Predicting uptake of aquaculture technologies among smallholder fish farmers in Kenya
In Africa, many governments and development agencies have promoted aquaculture as a panacea for household food security, rural development, and poverty reduction. However, aquaculture production in the continent remains low despite significant investments in research and technology development. While numerous initiatives have been directed at technological innovation and transfer, their present scale of uptake is very slow and therefore inadequate to achieve transformational change envisaged in the 2030 Agenda for sustainable development. In this paper, we aim to (1) critically analyze the factors that influence fish farmer’s perceptions, attitudes, and behaviors toward technology adoption; and (2) to determine the impacts of technology adoption on farmer’s livelihoods. Primary data were collected using a self-administered digitized questionnaire to 331 randomly selected farmers in Kenya. Multivariate logistic regression models were used to analyze data. Results revealed that variables including secondary education, diversified on-farm activities, farm size, production levels, attendance of extension training, ease of understanding, and ease of handling technologies were positive and significant predictors of aquaculture technology adoption. However, 30% of fish farmers were categorized as high adopters of novel aquaculture technologies, implying that there are gaps in technical skills hindering adoption of innovative technologies and best management practices. To facilitate learning and uptake of technologies and good practices by farmers, a range of aquaculture-related extension and communication materials, including posters, hard copy information leaflets and brochures of recipes in appropriate languages, short video presentations, and radio features, should be commissioned to support the smallholder farmers.
KeywordsAquaculture Technology adoption Sustainable livelihoods Smallholder fish farmers Kenya
Globally, aquaculture production has doubled every decade for the past 50 years (Bostock and Seixas 2015). As the world’s fastest growing agrifood production sector, aquaculture has become the predominant source of fish protein, surpassing the amount of fish produced for direct human consumption from wild-caught fisheries (Golden et al. 2017; Toufique and Belton 2014). Aquaculture currently contributes 47% of global fish production and is growing rapidly, with 5.8% annual growth rate during the period 2001–2016, but it no longer enjoys the high annual growth rates (average 8.8%) experienced in the 1980s and 1990s (FAO 2016, 2018). With the rapid expansion of aquaculture in the past three decades, the industry has experienced boom and bust cycles and received negative reputation for its associated environmental impacts (Waite et al. 2014). Given the increasing scarcity of several aquaculture inputs, e.g., land, freshwater, and energy which are associated with significant environmental impacts, there have been calls for sustainable intensification (SI), broadly defined as “producing more using less” (Henriksson et al. 2018) to improve aquaculture’s productivity and environmental performance (Waite et al. 2014).
The rapid growth of the aquaculture industry has been enabled through the expansion of aquaculture production areas (Oyinlola et al. 2018), intensification of production systems (Joffre et al. 2017), adoption of new technologies, and systematic improvement of existing technologies that brought control over husbandry and production processes (Kumar and Engle 2016; Henriksson et al. 2018; Kumar et al. 2018). Aquaculture offers great scope for technical innovation to further increase animal protein supply and resource efficiency (Waite et al. 2014). In the past 5 decades, technological advances in production and breeding systems, feeds and nutrition technology, vaccines, species and strain selection, reproductive control, mechanical aeration, and water exchange and non-technological innovations including improved regulatory frameworks, market, and certification standards, among others, have enabled the growth of aquaculture sector (Kumar and Engle 2016; Joffre et al. 2017). Recent studies indicate that investments in new production systems, management practices, and new products result in substantial benefits to producers and consumers (Kumar and Engle 2016; Kumar et al. 2018). However, there is still incoherent understanding of technological change in aquaculture development in Africa.
