3.1. Actual Rainfall Variability and Its Effect on FBSc
Long-term annual mean rainfall variability ranges from 38%, 33%, 25%, and 22% for Chiro, Kersa, Fedis, and Oda Bultum respectively. This implies Chiro and Kersa are highly variable while Oda Bultum and Fedis are moderately variable (
Table 1). For all cases, the CV of spring is higher than summer and annual rainfall. Thus, rainfall varies spatially and temporary with intra-seasonally and inter-annually variability. Rainfall amount with a CV greater than 30% is imply the area is vulnerable to drought [
35]. This argument is also supported by respondents’ perceptions.
Survey respondents ranked their perceived climate factors based on the frequency of occurrence and level of impact on crop production. Drought and delayed spring rain were ranked as the most important climate factors affecting farmers’ participation in FBSc. The result of Standardized Anomalies Index (Z) which demonstrates the intensity and frequency of drought indicates that for Kersa and Fedis 53% and 62% of the years are negative anomalies while it accounts 43% and 50% for Chiro and Oda Bultum for the year 1986 to 2018 (
Table 2). A substantial numbers of cases where also reported on prolonged drought and delayed rainfall in the past few years by FGD respondents. Indeed, farmers’ perceptions are based on their knowledge to capture the intensity and extent of drought. However, climate records capture average conditions at large spatial scales and diverse topographical environments within the district that often do not depict conditions perceived by farmers [
36]. Generally, successive drought and shortening of drought return periods were observed where farmers’ perception supports this argument.
All study sites are located in the long cycle crop growing region of Ethiopia where major crop production is mainly correlated with spring season rainfall. The spring season rainfall is crucial for the farmers as the season determines the preparation, planting activities and choice of seed and seed sources. These problems are often exacerbated by farmers’ inadequate access to very-localized early warning information on climate variability conditions. FGD claim that smallholder farmers’ might plant long-maturing varieties of sorghum in a season projected to have a short rainy season.
FGD participants also claim that climate variability affect seed supply from informal and FBSc because of delays in onset and early cessation of spring rain which may force farmers to miss the optimum sawing time. This has interrupted own farm-saved seed supply, limit the operation of social network, share of FBSc and, local markets. FAO [
1] argues that climate change as seed security stressors operating within an agricultural setting are usually exhibited by its effects on crop production, seed supply, and disrupt local market functionality. Similarly, Diriba et al. [
37] argued that adequacy of seed supply in Hararghe for various sources were mainly challenged by climate change and drought along with other factors. Thus, the changing climate works against a single optimal seed supply where farmers tended to shift between different seed sources.
3.2. Effect of Social Network on Farmers’ Exposure to FBSc
Farmers’ social network can affect the probability of farmers’ exposure/awareness about FBSc in many ways. Due social interactions farmers share information, advice and knowledge and this indicate what flows through interaction. Farmers who are exposed to information about FBSc through their networks are more likely to have a better understanding about the seed producers’ cooperatives and associated benefits, and hence decide in favour of joining it. This in turn affects participation decision. The exposure variable in this study shows knowledge of respondents’ on the characteristics of FBSc as a new innovation. FGD participants claim that they get information about FBSc from multiple sources such as fellow farmers who can be of relatives’ vs non-relatives, local, and extension agents. Farmers’ interaction with such group indicates network structure. Exposure to FBSc is fundamentally correlated to the structure of social interaction which is pre-condition for behavior changes [
14].
Social network as channel for learning and behavior change on innovation determines the nature and quality of information and knowledge shared in their social tie. This network supports exposure and participation by creating enabling environment for individuals to acquire new information, share their past experiences and knowledge with others on FBSc. Learning from peers may support potential members to make informed decisions. The learning can takes place by interacting and/or observing the actions of peers decisions and experiences. Linkage with external sources such as public extension agents and local administrators also viewed as one way of getting information about FBSc as a new innovation.
