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Fishers at Night, Seaweed Farmers by the Day: Determinants of Livelihood Diversification among Marine Fisher Communities of Kwale County, Kenya

In this paper, we assess the determinants of livelihood diversification among marine fisher communities of Kwale County in Kenya; the typologies of diversification and potential viability; and uptake of alternative livelihood sources that can cushion the households against environmental, social and economic vulnerabilities. We model the determinants of diversification using multiple linear regression together with Simpson Index of Diversity, for the analysis. A low level of diversification is reported among the marine fisher community but with a willingness and readiness for diversification to supplemental and enhanced livelihoods. Six predictor variables (Gender of Household Head, Training on livelihood, HH dependency ratio, Social Assets Value Index, Financial Assets Value Index, Membership to social organization and Fishermen attitude and identity) were found to be significantly and strongly associated with the dependent variable (extent of livelihood diversification). Contrary to other research findings, age, education, land holding size and marital status were found not significantly associated with level of diversification despite consistently being significant for related studies in agrarian communities. In consideration of these findings, the study recommends prioritization of non-fishing livelihood sources such as seaweed farming, apiculture, mangrove planting, small business enterprises among others.

contributing to growing pressure on marine resources and fish-based livelihoods. Thus, growing numbers of fishers" livelihoods are being increasingly squeezed in a vicious circle that signals an urgent need for livelihood diversification in fishing communities (Gordon and Pulis, 2010).
The diminishing fish catch increases the vulnerabilities that fisher communities are exposed to (particularly illness) and a lack of access to basic social services including health, education and sources of affordable credit (Okeyo, 2010). Understanding the relationship between fishing and livelihood diversification is important because fishing is an important component of rural livelihoods of households in the coastal areas. Diversification potentially increases the adaptive options of households and therefore places households in good position during periods of livelihood stress ensuring further that fishers who diversify reduce fishing pressure, which may contribute to long-term sustainability of the social-ecological system (Blythe et al., 2014).
This study sought to provide an understanding of the key determinants of livelihood diversification with the following specific objectives: a) explore the determinants of livelihood diversification among marine fisher communities, b) determine the various typologies of diversification, and c) assess potential viability and adoption of emerging alternative livelihood sources.

Statement of the problem
Livelihood diversification studies have primarily focused on farm households and pastoralists but with little attention to fisher households (Hoowerg et al., 2009) and much less focus to its effect and influence on marine fisher coastal communities yet it has been identified as one goal of sustainable fisheries and coastal sustainability initiatives, with both ecosystem and social benefits (Alden, 2011). Livelihood diversification in fisher communities is also viewed as a plausible solution for fishermen to cope with new constraints as it is a "away to resist in front of a real or perceived degrading economic and environmental context" (Henichart et al, 2010).
Dwindling fish stocks in the coastal waters and signs of overfishing of high value species in the territorial waters may already be predisposing the communities to deprivation occasioned by degradation of marine resources and declining fish catches (Luc, 2017;Hoorweg et al., 2009). Of interest, is the fact that fishers have stuck to the traditional forms of livelihood strategies yet alternative livelihood options and diverse income opportunities allow communities to be flexible to adapt to social, political, and economic changes (McLeod et al., 2006). Accordingly, the determinants of livelihood diversification among fisher communities is less understood including how diversification strategies influence fishing activities and, ultimately, pressure on fisheries resources (Brugère et al., 2008). This study thus, examined the various forms and typologies of livelihood diversification, the determinants of diversification and potential for diversification of alternative livelihood options for the fisher folk.

Theoretical framework
The study adopted the sustainable livelihood framework (UK Department for International Development). The framework identifies five integrated and interrelated asset types. The five assets encompass the financial, physical, social, human, and natural. An individual actor, in our case a fisher, may own or acquire access to a particular set of assets. The combination of assets is determined by the context in which the fisher lives (Parkinson and Ramirez, 2006;Bennet, 2010). The three key components to the livelihood framework are capabilities, assets and activities (De Satge, 2002). The framework ( Figure 1) has been shown to give a comprehensive and practically focused understanding of fishers" realities, which can then subsequently inform development initiatives and policy documentation (Baumann, 2002).