Although Africa has the fastest growing aquaculture industry with high biophysical potential, the sector has not yet significantly contributed to sustainable food supplies and economic development (Brummett et al. 2008; Waite et al. 2014). Aquaculture accounted for 17% of total fish production in Africa, while contributing a paltry 2.5% to global production (Chan et al., 2019; FAO 2017, 2018; Obiero et al. 2019a). Like the rest of Sub-Saharan Africa, aquaculture development in East Africa is constrained by lack of good-quality seed and feed, low technical capacity, poor market and value addition, inadequate extension services and materials, poor management of culture systems, low capacity in disease diagnostics and biosecurity, and increasing competition from cheaper imported fish products (Mwima et al. 2012; Rothuis et al. 2014; Kaminski et al. 2017). Feed is most often the largest cost item in aquaculture and, thus, offers opportunities for cost saving associated with reduced quality and performance. Most aquaculture production systems in East Africa use farm-made or supplementary feeds ranging from single ingredients (e.g., rice bran, wheat bran, and maize bran) to a mix of ingredients, cooked or raw, as moist and dry formulated feeds (Munguti et al., 2014; Amankwah et al. 2016). Larger aquaculture operators often depend on commercially manufactured feeds purchased from several small- or medium-sized feed mills in the region and in most cases switch to imported feed, which deliver a consistent performance. However, prices of imported feeds are extremely high, to the extent that many small-scale fish farmers in East Africa often abandon fish farming. Fish seeds are sourced from hatcheries which are owned by either the government or private farmers (Opiyo et al., 2018). The policy and legal framework for fish seed certification and mechanisms to monitor compliance to fish seed production, supply, and quality are weak and inadequate to guarantee high performance.
While new technologies and innovations are being developed to ensure high-quality and consistent supply of farmed fish to the markets, the impact and scalability of uptake is very slow and inadequate to achieve transformational change envisaged in the 2030 Agenda for sustainable development. In recent years, several studies have identified multiple factors that influence aquaculture technology adoption (Dey et al. 2010; Wetengere 2011; Kumar et al. 2018). However, Glover et al. (2016, p. 5) succinctly noted that “the technology adoption literature provides little insights into the scale or impacts of technological change in African agriculture, let alone the dynamics of these processes.” Furthermore, the literature on the livelihood impacts of aquaculture technology adoption, especially in the context of smallholder households, is limited. In this study, we aimed to (1) analyze the factors that influence fish farmer’s perceptions, attitudes, and behaviors toward aquaculture technology adoption and (2) determine the impacts of technology adoption on farmer’s livelihoods. We grouped aquaculture technologies into five categories representing sustainable intensification, namely (a) culture systems, (b) fish breeding and genetics, (c) feeds and fish nutrition, (d) fish health and disease control, and (e) value addition techniques, post-harvest management, and marketing (Waite et al. 2014; Joffre et al. 2017).
The research is focused in Kenya, where the government introduced a large-scale aquaculture subsidy program under the Economic Stimulus Programme (ESP) implemented from 2009 to 2012 (Ole-Moiyoi 2017). The ESP focused on pond construction, fish feeds and fingerlings supply, post-harvest management, and human resource capacity building of fish farmers and associated institutions. Prior to the ESP project in 2008, there were only 4,742 fish farmers countrywide with 7,530 fish ponds occupying 271 ha. The number of farmers increased tremendously to 49,050, with an estimated 69,998 ponds occupying 2,063 ha at the peak of the subsidy program in 2012 (Nyandat and Owiti 2013). With supportive government policies and substantial public investments, aquaculture production in Kenya increased rapidly from less than 1000 tonnes in 2006 to 24,000 tonnes in the mid-2010s (Obiero et al. 2019b) including in regions of the country with little history of fish production or consumption (Ole-Moiyoi 2017). There is a total of 47 counties in Kenya which are geographical units envisioned by the 2010 Constitution of Kenya as the units of devolved government. Using data collected from 38 counties in 223 constituencies implementing fish farming in Kenya, Macharia and Kimani (2016) estimated a total of 32,000 fish farmers countrywide in 2015. The total area under fish ponds was 2,105 ha in 2013, but the area reduced to ~ 1,808 ha in the year 2015 because of decline in number of ponds from 69,194 to 60,277 in 2 years (Macharia and Kimani, 2015). Therefore, pond-based aquaculture production has registered depressed performance for the third consecutive year, with total fish output dropping by 34% from 18,656 tonnes in 2015 to 12,356 tonnes in 2017 (KNBS 2015, 2018). These high rates of decline in number of fish farmers, ponds, and production present a unique scenario that warrants further investigation.