The result of current analysis (
Table 3) reveals awareness/exposure to cooperatives in social network is affected by the characteristics of social network. It is the characteristics of two different farmers that create social closeness based on social contagion theory in which both farmers’ are getting exposed to FBSc and enable them socially learn from each other. The nature and intensity of interaction between the matches varies with linkage based on kinship, friendship, geographic proximity, personal attributes such as education. In addition, it also varies with network structure showing the size of linkage. Most of the interaction reported on network size implying diversity of information available for each farmer households. These characteristics of network and attributes in turn affect the flows of resources that support smallholder farmers’ decision to join FBSc. The flows that support farmers’ decision to join FBSc include information on characters’ of FBSc, discussion and advice on quality seed production and management, and beliefs and resources necessary for expose to cooperatives. Ramirez et al. [
11] highlighted that extension services such as seed as technologies introduced to model farmers in a given village with the expectation that other farmers would observe the benefit conditional on farmers’ networks.
Focused group discussants revealed that in all study sites social learning is a powerful means in promoting FBSc as an innovation and is more effective than learning from public extension services. Farmers’ participation in FBSc is a continuous process involving farmers’ handling information from a variety of sources. FGD and key informant discussants revealed that there are different sources of information that influences farmers’ participation decision in FBSc. The major sources are the experiences of fellow farmers who were involved in seed production either through FBSc, FRG, NGOs-based seed multiplication, cluster based-seed multiplication, and other projects. In addition, farmers reported that they sought further information from external link with public extension agents and local administrators. FGD participants’ across all study sites frequently responded that farmers’ usually hit their links for further information regarding the economic viability of FBSc based on other farmers’ experiences making participation decision. Beaman et al. [
7] argue that lack of information is one of the impediments to agricultural technology adoption where social interactions can serve can as an important channel through which individuals can access information and learn about and eventually adopt new technologies and other innovations.
Organizational level networks serve as spaces for the negotiation of innovations where actors endeavor to engage others in the implementation of an innovation. For instance, weather-index insurance has been promoted by insurance company in collaboration with farmers’ cooperatives. Cooperatives and farmers organization are a natural focus for any new innovation as delivery channel. In Ethiopia, the adoption of weather-indexed insurance is higher when insurance is target to cooperatives or group-based informal insurance schemes [
38].
Chercher Oda Bultum and Afran Kallo unions in collaboration with Oromia insurance company has been working on crop insurance. For example, Misoma Gudina as primary seed producer cooperatives has been promoting weather-indexed seed insurance in Oda Bultum District. Most of FGD participants’ in all study sites argued that crop diversification and intercropping has been used as adaptation to climate change. Similarly, Shimelis and Kühl [
39] reported crop diversification is one of adaptation strategies used by smallholder farmers’ in Ethiopia. However, seed production through FBSc is basically monoculture in its practice where weather-indexed insurance services used as adaptation strategy to ensure continuity of seed production under FBSc. These activities also further support climate information services and strengthen the work of adaptation strategies.
In fact, accurate and timely weather data are crucial for successful adoption of weather-indexed crop insurance products. Weather-index insurance product enhances the adoption of adaptation practices. Million et al. [
40] reported that in Ethiopia, weather-index insurance allowed farmers adoption of improved seed. The use of climate information services drives the adoption behavior of farmers for climate-smart agricultural practices such as the adoption of water management and multiple cropping practices [
41,
42].
The FGD confirmed that farmers decided to participate in FBSc because of information and knowledge obtained from friends, relatives, local administrators, and extension agents. Out of the total respondents about 40% were exposed to FBSc. About 57% out of FBSc members are exposed to FBSc by social network (
Table 3). Similarly, available empirical studies indicated that social network serve as an important determinant of technology exposure and adoption decision making [
15,
16,
18] and decision regarding participation in farmer-organization such as cooperatives and cluster-based seed production [
22].
Table 4 presents the variable definition and comparisons of the means of explanatory variables between categories of participants and non-participants.
The average age of household is 45 and 49 years for participants and non-participants of FBSc. The average land holding size is 1.46 and 1.36 Ha for participants and non-participants of FBSc respectively. The average livestock ownership in size is 4.02 and 3.2 TLU. Thus, this shows that most individuals’ characteristics such as age, TLHa, and TLU are statistically insignificant differences between participants and non-participants in FBSc. However, there are differences between participants and non-participants in FBSc in education level of households and the difference is significant at a significant level of 0.05. Education is likely to have a positive influence on participation because well-educated farmers are more likely to possess the skills and networks necessary to initiate and manage an association [
43,
44].