Materials and methods
The study adopted a descriptive and analytical cross-sectional survey design based on mixed methods. The design has been hailed for its effectiveness in ensuring internal reliability and corroboration of facts and opinions (Brannen, 2005;Graff, 2017) and triangulation of sources. A multi-stage stratified cluster sampling was adopted. Quantitative data was collected from randomly sampled 346 households using a structured survey questionnaire while qualitative data was collected from fifteen (15) purposively selected key informants and nine (9) focused group discussion sessions that reached 714 males and 50 female respondents.
The extent of diversification of the households was measured using Simpson Index of Diversification (SID) preferred because of its computational simplicity, robustness and wider applicability (Khatun, 2012) and also for the fact that it takes into consideration both the number of income sources as well how evenly the distributions of the income between the different sources are (Minot et al., 2006;Joshi et al., 2003). Inferential statistics was undertaken using Analysis of variance (ANOVA), regression and correlation analysis as well as student T-test at a significance level of 95%. Qualitative analysis was performed using content/thematic analysisan analytic process that uses a theory-driven approach where the analysis categories are determined a priori based on the study objectives and identifying the common themes around which the analysis should be carried out.

The study sites
Kwale County is one of the six counties in the coastal region of Kenya. It borders Taita Taveta

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Journal of Agriculture and Environmental Sciences, Vol. 10, No. 1, June 2021 The main types of fish include Rabbit Fish, scavengers, snappers, parrot fish, octopus, squids and variety of ornamental fish. In addition, there are 338 fish ponds in the county (Government of Kwale, 2018).

3.1The extent of livelihood diversification
This study employed the Simpsons Index of Diversity (SID) to determine the degree of income diversification among the coastal marine communities of Kwale in Kenya. The preference for this index was based on the advantages it portends as realised by those researchers that have used it (Shaha et al., 2010;Babatunde et al., 2009 andJoshi et al., 2003) and for its "computational simplicity, robustness and wider applicability" (Ahmed et al., 2018). The index takes into consideration both the number of income sources as well how evenly the distributions of the income between the different sources are (Minot et al., 2006;Joshi et al., 2003). The formula for Simpson index is given as: Where: N is the total number of income sources Pi represents income proportion of the i-th income source.
The index value lies between 0 and 1. The value of the index is zero when there is a complete specialization and approaches one as the level of diversification increases. The more the SID value is closer to one, the more diversified the household income is. The index"s value is zero if there is just one source of income. As the number of sources increase, the shares (Pi) decline, as does the sum of the squared shares, so that SID approaches to 1. Households with most diversified income sources have the largest SID value, and the least diversified income sources have the smallest SID value. SID gets better with evenness of income sources.
The index was computed for all the households and comparison made against household characteristics including education level, household size etc. The levels of diversification as a result of the SID adopted the categorization by (Ahmed et al., 2018) which categorises livelihood diversification as follows: Where, D is the dependent variable, representing Livelihood Diversification Index, explained by βi which represents a vector of parameters, and Xi is a vector of exogenous explanatory variables. The explanatory variables for analysis were selected based on a literature review Sahal and Bahal, 2014;Bahal, 2010 andAhmed et al., 2018) which includes the sex of the household head, age, marital status, education level of the household head, household size, land size, total asset value of the household, access to credit and family borrowings, involvement in social safety nets among others.
The study identified 15 possible livelihood sources that were available for adoption in the marine communities of Kwale County. The households were subsequently asked which livelihood options they practised and estimated share contribution for each source of livelihood documented. The total number of livelihood sources practised by the household and the share contribution or each source of livelihood to the household income was used to compute the Simpson Diversification index (SDI) for the household.
Of the fifteen livelihood sources selected by the 346 respondents (see Table 1), agriculture sector received the highest number of cases at 147, followed by fish and shell fish sector (136), followed by small businesses (74) and salaries and wages at 30 to complete the top five most preferred sources of income for the residents of Kwale. The least preferred was mariculture reporting only one case followed by mining and quarrying (5 cases). Among the emerging sources of livelihood, sea weed farming received the highest cases at 18 indicating its steady rise and acceptance by the coastal marine communities. The transport, storage and communication sector with 27 cases is also steadily on the rise mainly as a result of the use of motorbikes popularly known as the boda to the locals. Agro-coconut related (All production related to coconuts including palm wine) 34 8 Tourism sector (eco-tourism activities, tour guiding, beach boys, hotels, crafts and carvings, jewelry etc.) 11 9 Service and manufacturing & cottage industry (Bicycle repairers, Black smiths, Builders, Dress making, Mechanics).