Analytical framework for technology adoption
Predictor variables that influence farmers’ decision to adopt new aquaculture technologies
Definition of variables
Age of farmer in years
1 if the farmer is male; 0 otherwise
Level of education of the farmer
Years of fish farming experience of the farmer
1 if full-time; 0 otherwise (part-time farmer)
Number of family members
Diversified on-farm enterprises
1 if the farmer is engaged in on-farm enterprise; 0 otherwise
Land tenure arrangements
1 if full-time owner; 0 otherwise (part owner)
Fish farm size
Land area utilized for fish farming
Production status (yield)
1 if high production (> 1.5 kg per m2); 0 otherwise (low)
Sources of information/extension support
Attended extension training
1 if the farmer has attended training; 0 otherwise
Access to extension services
1 if farmer paid to access extension service; 0 if otherwise
Satisfaction levels with extension
1 if the farmer is totally satisfied; 0 totally dissatisfied
Fish cluster/group membership
1 if farmer belongs to a group; 0 otherwise
Source of capital
1 if farmers use personal savings; 0 if otherwise
Characteristics of the technology (perceived attitudes by farmers)
Important for farming needs
1 if technologies are important; 0 otherwise
Better than replacement
1 if new technology is better than replacement; 0 otherwise
1 if technologies increase profits; 0 otherwise
1 if technologies increase yield; 0 otherwise
1 if technologies save time; 0 otherwise
Ease of understanding
1 if the technology is easy to understand; 0 otherwise
Ease of handling
1 if the technology is easy to handle; 0 otherwise
Materials and Methods
Study area and sample selection
To calculate the sample size for this study, we assumed a reduction in the number of fish farmers to be 10% based on observed trends, with a power of 80% and 95% confidence level using power one-proportion method in Stata 13 (StataCorp 2013). This resulted in 305 farmers but after adjusting for a 15% non-response rate, we needed 351 farmers distributed across the selected counties by the proportion of farmers in each county. A total of 331 respondents were interviewed in the study.
Data collection instruments
Farmers completed an interviewer-administered, digitized questionnaire conducted using Open Data Kit (ODK) suite, an open-source application installed on Android mobile phones (Tecno PhonePad 7 II Tablet). The electronic data capture replaces paper-based questionnaire forms and allows for in-built logical checks and skip patterns, thus enabling data to be instantaneously uploaded to and stored securely in a password-protected online database (Hartung et al. 2010). The structured questionnaire solicited information on socioeconomic and demographic details, fish farm profile and production characteristics, sources of information and extension services, farmers’ awareness and adoption of aquaculture technologies, economic viability, and livelihood impacts of aquaculture technologies.
Data were analyzed using Stata version 13 (Corp 2013) and IBM SPSS Statistics for Windows, version 22 (Corp 2013). Descriptive analyses were done by use of counts, means, median, percentages, standard deviation, and ranges. Composite scores were calculated for Likert scale ranked data for ease of interpretation. Adoption score was calculated using the following ranks: adoption = 0 if the farmer reported awareness, interest, or decision to try different aquaculture technology components and adoption = 1 if the farmer had tried or fully adopted the various components of sustainable aquaculture intensification. As for the attitudinal statements, agree and strongly agree were coded as 1 and the other three scales (strongly disagree, disagree, and neutral) as 0. Inferential analyses were done using univariate and multivariate logistic regression models to identify the interaction effects of explanatory or independent variables on the dependent variable, i.e., the likelihood of technology adoption classified as high adopters and low adopters. Multivariate analysis included variables that were significant during univariate analysis but only one variable was picked among a group of variables that were collinear. High adopters were farmers whose overall adoption score was above the mean adoption score of 4.7 (technically those who scored ≥ 5 points). All the explanatory variables were entered into the multivariate logistic model directly and resultant adjusted odds ratios (OR) and their 95% confidence interval (CI) reported (Stoltzfus 2011). A hierarchical cluster analysis (HCA) was used to explore underlying constructs in the survey data to identify distinct farmer adoption categories based on measures of attitudinal statements and motivations for adoption (Mattia et al. 2016). Additional discriminant and stepwise analyses were conducted on the resulting clusters to provide a subset of key variables to assess for significant differences between clusters. Statistical significance was considered when α = 0.05.