The mean size of social network is 5.08 and 4.78 for participants and non-participants of FBSc, and the difference is significant at a significant level of 0.05. Network size is positively associated with membership in FBSc showing that the size of network is an essential driver of participation decision behavior towards collective action. When farmers receive the same information about the benefit of FBSc from many farmers, they are more likely to understand and decide to join FBSc than those who receive information from few or one person.
Having links with relatives and friends affect farmers’ participation in FBSc. There are significant differences in having links with relatives (t = 7.7,
p < 0.05) and friends (t = 6.3,
p < 0.05) between participant and non-participant in FBSc. The stronger the links the higher probability of being membership in FBSc. Songsermsawas et al. [
31] found that caste-based network generated peer effects in technology adoption.
Strength of tie is also measured by geographic proximity. Participation in FBSc as an innovation in social networks is influenced by tie strength, which is a reflection of the closeness and frequency of interactions among individuals. The result of network links on inter-village and intra-village also shows an association with farmers’ participation in FBSc. The mean number of links out of the random matches within the village (t = 2.79) and (t = 2.69) for participants and non-participations in FBSc. There is mean differences in intra-village network between participants and non-participants in FBSc and statistically significant at a significant level p < 0.05. This implies that network within the village is stronger than that of outside village. This implies exposure to FBSc is not promoted by having link out-side village as it requires continues interactions and reliable actors from whom the household believes.
FBSc participants found to have a higher proportion of farmers who have frequent contact with extension agent, access to irrigation and off-farm income and the result is statistically significant at p < 0.001. Farmers who perceive climate is changing and perceived high benefit of FBSc than other are participating in FBSc and the result is statistically significant at p < 0.00l. Smallholder farmers’ participation decision is also shaped by the strength of ties and their importance to the information being pursued. Having a link with external source such as public extension agents and local administrators is often viewed as weak tie.
3.4. Effect of Social Network on Farmers’ Participation to FBSc
Econometric (probit regression results on determinants of farmers’ participation in FBSc are presented in
Table 6. The chi-square results demonstrate that likelihood ratio statistics are highly significant (
p < 0.000), suggesting the model has the power to reliably explain behavior that leads to participation in FBSc. The parametric model shows result of the Probit regressions estimated for the sub-sample of exposed participants only, while the classic model shows result for the full sample of participants in FBSc, including those who are not exposed to FBSc. As indicated in
Table 6 non-exposure bias is insignificant. Thus, we present the result of both parametric and classic models, but we discuss only the result of the parametric model.
The study indicates that from all specifications participation decisions are positively and significantly associated with information network. This is in line with the theory and empirical evidence of network tie on technology adoption. In the context of new technological innovation practice a number of studies have found a positive relationship between information network and participation in collective action.
The results of endogenous social network variables such as size of social network, having links with relatives and frequency of contact with extension agents have positive and statistically significant effect on participation decision of farmers. Farmers who have large size of social network are more interested to participate in FBSc. Farmers who are members of FBSc share information and knowledge among themselves; this increases their inclination for further participation in FBSc. Thus, receiving FBSc information from multiple sources indicates large size of social network likely influence exposure to the characteristics of FBSc thereby increase the likelihood of participation decision of smallholder farmers’ to collective action. Moreover, when it comes to collective action, information received from relatives and extension agents are seen as powerful network links which increases the likelihood of participation decision in FBSc. This finding suggests the presence of endogenous effects of social networks on farmers’ participation in FBSc. Endogeneity of social network on collective action has been demonstrated by vast number of literatures [
42,
43]. Since social network and participation in collective action have reverse causality relationships.
Social network also demonstrate endogenous effect on technology adoption. Empirical evidence suggests that many individual adoption decisions such as in agricultural technology [
16,
17,
18] are positively correlated with the endogenity of social network. Similarly, Negi et al. [
18] reported that the effect of social networks on the adoption of technologies varies depending on the size of the network, the complexity of the technology, and heterogeneity in the agro-ecological and socio-economic conditions. Thus, the effects of networks on participation behavior in FBSc could be heterogeneous, depending on the types of network, the technology in question, and the characteristics of the members.