Figure 2: Percentage cases of households and sources of livelihood
The average number of income-generating activities or sources per household for the 346 households was 1.62 (SD=0.804). The minimum number of sources identified was one and the maximum was five. In total, more than half of the households, 187 corresponding to 54%, were engaged only in one source of livelihood; 114 (32.9%) were engaged in two sources; 35 (10.1%) were engaged in three sources; 8 (2.3%) were engaged in four sources; and only 2 (0.6%) were engaged in five sources of livelihood. Using Ahmed et al., (2018) classification, the number of livelihood sources was determined to be significantly correlated with the level of diversification with 99.5% of the respondents reported at the level of "No diversification" engaged in only one source of income and those with high level of diversification were engaged 100% in five different sources of income as shown in Table  2. It was established that women were more specialised than men in the extent of diversification.  The study showed that only 52.6% indicated that they did not make extra income from other sources apart from their current main source while 47.4% diversified their income sources. Based on the calculated SID values (See Table 4) and the categorisation adopted by Ahmed et al., (2018), the study established that more than half of the respondents (54.3%) of the 346 households interviewed had no diversification. In addition, 11.6% had low level of diversification, 27.5% had medium level of diversification, and a paltry 6.6% had a high level of diversification. These results showed that most of the households are still stuck to the traditional sources of livelihood, mainly fishing and agriculture and small businesses. Only a very small fraction of the households is presently engaged in multiple livelihood sources, and even then, those that have diversified livelihoods have a large contribution share by one major source mainly agriculture and fishing.

3.2Extent of diversification based on SID values
It was established that upstream communities were mainly engaged in mixed farming while the shoreline communities were predominantly engaged in fishing and fish-based livelihoods. In the fishing communities, men were mainly engaged in fish harvesting while women were in fish processing. In the upstream communities both men and women were engaged in farming but with diversified livelihoods in small businesses and other activities in mining, motor bike riding etc. Discussions with fishermen and adult men and women in the communities showed that in recent times there have been new sources of livelihood such as seaweed farming, apiculture and carbon trading championed by other agencies such as Plan International and German donor organizations managed by Kenya Marine and Fisheries Research Institute (KEMFRI).
Linear regression model was used to identify factors which influence or condition household"s livelihood diversification strategies. These factors were theorised and modelled from various variables based on literature review to try and understand how they predict livelihood diversification. Thirteen explanatory variables were identified, and regression analysis conducted with the level of diversification (computed as SID) being the dependent variable.
A total of 16 explanatory variables were included in the model to establish the determinants of diversification among the marine communities of Kwale County. The variables included: Number of HH working members; Gender of Household Head; Age of HH head in years; Marital status of respondent; Household Size; Access to credit (loans and borrowings); Training on livelihood; Land unit held in Ha; HH dependency ratio; Social Assets Value Index; Physical assets value Index; Public assets index value; Financial Assets Value Index; Human Assets Value Index; Membership to social organisation and Fishermen attitude and identity. Table 5 explains the explanatory variables. Fishermen attitude and identity index A single value derived from a set of 12, 5-point Likert scale questions in which 1= Strongly Disagree, 2=Disagree, 3=Neither Agree nor disagree, 4=Agree and 5=Strongly Agree Upon putting through the 16 explanatory variables into the model, the results showed that the overall summary F-Test for the model was significant (P≤0.05) and that the R-squared value of 0.180 was recorded. This means that 18% variation in the dependent variable (extent of livelihood diversification) was explained by the explanatory variables modelled in the study. Following the results derived from the model, it was established that some factors were significantly associated with the extent of diversification of livelihoods while other had no significant association. The results of the coefficients tallied in Table 6 showed that out of the 16 explanatory predictor variables put in the model, six (6) were found to be significantly and strongly associated with the dependent variable (extent of livelihood diversification). They included: (Gender of Household Head, Training on livelihood, HH dependency ratio, Social Assets Value Index, Financial Assets Value Index, Membership to social organization and Fishermen attitude and identity). The other 10 variables were not significantly different from the dependent variable (extent of diversification). The regression output indicated that there are certain household level variables (demographic and socioeconomic) affecting the extent of livelihood diversification among marine communities. The size of household, access credit (loans and borrowings) and marital status was not significantly associated with diversification. The finding on the household size contrasts that of Mphande (2016) that a big family needs more resources for sustenance than a small family and hence "people with big families will venture into as many ways as possible to gain the required resources to support their families." These results are however consistent with related findings by Naznin et al., (2015) that concluded that household size, access to loan and marital status do not affect income diversification. The findings on household size however differs with other findings (Tizale, 2007;Bryceson, 2002) that found household size to affect significantly diversification of households. They argued that the chance of a household diversifying would increase with the size of the household. These variations can however be seen to vary from marine to agrarian based communities whose primary focus is on water resource and not land based resources.
The household size which measured the total number of people in the household was thought to have a positive significant association with diversification such as findings by Mentamo and Geda (2016) that showed that "A unit change in households" size brings about 0. 009 units and 0.07 unit change in the dependent variable (livelihood diversification)" but this turned out not to be a significant factor in influencing livelihood diversification.