Out of the 331 respondents, 258 (78%) were males. The mean age of fish farmers was 50.8 years (SD = 13.5), with the youngest being 19 years and the oldest 80 years. Overall, there were few women (22%) and youths of age ≤ 35 years (13.3%). Seventy percent of farmers had attained secondary education and beyond. Almost equal proportions of fish farming households were composed of 6 to 10 members (46.5%) and 45.9% for those with ≤ 5 members. Most farmers (84.6%) practice fish farming as an income-generating activity, out of which 59% have practiced fish farming for over 5 years. Apart from fish farming, 91.5% of farmers have diversified into other on-farm agricultural activities including livestock rearing (56%), cereal production (45%), horticulture (39%), and industrial crop production (22%) as a strategy to increase their income sources.
Eleven profiles were obtained through pooling five socio-demographic variables, i.e., age, gender, education, household size, and occupation to form a multivariate representation of the fish farming households. The higher the number of profiles (n ≥ 10), the greater the degree of homogeneity among fish farmers, and vice versa. Ten types of socio-demographic profiles were categorized as homogenous, constituting approximately 60% (n = 195) of all sampled fish farmers while 41% (n = 136) belonged to highly heterogeneous profiles, hence were lumped together in one profile. The largest profile (11%) consisted of males aged > 50 years who had attained secondary education/craft certificate (village polytechnics, vocational training institutes) and living in households with > 5 members. Five profiles consisted of male farmers aged < 50 years but with varying household sizes and education levels. Despite fish farming being male-dominated, two profiles were categorized as female-dominated, falling within the same age group (≤ 50 years) with diverse household sizes and education levels.
Farm production characteristics
Close to three quarters (72.8%) of farmers inherited the farmland from their family lineage while the rest purchased, leased, or rented the land for a specific period subject to payment of a fee or rent. The average agricultural farming household owns 5.4 acres of land with a standard deviation (SD) of 8.6 acres. The average land size utilized for fish farming is 0.21 (SD = 0.31) acres (≈ 850 m2). The main reasons farmers venture into fish farming are to increase their incomes (91%) and provide food for their household (51%). The main sources of capital for initiating aquaculture enterprises is through borrowing from banks and microfinance institutions (66%). Worth noting is that 41% of farmers had initiated fish farming activities between 2009 and 2013, mainly supported by the government’s aquaculture subsidy program.
Most farmers (84%) actively manage their ponds, with a few (11%) having dormant or abandoned ponds. Over 79% of farmers use earthen pond with an average size of 300 m2 owned individually (≥ 80%) for the culture of tilapia and catfish species. Twenty-four percent of farmers use liner ponds for culture purposes. The median number of tilapia and catfish fingerlings stocked by farmers is 1,000 (Inter-Quartile Range (IQR), 500–1,500) for each culture period that normally lasts an average of eight months. Sixty-six percent of farmers reported low fish production during their last production cycle. The main reasons attributed to the low fish production were lack of quality and affordable fish feeds (40%), predation of fish (22%), and lack of quality fingerlings (18%).