The individual characteristics of the peers (exogenous) (e.g., age, sex, education, livestock and land) in the social networks do not have association with farmers’ likelihood of participating in FBSc. This reveals that farmer’s participation in FBSc decision is not correlated with the exogenous characteristics of his/her network members. These shows there are no exogenous effects of social network on farmers’ participation in FBSc and the result is supported with literature [
16,
18]. However, Abdula-Rahaman and Abdulai [
45] revealed that average sex is significantly influence farmers value chain participation decision. Similarly, Song and Chang [
46] found that average education of network members positively influences the frequency of health information seeking. This suggests that the effect of exogenous characters’ of social networks varies depending on the structure of networks and characteristics of innovation under study.
The result of the study reveals that all the average exogenous characteristics of the farmers’ peers were not statistically significant implying absence of contextual effects. The result also reveals that the number of links out of the random matches within and outside the village and location of the study were statistically insignificant. This implies that no evidence of correlated network effects. Songsermsawas et al. [
31] reported that geographical closeness is not a good proxy for the social networks, and the caste-based networks produce peer effects.
All household-level characteristics such as gender, age, livestock ownership, land holding size, and education level of household head do not demonstrate associated with the likelihood of participation decision in FBSc. Similar result are found by Abdula-Rahaman and Abdulai [
45] who report individual characters’ of household did not influence participation in value chain. Mokenon et al. [
47] reported most household characters’ except education and land holding size have no association with adoption of row planting in Ethiopia. Under changing climate the effect of household-level characteristics on farmers’ participation behavior varies substantially depending on nature and characteristics of collective action. Similarly, the exogenous effects of social networks on varietal adoption are not significant for the peers’ individual characteristics, but jointly these characteristics are significant and enhance the endogenous effects of social networks [
18].
Social network size: Size of social network is positively and significantly related to farmers’ participation in FBSc which means that household having larger size of social network is more likely to participate in FBSc (Beta = 0.2529,
p < 0.05) than others. If household network sizes increase by a unit, the probability of participation in FBSc would increase by 0.89% keeping other variable constant (
Table 7). Social network size is an indicator of social resources in which farmers’ participation decision to FBSc varies with size of social network. Household who have more links have diverse sources of information thereby increases the trust on the information. Diverse source of information increases farmer level of awareness about social /institutional innovation such as FBSc which can enhance the participation of farmers. A household who has linkage with many number of actors are socially more integrated than someone who has few linkages [
48]. Similarly, the effect of social networks on the adoption of technologies varies depending on the size of the network, complexity of technology and socio-economic condition [
18,
49]. Mokenen et al. [
16], in their study used rigorous econometric techniques and further confirmed that technology adoption is positively influenced by the size of the social network with which information is exchanged. Our findings emphasize that the effect of social networks varies depending on their structure and the characteristics of the innovations considered.
Social network with relatives: Network with relatives is positively and significantly related to farmers’ participation in FBSc which means that farmers who have contact with relatives of their network out of match have a higher likelihood of participating in FBSc (Beta = 0.8665,
p < 0.05). Relational ties between family members/relatives and friends often viewed as bonding and strong ties. It suggest that having link with relatives increases the likelihood of participating in FBSc by 43.7% as compared to who have link with non-relatives keeping other variables constant. Homophily based on same socio-economic and demographic characteristics creates strong and bonding ties [
48,
49] where structural characters of social networks support the flow of information on collective action.
The type of network matters in illustrating its effect on farmers’ participation in agricultural innovation practices such as quality seed production through cooperatives. Our finding shows that network with relatives out of match is viewed as a denser network in which continuous exchange of information and knowledge, and hence, is considered as strong link. According to Molina and Martinez [
32] strong tie has the advantage of getting high-quality of information and creating strong social norms as well as sanctions generated in this process. Farmers’ participation in FBSc is not a simple agricultural practice in which information from multiple sources and strong tie reinforces farmers to join FBSc. Similarly, in Ethiopia studies by Todo et al. [
15] highlight that adoption of complex technologies requires strong social tie.
