampling, a complex form of cluster sampling, was adopted to guide sampling for the household questionnaire survey. Rusitu and Gokwe were purposively selected as the two research sites given their contrasting characteristics and representativeness of the generality of smallholder dairy schemes in Zimbabwe. At the second stage, smallholder dairy farmers in both Rusitu and Gokwe were stratified on the basis of their level of participation in dairy innovation platforms. The household was then used as the unit of sampling during the third and final stage of sampling. At this stage and within the strata, a probability sampling method was used as the basis for selecting households included in the survey. A total of 227 households were sampled for the study. Of these, 100 households (44.1%) actively participated in smallholder dairy innovation platforms, while the remaining 127 households (55.9%) were not.
7.2.3 Field data collection
Primary data were collected through the use of desk studies, key informant interviews, focus group discussions, and a structured household questionnaire survey. The use of numerous data collection methods was deliberate since this is a way of triangulating collected data for purposes of verification, validation and improving the reliability of collected data (Babbie et al., 2001;
Wagner et al., 2012). The formal household questionnaire survey collected data on household demographics, participation in innovation platforms, use of ICTs, asset ownership, livestock numbers and dynamics, dairy production and marketing, as well as access to livestock technology, inputs and support services. The questionnaire was pre-tested before use for purposes of ensuring that the study generates accurate, consistent, dependable and reliable data.
Page 160 of 231 7.2.4 Analytical model: Multinomial Logit (MNL) regression analysis
The decision on the methodological framework and econometric model used in this study depended on the research objectives and the hypotheses to be tested. Given that adoption decisions involve multiple options (1=full adoption, 2=partial adoption, and 3=non adoption), multinomial regression techniques were adopted to evaluate choice decisions. The precise methodology applied was the Multinomial Logit regression with the objective of analyzing the determinants of farmers’ choice decisions since this approach has been widely adopted for use in adoption studies involving multiple options (Hassan and Nhemachena, 2008; Joshi and Bauer, 2006; van Edig and Schwarze, 2012) and is usually simpler and produces more accurate results than other possible options such as Multinomial Probit (MNP) (Tse, 1987; Kropko, 2008). The main limitation of the MNL model is the independence of irrelevant alternatives (IIA) property, which postulates that the ratio of the probabilities of choosing any two alternatives is independent of the attributes of any other alternative in the choice set (Tse, 1987). Despite this weakness, as argued above, the model is still very useful and acceptable in analyzing decisions involving multiple choices.
The MNL model was applied as follows; letAibe a random variable representing the adaptation measure chosen by any farming household. The researchers assume that each farmer faces a set of discrete, mutually exclusive choices of adaptation measures. These measures are assumed to depend on a number of climate attributes, socioeconomic characteristics and other factorsX . The MNL model for adaptation choice specifies the following relationship between the probability of choosing option Aiand the set of explanatory variablesX (Greene, 2003).
j Je j e
A
ob j
k x x i
i k i j
...
1 , 0 , Pr
0
' '
(1)
Page 161 of 231 wherej
is a vector of coefficients on each of the independent variables X . Equation (1) can be normalized to remove indeterminacy in the model by assuming that 0 0
and the probabilities can be estimated as:
, 0,1,2... , 01
Pr 0
1
' '
J j
e x e
j A
ob j
k x x i
i
i k i j
(2)
Estimating equation (2) yields the Jlog-odds ratios
,if k 0ln ' '
j i k j i ik
ij x x
P
P
(3)
The dependent variable is, therefore, the log of any one alternative (adaptation strategy) relative to the base alternative (no adaptation). The MNL coefficients are difficult to interpret, while associating the j
with the
jth outcome is tempting and misleading. To interpret the effects of explanatory variables on the probabilities, marginal effects are usually derived (Greene, 2003):
j j
j
k k k j
j i j
j P P P
x P
0 (4)
The marginal effects measure the expected change in the probability of a particular choice being made with respect to a unit change in an explanatory variable (Greene, 2003). The signs of the marginal effects and respective coefficients may be different, as the former depend on the sign and magnitude of all other coefficients.
7.2.5 Model variables, expected signs and data sources
The dependent variables in the empirical estimation for this study is the level of adoption of the CSA practices in dairy production (AI and fodder production), and falls into three different
Page 162 of 231 categories (1=full adoption, 2=partial adoption, and 3=non adoption). Non adoption was taken as a reference category, while the choice of explanatory variables and expected sign of influence is largely guided by empirical literature that includes studies by Hassan and Nhemachena (2008) and Ahmed (2016). The same model was used for both AI and fodder production since the two dependent variables are affected by almost the same variables. Table 7.1 summarizes the explanatory variables used for empirical estimation, together with their expected influence on farm level adaptations.
Table 7.1: Description of explanatory variables and expected signs Explanatory
variable
Description Expected sign for
CSA adoption
Age Age of household head (years) +
Gender Gender of household head (1 if male, 0 otherwise) +/- Educ Number of years of formal education of household head + Agrictraining Household head completed agricultural training (1=yes, 0=no) + Stocktype Dominant herd stock type (1=indigenous, 0=otherwise) -
Herdsize Size of the dairy herd +
Lactcows Total number of lactating cows +
Dairyincome Estimated annual income from dairy activities ($) +
ICTuse Use of ICTs in dairy activities (1=yes, 0=no) +
Innovation Farmer participation in innovation platforms (1=yes, 0=no) +