Advisory and extension services
Over three quarters (77%) of farmers participated in aquaculture advisory, extension, and training programs. The farmers attended or received a mean number of two trainings per year. The main organizations offering advisory and extension services include County Fisheries Departments (43%) and non-governmental organizations or community-based organizations (31%). The knowledge and skills acquired by farmers during extension training are related to pond fertilization and liming (81%); pond design and construction (79%); fish feed formulation, processing, and management (74%); water quality management (71%); and record keeping and enterprise budgeting (64%). For the trained farmers, three quarters (76%) reported they acquired new knowledge and skills that motivated them to adopt new aquaculture technologies and increase their yields and household incomes as well as create employment opportunities.
Farmers participating in extension programs preferred a mix of extension approaches for delivery of training services—50% preferred training and visit approach, 20% favored farmer group training, and 13% liked “farmer to farmer” exchange visits to model farms. The four main extension methods used for disseminating aquaculture information include individual farm visits (68%); mass media (46%), e.g., radio and television; practical demonstration of technologies (37%); and theoretical training (36%). Most farmers (69%) are members of a group or cooperative/association supporting their fish farming operations. The median number of years of membership to either a group or an association is 3 (IQR, 2–5), with the median number of members per group being 23 (IQR, 15–50), while the median number of meetings held yearly is 8 (IQR, 3–12). The key benefits enjoyed by members of groups or associations include training and skills development (62%), linkages to extension providers (44%), better access to markets (42%), access to affordable finance and credit (35%), and sourcing of quality fingerlings and feeds (25%).
Across the five stages of technology adoption process, 62.9% of farmers are aware of new technologies while 28.7% are interested in adopting aquaculture technologies. On average, 26.3% of farmers are currently using supplementary or commercial fish feeds, 18.5% use breeding and genetic techniques, and 9.6% apply value addition and post-harvest loss reduction techniques. The main technologies fully adopted by farmers include use of hormonal sex-reversed fingerlings (62.3%), supplementary feeds (60.4%), value addition techniques (30.8%), commercial pellet feeds (28.1%), complete starter feeds (26.6%), use of liner ponds (24.2%), integrated agriculture-aquaculture systems (19.6%), and on-farm feed formulation (19.3%). Few farmers have decided to try new technological innovations, but the adoption of modern culture systems, e.g., recirculation systems and aquaponic, is still extremely low (< 1%).
Predicting aquaculture technology adoption process and behavior
Predictor variables with significant impact on the adoption of key aquaculture technologies in the study areas
High adopters (n = 99)
Low adopters (n = 232)
Odds Ratio (OR) (95% CI)
Adjusted OR (aOR)
n (%) mean ± SD
n (%) mean ± SD
47.39 ± 14.32
52.29 ± 12.87)
0.98 (0.96, 1.01)
Below secondary level
1.90 (0.92, 3.93)
Above secondary level
4.31 (2.14, 8.67)
1 year and below
2 to 3 years
4 to 5 years
5 years and above
Household size > 5 members
0.79 (0.46, 1.37)
Diversify on-farm enterprise
3.28 (0.90, 11.88)
Land tenure arrangements
Fish farm size (1 acre and above)
3.74 (1.14, 12.27)
Productivity status (yield)
Sources of information/extension support
Attend extension training
3.25 (1.49, 7.10)
Pay to access extension services
Satisfaction with extension service
Characteristics of the technology
Important for farming needs
Better than replacement
Ease of understanding
2.39 (1.36, 3.48)
Ease of handling
Categories of fish farmers and comparisons between their technology characteristics
Perceived usefulness (PU) of technology
Is this technology important to your farming needs?
Is this technology better than what it replaces?
Is this technology able to increase your financial profits?
Is this technology able to increase production for you?
Is this technology able to save you time?
Perceived ease of use (PEOU)
Is this technology easy for you to understand?
Is this technology easy for you to use?