Frequency of extension contact: Frequency of extension contact with extension agent is positively and significantly related to farmers’ participation in FBSc this implies that a farmer who have higher frequent of contact to extension agent is more likely to participate in FBSc. The result is statistically significant at significant level of 0.001. Frequency of interaction is a key feature of social networks [
50] and is a component of the betweenness centrality measure. If the frequency of extension contact increase by one unit, the probability of farmers’ households’ participation in FBSc would increase by 6.2%, keeping other variables constant (
Table 7). Extension agents and local administrators’ are heterophilic actors who spread the information vertically through weak bridging ties. Weak ties are formed by dissimilar groups either within or outside their groups’ members which serve as bridge tie. Extension agent is among the actors from formal institution that promote innovative practices such as farmer-based seed production. Farmers’ will be more exposed to be influenced from those that they believe to be a realm expert and with whom they feel a strong measure of trust. University, research centers and often employ public extension agent and local administrators in seed multiplication. For instance, practical demonstration of new crops and/or varieties for adaptation trial (e.g., FRG, cluster-based community seed multiplication) that involve farmers has been promoted by public extension agents and local administrators’. Official information obtained from extension agents may help to lessen risks, uncertainties, and distorted information and thereby play a key role in increasing cooperative actions [
39]. Frequency of contact with extension agent have effect on participation in FBSc in which information from reliable extension agent can be trustworthy and is more likely to be absorbed and implemented by farmers’. Todo et al. [
15] demonstrated that adoption of complex agricultural knowledge in low developing countries requires strong external ties and flows of the same information from multiple sources.
Access to off-farm income: Access to off-farm income is positively and significantly related to farmers’ participation in FBSc (Beta = 1.357,
p < 0.001). If sample household had the probability of participating in FBSc would increase by 5.7% compared to those household who lack access to off-farm income keeping other variables constant (
Table 7). Farmer household who have off-farm income gating the purchasing power of input needed for seed production. Quality seed production through seed-producers cooperatives requires proper supplementary inputs such as basic seed, fertilizer and chemicals for herbicides. Thus, income obtained from off-farm can be re-invested to purchase supplementary input which enhance farmers’ desire to join FBSc. Similarly, recent empirical evidence identified that the financial resources of off-farm income determine farmers’ participation in agricultural cooperatives [
42,
51] if farmers’ face legal constraints or additional investments related to agricultural production. However, other studies finding stated that off-farm income reduce the likelihood of farmers’ participation to seed production. For example, Rubyogo et al. [
52] and Tebeka et al. [
8] revealed that household heads who have access off-farm income opportunity might face high opportunity cost of time when obliged to attend training and meetings on seed production techniques supported by the project. This might be true if the opportunity of off-farm income is higher than farm income.
Perceived profit: Farmers who perceive the higher benefit of seed than grain is less willing to participate in FBSc (Beta = −0.514,
p < 0.05). If sample household perceive higher profit of seed than grain, the probability of participating in FBSc decrease by 15.3% as compared to those households who don’t perceive higher profit keeping other variables constant. All FBSc members are liable to all costs of climate related production risks and rejections by seed inspectors. In addition the limited the availability in type, amount, and high costs of basic seed minimize profit margin. In climate ‘Hot-Spot’ areas such as Hararghe smallholder farmers’ mainly focuses on risk aversion than opting for profit. As observed by Teferi [
53], and Tebeka et al. [
8] resource constrained farmers are generally driven by food security objectives and might not respond to profitable opportunities. Thus, weather-indexed seed insurance is serving as coping strategy and helping seed producers’ farmers’ vulnerability who are totally rainfall dependent and thereby ensures the sustainability of seed business. In fact, the success of weather-indexed seed insurance services dependent on effective climate information services. The adoption of weather-index seed insurance also further support climate information services and strengthens the work of adaptation strategies. According to news story of ISSD and Benefit project of Haramaya University, in Ethiopia in the year 2014 immense decline of seed production trends for seed producers’ cooperatives due to El. The use of climate information services drives the adoption behavior of farmers for climate-smart agricultural practices [
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40] (where FBSc is also cited as climate-smart agricultural practices.