Extent of diversification and gender
The study considered the influence of gender of the household head on diversification and established it was positively associated with the livelihood diversification of marine communities. The male headed households were hypothesised to be positively related to higher levels of diversification compared to female headed households who suffer gender-based restriction and unequal power balances especially in fishing communities where men control fishing resources. The findings are consistent with similar related studies such as Dirribsa and Tassew, 2015;Debele and Desta, 2016) who for instance noted that gender significantly influenced livelihoods with a difference at 1% level, between the mean number of livelihood activities engaged in by men and women.
A cross tabulation between the gender of the household head and the level of diversification reported a significant level of difference between the males and females X 2 (p≥05=013). Comparatively, 47.6% of the females reported "No diversification" compared to 59.2% of the females with the results suggesting that females were most likely to be specialized in sources of income than the men who are more diversified.

Extent of diversification and age of the household head
A further analysis on age as an explanatory variable showed that it was not significantly associated with the level of diversification. This is despite the studies such as that of Mariotti et al., (2014) that showed that as age increases, and the household heads cross the turning point of approximately 60 years, it is less likely that the households would choose to have diversified livelihoods. It is however in consonance with FAO (2016) study that showed that young people migrating outside the communities, have possibilities of diversification than the ageing indicating that "Young men also tend to be more mobile, which may enhance their opportunities for income diversification" and still is counter findings that suggested that household head"s age is the prime force towards livelihood diversification (Khatun and Roy, 2012;Ellis, 2005).
In the focus group discussion with fishermen they indicated that some level of diversification is only possible with a certain age. One participant observed, "We know of very few people who have stopped fishing and completely shifted into other forms of livelihood e.g. herbalists, small businesses, Base Titanium Factory as drivers and wage and salaried employment; but it changes with age and time; so that while one is still young he would prefer being a fisherman while with age one prefers being a fish monger or get alternative livelihood to fishing." (Male Participant FGD, Gazi)

Education of household and extent of diversification
Contrary to findings by Yizengaw et al. (2015) and Khatun & Roy (2012) that showed that educated household heads are, more likely to have diversified activities with an escalated probability of engagement in livelihood diversification, this study established no significant association. The finding is possibly due to education levels of the coastal communities in Kenya remaining relatively low and even lower among the fisher folk. The findings contrast those of agrarian communities where education is invariably associated to diversification as farmers that acquire new knowledge on agricultural techniques get more likely to diversify (with Oluwatayo, 2009;Ng"ang"a et al., 2011). Debele and Desta (2016) observed in their study in Ethiopia that "that an increase in education level of head will increase the likelihood of being in highly and moderately diversified compared to the probability of being in less diversified strategy." Presumably, education is the key to literacy. If an individual is able to read and write, they have a higher chance of choosing an appropriate field of work or further skills training in order to advance their livelihoods. The Ministry of education official exemplified the role of education during the interview, "education standards determine level of knowledge and skills as well as attitudes, which together translate to livelihoods activities; In addition, proper livelihoods give good support to education e.g. farming and fishing in the county which has a direct influence on education standards."