Total (n = 331)
Livelihood impacts of aquaculture technology adoption
Potential impacts of aquaculture technology adoption on livelihood outcome indicators
Livelihood outcome indicator
High adopters n = 99
Low adopters n = 232
Total positive impact
OR (95% CI)
Increased fish consumption
Increased availability of fish in markets
Increased incomes and profits
Wealth creation, e.g., ownership of household assets
Create employment opportunities
Fish farming in Kenya is dominated by men possessing secondary education certificate with over 5 years of fish farming experience. There are huge gender and youth disparities, with women composing less than a quarter of fish farmers in Kenya. These findings agree with studies by Ole-Moiyoi (2017) and Amankwah (2016) revealing fish farming households are predominantly headed by males. This is probably based on strong cultural norms that typically look to male heads of households as decision-makers while placing most household chores and responsibilities in the hands of women. As elaborated by Ndanga et al. (2013), women’s roles in aquaculture production activities are significant, but often under-recognized or “hidden” in aquaculture value chain analyses. Women’s role is similarly masked by ownership of land and other production facilities frequently being formally or informally held by male household members (Kruijssen et al. 2018). The low uptake of aquaculture among women and especially the youth is a threat to the social sustainability of aquaculture (Obwanga and Lewo 2017).
The average age of a fish farmer in Kenya is 60 years, which is striking because the country’s demographic is skewed toward the young people (UNDP 2018). Even though Kenya’s youth (18–35 years) account for 35% of the population and 60% of the total labor force, only 10% are directly participating in the agricultural sector (British Council 2018). Recent trends indicate the youth population has opted to abandon agriculture in pursuit of white-collar job opportunities in urban centers (UNDP 2018). Some of the critical barriers hindering the participation of women and youth in the aquaculture sector are low literacy levels, lack of access to capital, inadequate land ownership and markets. For fish farming to be attractive to the youth, there is a need to change business strategy by investing in modern technologies. This is because younger farmers are more innovative and prefer to keep up with new technologies and have longer planning horizons (Koundouri et al. 2006). While farmers’ age is negatively associated with technology adoption in the present study, there is no clear consensus on the effect of age on previous adoption decisions (Kumar et al. 2018). Age is often cited in the literature as influencing technology adoption, but since its direction of influence is inconsistent between studies, it can be excluded as an explanatory variable (Pannell et al. 2006; Kuehne et al. 2017).
Education is considered a key element for better employment opportunities. Kenya has made significant progress in recent years with primary education enrolment numbers now officially at 100% (British Council 2018). Education is positively and significantly correlated with aquaculture technology adoption (Pannell et al. 2006; Prokopy et al. 2008; Läpple et al. 2015; Ngoc et al. 2016). Well-educated farmers are able to access information and knowledge of production processes and a higher capacity to process and analyze new information (Cofre-Bravo et al. 2018). Uaiene et al. (2009) reported that education gives farmers the ability to perceive, interpret, and respond to new information much faster than their less-educated counterparts. Amankwah et al. (2016) found that household heads in Kenya who had spent more years in school and operated fish farms for more than 5 years had high levels of technology adoption, which is vital for the sustainability of the sector.
The results indicate that large household size is negatively associated with technology adoption. In Kenya, smallholder agricultural families are usually large, with an average of seven members, out of which two are younger than 14 years of age (Rapsomanikis 2015). In contrast, Danso-Abbeam et al. (2018) reported that large households tend to have the free labor supply toward the adoption of an innovation than the smaller households. In addition, larger households have more ability to participate in extra activities as they divide their manpower into various activities. Therefore, it is expected that, all else being equal, the larger the household size, the greater the probability of participation in technology adoption (Suvedi et al. 2017).
