CHAPTER 3 FACTORS INFLUENCING SMALLHOLDER FARMERS’ CHOICE OF STORAGE
3.2 Research methodology
3.2.2 Conceptual framework and selection of variables
The theory of rational choice, also known as a choice theory or rational action theory, guides the microeconomic behaviour of smallholder farmers. According to Lawrence and Easley (2008), the rational choice theory provides the framework for understanding and modelling social and economic behaviour. The theory tries to explain what will happen when individuals are faced with a choice decision, for example, when smallholder farmers have to choose from several post-harvest storage technologies. The underlying assumption of the theory is that farmers are rational when choosing storage technologies. Rationality means that smallholder farmers consider the costs and benefits of post-harvest technologies and pick an alternative that is likely to give them the greatest satisfaction (Abudulai et al., 2014;
Coleman, 1973).
Qualitative choice analysis methods are used to study this behaviour. The methods describe the discrete choices of smallholder farmers in choosing, in this case, a storage technology according to a number of explanatory variables. The choice models are developed from economic theories of random utility. Random utility theory assumes that a decision maker, such as a farmer, always chooses the alternative for which the value of utility is maximized.
In economics, utility refers to the real or fancied ability of a good or service to satisfy a human want (Okoruwa et al., 2009). Hence, using the concept of utility, the choice that a farmer will make or should make, among the available alternatives can be predicted or described. This is achieved by assigning a utility to each of the possible mutually exclusive alternatives. According to the principle of expected utility maximization, from Expected utility theory (EUT), a rational investor such as a smallholder farmer, when faced with a choice among a set of competing post-harvest storage technologies, acts to select that investment which maximizes expected utility. Expected utility theory assumes that
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preferences of smallholder farmers comply with the axioms of ordering, continuity, and independence (Starmer, 2000), and also that there is a utility function U that assigns a numerical value to each storage technology alternative (Hardaker et al., 1997). For example, if Y is a set of mutually exclusive choice objects (grain storage technologies) and a finite subset D of Y represents a decision problem (that is the farmer‟s behaviour is described by a random choice rule p which assigns to each decision problem a probability distribution over feasible choices), then the probability that the smallholder farmer chooses x є D is denoted p D(x).
Table 3.1 outlines the dependent variable and exogenous factors hypothesized to influence the choice of storage technology among smallholder farmers in Zimbabwe. In general, the literature shows that farmers‟ age has a negative effect on technology adoption (Bocqueho et al., 2011). Older farmers are argued to be more reluctant to change hence the negative influence on technology adoption. However, other studies suggest that older farmers are more experienced and are not risk-averse hence are more likely to adopt new technologies than younger farmers (Atibioke et al., 2012). In this study, the influence of age on technology adoption is thus expected to positively influence the choice of grain storage technologies in this study. Age is measured in years.
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Table 3.1: Exogenous variables used in the multinomial logit model Dependent variable Definition Measurement Typestorage_tech Type of storage technology
used to store maize grain
1=Insecticide technology;
2=No insecticide technology;
3=Other technologies (storage that used smoking, biological treatment of grain using plant leaves, and ash)
Exogenous variables Definition Measurement Apriori expectation
Age Age of household head Years +
Mar_status Marital status 1=Married,
0=Otherwise +
Sex Sex of Household Head 1=Male,
0=Otherwise +
Educyears Education level of
household head
Years +
TTstored Total quantity of grain
stored
Kilogram +
PCValuNONFOOD_Crop Value of non-food crop income
USD +
PCbusiwages_income Business and wages income USD -
PCLivestock_value Livestock value USD +
PCLandsize Land size Hectares +
Extension_acc Extension access 1=Yes,
0=Otherwise +
PCEquip_value Productive Equipment value USD +
PCVegetable_income Vegetable sales income in a year
USD +
Own_cell Ownership of cellphone 1=Yes,
0=Otherwise +
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Although it is not known how marriage status influences the smallholder farmers' choice of grain storage technology (Maonga et al., 2013), marital status (Mar_status) of the household head is hypothesized to positively influence the choice of grain storage technologies among smallholder farmers in this study. Mar_status is a dummy variable that takes the value 1 if household head is married and 0 otherwise. This study argues that married household heads could easily make a unified decision with minimum risk aversion to choosing a grain storage technology that is deemed to improve household socioeconomic status.
The influence of gender on technology adoption has also been varied. Male-headed households are argued to be better positioned within society due to differential access to external inputs, information, and services (Lopes, 2010). Therefore, the sex of the household head (Sex) is postulated to positively influence the choice of grain storage technologies among smallholder farmers. The variable is a dummy taking on the values 1 if male-headed and 0 if female-headed.
The quantity of grain stored (TTstored) is an important factor that can influence the choice of storage technologies (Adetunji, 2007). It is measured in kilograms. The study expects total grain stored to positively influence the choice of grain storage technologies among smallholder farmers.
Education (Educyears) is expected to positively influence the choice of storage technology in this study. According to Adegbola and Gardebroek (2007), education improves farmers‟
ability to process information, allocate inputs more efficiently and also enables them to accurately assess the profitability of new technology compared to farmers with no education.
It is defined at the household head level and measured in education years.
Contact with extension agents (Extension_acc) and the use of other media services such as cell phones (Own_cell) makes farmers aware of new technologies and how they can be used (Mwangi and Kariuki, 2015). Thus access to extension services (Extension_acc) and ownership of cell phone (Own_cell) are expected to have a positive influence on technology adoption in this study. Both variables are measured as dummies, that is, 1 if yes and 0 if otherwise.
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The study also includes household economic attributes as important factors influencing the choice of grain storage technologies among smallholder farmers. These are the land size (PCLandsize), the value of non-food crops (PCValuNONFOOD_Crop), business and wages income (PCbusiwages_income), livestock value (PCLivestock_value), equipment value (PCEquip_value) and vegetable sales income (PCVegetable_income). Land size is expected to positively influence the choice of grain storage technologies and is measured in hectares. The land is a productive resource that has a direct effect on output, therefore, households endowed with larger land sizes are more likely to adopt grain storage technologies (Bokusheva et al., 2012; Mwangi and Kariuki, 2015). The access of households to other sources of income such as vegetable sales, non-food crops, and livestock relieve them of financial constraints to adopt new storage technologies (Yehuala et al., 2013), hence are expected to positively influence the choice of grain storage technologies. However, this study argues that farmers who earn wages and have viable business outside farming are less likely to grow maize for storage. Hence, business and wages income is expected to negatively influence the choice of grain storage technologies among smallholder farmers. All the economic variables are measured in per capita value.