Extent of diversification and marital status
There was also no statistically significant association established between household extent of diversification and the marital status of the household head. This finding differs with similar findings in farmbased livelihoods that have established a significant positive association between marital status and extent of diversification (Olale et al.,2010).

Social assets, memberships and extent of diversification
The study established a positive association between diversification and assets especially the social assets and financial assets all which were established as significantly affecting livelihood diversification. The findings mirror closely those of Mariotti et al., (2014) who found that "assets owned have a positive impact on whether households diversified their livelihoods to earn more income." Separately but related to this, was the positive association reported between the level of diversification and membership to social organisation. It has been noted that social networks seem to enable household members to extend their participation to new activities (Stefan and Manfred, 2005) and that "these networks are beneficial in obtaining knowledge that can be used to further livelihoods" (Mphande, 2016).
Further to this, participation in social groups outside the fishing sector can lead to learning and acquisition of new knowledge on emerging livelihood strategies and provide a great sense of security to try out new livelihood strategies (Rhona and Susana (2011). A cross tabulation of household membership to a social organisation in the community such as village savings and loaning associations, resource conservation groups among others and extent of diversification showed a positive significant correlation (X 2 =00). Of the 346 households, 61.9% of those who were not members of any group had "no diversification" compared to 40.7% of those who were members. Additionally, more households (42.3%) reported either medium or high level of diversification compared to 29.6% of those who were not members. Conclusively, being in a group is associated with significant positive association with diversification with those who are in a social grouping likely to diversify than those who are not.

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Journal of Agriculture and Environmental Sciences, Vol. 10, No. 1, June 2021 Further analysis showed a cross tabulation of the extent of diversification and the number of times a household was able to meet other household"s members in the community and interact with them in the three months preceding the study reported a significant positive correlation (X 2 =013). Based on the findings, households that never had any social interactions had 69.6% "No diversification", compared to 49.3% for those who had interactions more than three times, 45.5% for three times, 53.8% for two times and 50% for once. Medium level of diversification and high level of diversification is reported as number of times of interaction in the community increases clearly showing the importance of social assets as a factor in the marine community livelihood diversification. The findings are shown in Table 7.

Extent of diversification and total household asset value
Even though some of the asset elements like human assets and public assets did not report significant positive association, collectively as a measure of Total asset index they strongly positively affected the level of diversification indicating that assets are an important factor in diversification of livelihoods among marine communities. The attitude and identity of the fishermen equally played an important role with those fishermen with a negative attitude and identity more likely to report diversification than those with a positive attitude. Fishermen who considered their quality of life as poor and status as low were more likely to diversify.

Extent of diversification and household dependency ratio
The other explanatory variable examined was the dependency ratio which measures the ability of the household to sustain and meet their needs. It was observed that increase in the dependency ratio puts more pressure on the household and decreases the ability of the household to meet their needs. An increased dependency ratio arguably pushes the household into diversifying into other activities that can bring more income to the household (Khatun and Roy, 2012). Contrary to this finding, the study established no positive significant association between livelihood diversification and dependency ratio of the households which nonetheless was established to be very high among marine communities. Separately, access to credit and borrowings was equally not found to have a significant influence on diversification. This is so with farm-based study by Ahmed (2018) that showed that "households having more amount of credit are likely to be more diversified in their livelihood activates" as well as other related studies (Babatunde, 2009;Asmah, 2011 andSaha et al., 2010;Oluwatayo,2009).

Extent of diversification and household training skills on new livelihood sources
The other positively associated explanatory variable was training which enabled the communities to be adequately empowered to make deliberate choices to diversify their livelihoods. According to Mphande (2016), "the more the skills in a household, the more they can venture into different markets thereby widening their ability to make money and support their family." Additionally, there was no positive significant association between diversification and land holding despite the fact that significant proportion being farmers (P=0.95). A cross tabulation of occupation practised by the respondents and levels of diversification was not significant. However, there were variations reported for instance 55.70% of those engaged in agriculture had no diversification compared to 58% of those engaged in fishing. The highest level of specialization was reported among those undertaking merchant and trade business (66.7%) and with the religious leaders reporting the highest level of diversification.