Farm-specific characteristics, proxied by diversification of agricultural activities, farm size, and production status, had a highly significant and positive effect on aquaculture technology adoption. Farmers often diversify their activities as a risk management strategy to stabilize their incomes. The size of farms is often suggested as important in farm-based decisions on technology adoption and other agricultural activities (Kumar et al. 2018). Farmers with larger farms, it is often suggested, are more likely to innovate and adopt improved aquacultural technologies than those with smaller farms (Wetengere 2011; Bosma et al. 2012). The owners of large farms are usually wealthier and less risk averse to employ diversification strategies (Rapsomanikis 2015). However, while a high proportion of fish farmers in Kenya continue to actively manage their fishponds, their production volumes remain low, hence hindering adoption of novel technologies. The low production status is mainly caused by high cost of inputs, inadequate supply of quality and affordable fish feed and fingerlings, limited financial and credit facilities, predation and theft, lack of skilled workforce, water scarcity, and complex and expensive technologies, which are consistent with previous studies (Rothuis et al. 2011; Munguti et al. 2017; Obwanga and Lewo 2017).
Extension and advisory services play a major role in the promotion and adoption of sustainable aquaculture practices (Engle 2017; Kumar et al. 2018). A major factor for China’s success in maintaining a highly productive aquaculture sector is the presence of a national extension system with well-trained and qualified staff offering widespread outreach to producers (Msangi and Batka 2015). In Kenya, several aquaculture extension programs have been launched (Ngugi and Manyala 2009), and access to advisory and extension support services is well established in the study areas. Farmer’s attendance of extension training programs and their satisfaction with extension services were significantly associated with high adoption decisions. Generally, farmer’s knowledge about new technologies depends on several characteristics, i.e., their existing skills and knowledge, their involvement in farmer groups, and their usage of farm advisors and on the relative advantage of the practice (Kuehne et al. 2017).
Frequent visits to farmers coupled with issuing of simple materials for reading and constant communication by extension agents positively influences adoption behavior for fish farming (Wetengere 2011; Joffre et al. 2017; Kumar et al. 2018; Ngugi et al. 2018). Amankwah et al. (2016) found that households receiving ten extension contacts per year have about 76% likelihood of purchasing large quantities of commercial pellets in Kenya. Therefore, extension service providers must devise optimum arrangements and appropriate technical specifications for public and private-sector extension support service for smallholders engaged in aquaculture. Based on these study results, extension approaches should contain an appropriate mix of practical demonstrations, Farmers’ Field Schools (FFSs), and farmer-to-farmer exchange visits and methodologies that also maximize the participation of women (Faure et al. 2012). Misiko and Halm (2016) recommended using a mix or combination of extension delivery methods rather than concentrating on one as the most effective approach. Therefore, extension and advisory service provision not only promote the social sustainability of aquaculture program but also lead to increased farm productivity, incomes, and employment opportunities (Engle 2017).
The key training needs that farmers lack and where extension workers can focus their efforts are mainly on business skills, such as production planning, cost and revenue recording, and marketing, as well as support in accessing finance to ensure fish farmers and input suppliers are equipped to invest in their businesses and increase their income. When given a choice, individuals usually choose to interact and communicate with a group with similar beliefs, education, and social status (Kumar et al.,2018). Therefore, farmers’ participation in groups exposes them to various information sources which enables them to analyze risks and benefits and take advantage of new innovations (Mignouna et al. 2011; Ghimire and Huang 2015; Suvedi et al. 2017). Some of the benefits accrued by members of groups/associations include linkages and access to training and extension providers, better access to markets for their selling products, access to affordable finance and credit facilities, affordable sourcing of fingerlings and quality feeds, construction of ponds, and provision of fish storage services.
The choice of aquaculture technologies and their adoption levels remain a focus of smallholder farmers to increase production, productivity, and farm incomes (Ngugi et al. 2018). The main aquaculture technologies ranked highly in this study were related to feed and seed production. Regarding feed production, the main technologies and innovations associated with increased incomes and profits include use of supplementary feeds, use of commercial pellet feeds, and use of complete starter feeds. Much of the aquafeeds used in Kenya are supplemental farm-made feeds either produced on-farm or by small-scale semi-commercial feed manufacturers. Using socioeconomic survey data from 1000+ fish farming households in the Lake Victoria region, Ole-Moiyoi (2017) demonstrated that farmers using commercially formulated pellets realized yields of 321 kg pond−1 year−1, farmers using their own formulated pellets obtained yields of 276 kg pond−1 year−1, while those using “kitchen scraps” leftover from meals and food preparation obtained yields of 249 kg pond−1 year−1. However, the development of aquaculture has been greatly constrained by the ever-increasing costs of fishmeal and fish oil. To remedy this situation, researchers and feed manufacturers have made significant progress to identify alternative ingredients from plant-based and insect-based protein sources for use in fish diets to develop cost-effective feeds that provide adequate nutrition while concurrently reducing the use of traditional sources of protein (Irungu et al. 2018; Ngugi et al., 2017; Onsongo et al. 2018).
