Smallholder Apiarist Essay

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3.5 Analytical Methods
3.5.1 Characterization of smallholder apiarists
Descriptive statistics were used to determine the mean difference of socio-economic characteristics between the apiarists. The variables considered were; age of household head (measured in years), gender (given by the sex of household head (male=1, female=0), education level of household head (number of years of formal education attained by the household head). Others were: farming experience (measured number of years in farming), beekeeping experience (recorded in years), non-farm income (measured as binary response on availability of other non-farm income on the farm (if yes =1, otherwise = 0), and household size (given by the number of household members). Also included
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Lastly, the relationships and their significance were established by carrying out the chi-square test between the honey yield and each of the variables of information source diversity, knowledge level and adoption level of apiarist.
3.5.3 Determining factors affecting Adoption Intensity in Apiculture
Previous studies have postulated that adoption intensity among smallholder farmers is function of knowledge of the farmer and socio-economic and market-based factors (Paxton et al., 2011; Alene et al., 2000; Feder et al., 1985). This can mathematically be presented as follows:
I^*=β_0+ β_1 X_1+β_2 X_2+ …….+ β_n X_n+μ_i=f(X_i) (6)

Where I is the intensity of the use of the technology, I* is equal to an index reflecting the combined effect of the explanatory variables hindering or promoting the use of the technology, I* is not observable and is recorded as zero for not using the technology.
For instances, a study by Kizito and Steve (2009) on analysis of the intensity of farmers’ adoption of different components of conservation farming practices by vulnerable households in Zimbabwe modeled adoption as
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Available literature shows that considerable focus in technology adoption in agriculture and apiculture in particular focused more on the incidence of adoption and the perception of farmers on new technologies, once innovative technologies are introduced in an area. However, limited attention has been paid to adoption behavior of the technologies beyond what happens after adoption has taken place. Thus, leaving a knowledge gap on some important aspects of adoption such as adoption intensities and adoption spread, for which this study is intended to address.
The current study modified the empirical framework of Kizito and Steve by introducing two variables that are peculiar to beekeeping. These variables are hive colonization and honey yield size. The reason for inclusion hive colonization and honey yield in the modeling is because they are key factors in motivation farmers, first to adopt a technology but also practice well apiary activities in anticipation of higher benefits. Thus, the current study estimated the adoption intensity using empirical model in equation (8)

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