Information asymmetry has been recognized as a major impediment to small holder agricultural commercialization in most parts of sub-Saharan Africa. Theoretical and empirical studies in economics and sociology argue that social networks are the most persuasive source of information about new products and behaviours, but governments in developing countries continue to rely on extension services, usually a set of external agents, to communicate with farmers about new technologies. Mixed modelling has been used in this analysis to describe the role of information sharing among banana farmers in enhancing banana commercialization. Social network analysis (SNA) methodology was used to illustrate the network structure revealed by small holder banana farmers in Murang‘a. Double Dekker semi-partialing multiple regression quadratic assignment procedure (MRQAP) was used to determine drivers to networking among the farmers. Ordinary least square approach has been used to determine the extent to which networking influences banana commercialization in the area. The network structure depicted is diversified and heterogeneous in composition. Male and female farmers jointly interact in the sharing of information. In terms of network diversity, alters range from neighbours, banana traders, same organisation members and friends with friendship network dominating the structure. Friendship, gender, group membership and neighbourhood (geographical proximity) were found to have an influence to resource sharing among the farmers. Resources sharing among group members and networking among male and female farmers had an impact on the degree of banana commercialization in the study area.

Background information 
Human societies are comprised of individuals connected to one another by overlapping arrangement of social ties that together constitute a social network. At any point in time, banana farmers in Murang‘a don‘t produce in solitary but rather rely on each other to get new varieties of planting materials and market information for their produces. Information on production reputation of one farmer easily circulates among the farmers within the network. 

As an interest in networking has grown, different views have emerged regarding the extent to which networking relations are distinct and separate from other relations. White et al. (1996) suggests that networks provides a basis for resource sharing, gathering information and the general survival of actors in the network where there is inefficiencies in transmission of information; be it production, management or marketing of either business or agricultural outputs. 

Banana is an important food crop and income earner in the country. In 2011, the government earned 23 billion shillings from banana sold locally while flowers brought in revenue of 40 billion shillings. Compared with other fruits, banana is the number one income earner and second to flowers (GoK, 2012). Banana production in Kenya has increased in the past 15 years. This is mainly due to the improved production practices and introduction of improved banana varieties. The key production areas include Nyanza (30,303 ha), Eastern (15,074 ha), Central (11888 ha) and Western (14,116 ha) regions (HCDA, 2010). 

Banana production in Kenya receives little support from the government since it is rarely considered critical to national food security. Consequently, agricultural households face high transactions costs owing to limited access to financial services, information asymmetries and poor market infrastructures hence inhibiting their participation in markets (Nkhori, 2004). The converse concurs with Wanjiru et al., (2012) who argue that households with lower transaction costs are more likely to participate in markets or otherwise commercialize since they are more likely to recover their production and marketing costs. Participation therefore depends on ability to overcome cost of participating caused by information asymmetry among the players in banana production and marketing. 

A pilot project study by Wambugu et al. (2008) found that tissue culture have additional advantages over traditional banana accrued from the superiority of the planting materials in terms of; early maturity (12-16 months compared to 18-24 months for the traditional banana), bigger bunch weights (at least 20kg compared to 10-15kg for traditional banana), high annual yield per unit of land ( up to 50tonnes per hectare compared to 30tonnes for traditional banana), resistance to pests and diseases and coordination for market due to uniformity in maturity. 

Since introduction of tissue culture technology in Kenya more than ten years ago, banana has turned from a backyard crop to a commercial crop in the country (Kabunga et al., 2012). However, how this technology is transmitted to farmers is characterized by inadequacy from agricultural extension officers. Although the ratio of extension officers to farmers in Kenya is relatively compelling compared to other East African countries, the situation is still not promising. Statistics show that the ratios of extension officers to farmers are: Kenya; 1:1000 (mFarmer, 2014), Tanzania; 1:1145 (SAMT, 2014) and Uganda; 1:2400 (Laura, 2012). Due to this low ratio, farmers do rely on social interactions among themselves to get crucial information on new varieties of planting materials and market information for their produces. 

Social network, an informal institutional arrangement, is one of the interaction form that has an impact on agricultural commercialization. Social network is an important platform within which actors or a set of individuals have connections of some kind to some or all of the other members of the set (Malerba, 2007). Owing to these connections, social networks can therefore ease transmission of information or the flow of new ideas and other resources hence can be a desirable avenue by which farmers can commercialize their production. 

Several studies regard social network as a major form of social capital given that it is a resource found in personal relationships maintained by households that can influence production decisions and economic outcomes (Putnam et al., 1994; Narayan and Pritchett, 1999; Grootaert, 2001; Renard and Guo, 2013). Therefore, prevalence of social network particularly in rural areas has prospects to improve the productivity as well as the welfare of households and the overall society. Actors within social networks can be connected on the basis of similarity (same locality, affiliations, or other similar attributes), social relations (kinship, affective or cognitive relations), interactions and/or resource/information flows (Hartmann et al., 2008; Borgatti et al., 2009); as such one farmer in a network affects other farmers‘ choices directly without the intermediation of the market. Consequently, these farmers‘ network conceptualized households as often participating in networks that reduces market barriers and therefore enhancing the probability of banana commercialization.

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Item Type: Kenyan Topic  |  Size: 51 pages  |  Chapters: 1-5
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