The barriers to livelihood diversification
It was established in the study that the main reason why the communities are still sticking to their current main source of livelihood despite their dwindling fortunes is because of lack of viable alternatives for which they have capacity to undertake (58.4%), followed by lack of adequate skills to engage in the emerging livelihoods sources thus limiting them to what they are used to doing with skills transferred to them from one generation to the other (30.6%), deep seated belief that the livelihood source was bequeathed to them by their previous ancestral generation and that they are morally obligated to stick to it (13.4%), unfavorable and changing climatic patterns that causes seasonality in some livelihood sources such as agriculture which relies on the rain (10.5%) and unpredictability of the income sources (6.2%). (See Table 8). In the focus group discussion with adult fishermen at Gazi, participants noted that whereas they saw the need to get out of fishing because of its dwindling returns, they were yet to get alternatives and even so they also lack adequate skills and knowledge on what to do with emerging livelihoods like sea weed farming and aquaculture. The fishermen were of the opinion that fishing should be enhanced by providing them with recommended gears that are environmentally sustainable and which will not put them through legal conflicts with law enforcement authorities as they presently do. They also indicated the alternatives need be those that they have technical capacity to undertake.

Typologies of diversification
Having established that there are emerging alternative livelihoods apart from the mainstream traditional livelihoods of farming and fishing, the study sought to determine if the alternatives were culturally acceptable, economically viable, socially viable and environmentally sustainable. The study sought to determine the level of acceptability of the emerging livelihoods and whether the communities were willing to trade them off for their current livelihood strategies or continue doing them alongside their mainstream livelihoods. The importance of these emerging livelihoods has been recognised by 42.2% of the respondents. Apart from the mainstream livelihood sources the other emerging sources include boda, small scale businesses, charcoal burning, mangrove farming, sea weed farming and quarrying.
In trying to understand if the fisher folk would leave their fish-based livelihood for the emerging livelihood strategies, the study established that 69.5% (N=118) of respondents were willing to leave fishing and turn to other sources of livelihood if any case there arise an opportunity. The findings are concomitant to those of Versleijen (2001) that found out that more than half of the fishermen whose fathers were fishers (54.6%) were willing to stop fishing if another job was offered to them with the rest (45.5%) not willing to stop.
Despite the willingness to diversify, 30.5% would still hold on fishing even if there emerged another source of livelihood. The choice over fishing instead of other alternatives was discussed in the focus group discussions where it was observed that fishing still remained a favourite because of lack of alternatives. At Gazi, a woman in adult FGD observed thus, "…We don"t like fishing but there are no alternatives. It is also cultural, fishing is a lifestyle so we shall leave but finally go back to it or do it as part time job." This is what Nayak, (2017) called a "mental block" in reference to fisher households who consider fishing as a caste or cultural activity, a way of life, rather than an economic pursuit which he argued complicates livelihood choices further in terms of people moving out of fishing to non-fishing activities. The discussions however revealed that there are new livelihoods such as sea weed farming that are increasingly getting embraced by the marine communities.
The study equally sought to establish some of the reasons why the communities would continue sticking to their main source of livelihood even with the knowledge that the returns on fishing are declining and may not be sustainable to the families.
The analysis showed that 26.2% (N=168) prefer the identified livelihood due to its economical nature while 19%% of the respondents are expert in the identified areas so they would rather stick with what they know how to do best. In addition, 4.2% hold that the identified are not capital intensive and so easy to start. Other reasons mentioned for preference of the main source of livelihood over the others are; climatic factors, genderbased factors, promotion by the government and agencies among other reasons. These are shown in Figure 4. The study also established that some of the factors that push families towards diversification are increased family demands, decline in productivity and climatic changes. These are reported in Figure 5.  Three sets of livelihood types were investigated, that is enhanced livelihoods in which the household decides to add value to ongoing traditional or historically associated mainstream livelihood, diversified/Supplemental livelihood which includes elements of enhancing existing livelihoods and adopting "supplemental" strategies (making current practices more sustainable) and alternative livelihoods that involves completely changing occupations and is often for those involved (Pomeroy et al., 2005). Based on this dichotomy, the study established that 29.7% cases indicated having switched completely to an alternative source of livelihood, and 69.5% kept doing their main source of livelihood even as they engaged in the other alternative source (supplemental livelihood). These findings were corroborated in focus group discussion with beach management members who indicated that supplemental livelihoods were their most preferred. A member observed thus: We know that fishing as a livelihood has a lot of challenges and we are not happy engaging in it as our main source of livelihood. However, we do know there is no alternative job and or employment for us; it is an easier way of starting up and a gateway to other alternative forms of livelihoods. If there are other sustainable sources we would be willing to take them up as supplementary livelihoods─ Adult Male FGD,Beach Management Unit,Gazi Further,22.9% engaged in their main livelihood but on seasons when it became unsustainable either due to seasonal changes such as climate factors or declined productivity they switched to an alternative source but returned soon after the main source stabilised (enhanced livelihood). Supporting this view, is an observation in the focus group discussion where the fishermen observed that, fishing has declined because of too many fishermen, poor equipment, Inferior fishing gears, and lack of equipment for deep sea fishing restricting them to shallow waters with less fish catches. They indicated that their current fishing livelihood requires enhancement with better fishing gears that are recommended and licensed and which are environmentally friendly.
The fishermen continued to explain that they are sticking to fishing only because they lack alternatives. They also recognise fishing as an old-time cultural inheritance which they would find difficult to completely shift from. One male participant in the Gazi FGD explained, "Fishing is a cultural part of us and a lifestyle, it is not just a source of livelihood so we shall leave it but finally we shall go back to it or do it as part time job." Conclusively thus, the majority of the fishing community would rather continue engaging in fishing or their main source of livelihood supplementing it with other sources. This supports the view of Campbell (2008), that opines that a change from one livelihood to another is not always the only way forward and that enhancing existing livelihoods also has a role to play and can facilitate a more profitable livelihood and reduce the need to engage in destructive activities. It also confirms the position of Pollnac et al., (2001) that "providing or facilitating uptake of alternative livelihood activities may not necessarily cause fishers to leave a fishery", and that "addition rather than substitution of activities may take place." It also gives credence to the doubts cast by Hoof and Nathalie (2017) that fishers would be least likely to abandon fishing completely if they found other employment and were more likely to combine the two.

3.5Readiness for diversification
The study sought to determine how willing the households were to consider an alternative livelihood source. It was realised that 38.4% were strongly willing to consider other sources of livelihood, while 49.1% were willing with only 13% not willing to diversify and instead stick to their current source of livelihood. The study therefore established that majority of the community members are willing to consider other sources of livelihoods if well addressed thus will be able to progress economically and socially.

Conclusion and recommendation
A low level of diversification is reported among the fisher communities in Kwale County. Six (6) variables are found to be significantly and strongly associated with the dependent variable, (extent of livelihood diversification). They include: (Gender of Household Head, Training on livelihood, Household dependency ratio, Social Assets Value Index, Financial Assets Value Index, Membership to social organization and Fisher folk attitude and identity). The study finds no significant association between age and level of diversification. The level of education is equally found not significantly associated with level of diversification contrary to related studies in farm-based livelihoods. In line with this finding, it is advised that programs that encourage diversification among fisher households need to focus on the identified determinants and tailor make their programs while considering these key determinants if they are to succeed in influencing the fisher households towards livelihood diversification. There is also high willingness to turn to alternative livelihoods by the fisher folk with much focus on supplemental livelihoods as opposed to alternative livelihoods.
Given that most fisher households combine fishing and non-fishing strategies; livelihood intervention programmes should prioritize improvement of the non-fishing activities. Understandably, this will take away some pressure from the fish stock by allowing the fish stock to regenerate. In consideration of these findings, the study recommends prioritization of non-fishing livelihood sources such as seaweed farming, apiculture, mangrove planting, small business enterprises among others. To be successful, however, support in the form of credit and training must go together if alternative income sources of the house-holds are to be relied upon.