In terms of seed production, there exists three significant technologies and innovations used mainly in Kenya: (a) selective breeding to improve desirable traits in Nile tilapia and African catfish; (b) hormonal sex reversal to produce all-male tilapia fingerlings to control reproduction and improve marketability of Nile tilapia (Githukia et al. 2015; Nyonje et al. 2018); and (c) mass production of catfish fingerlings through artificial propagation using injections of pituitary hormones (Opiyo et al. 2017; Nyonje et al. 2018). Furthermore, Ole-Moiyoi (2017) reported that ponds containing monosex male tilapia reach higher yields (343 kg pond−1 year−1) much faster than fish reared in mixed-sex ponds (275 kg pond−1 year−1). However, hormone treatment has raised concerns because it may affect consumer acceptance of the fish, and hormone residues may damage water quality and biodiversity (Bink, 2019). Despite the widespread use of hormones in monosex aquaculture, they are likely to alter various body systems, and possibly influence the susceptibility of fish to diseases and opportunistic infections, and they can pollute the environment (Abo-Al-Ela et al. 2017). Therefore, further studies should be conducted to find alternative more safe ways to ensure all-male aquaculture production, based on the masculinizing effect of high temperatures, and other genetic improvements, involving the use of YY male breeders which give all-male progenies (Abo-Al-Ela 2018). Elsewhere, Henriksson et al. (2018) demonstrated that simple changes in fish farming technology and management practices could result in economic and environmental sustainability of aquaculture enterprises. To sum up, there is a need for adoption of a commercial-oriented approach focusing on investments to improve the productivity of existing smallholder aquaculture producers from the current average of 60–100 kg/pond up to 250–350 kg/pond through appropriate production technologies, proper management, and good-quality inputs (Ole-Moiyoi 2017).
The results show perceived usefulness and perceived ease of use are key indicators in determining technology adoption among smallholder farmers. For a technology to receive wider adoption, a significant number of farmers stated that it should be easy to understand, be easy to handle, and increase yield and incomes. In line with previous studies, perceived usefulness of technology had a greater relative influence on adoption and usage behavior of technologies than perceived ease of use of technology (Flett et al. 2004; McDonald et al. 2016). Fish farmers categorized as high adopters reported increased aquaculture production and productivity, increased household incomes, and improved fish consumption and business opportunities in aquaculture. However, only 30% of fish farmers have fully adopted various aquaculture technologies, implying there are still gaps in technical skills hampering adoption of innovative technologies and best management practices. There is a need for research and extension service providers to improve technical skills and practical knowledge of fish farmers through assistance to women and youth to initiate projects by increasing the funding of training and extension programs at national and county levels. Although not all technologies are associated with higher aquaculture productivity, this study reveals that the most promising technologies that can lead to increased profit margins include use of sex-reversed fingerlings, use of complete commercial feeds, and use of valued addition methods. Therefore, a range of aquaculture-related extension and communication materials, including posters, hard copy information leaflets and brochures of recipes in appropriate languages, short video presentations, and radio features, should be initiated to support the smallholder farmers.
Open access funding provided by University of Natural Resources and Life Sciences Vienna (BOKU). Special thanks go to the project coordinators for the APPEAR project “Strengthening Regional Capacity in Research and Training in Fisheries and Aquaculture for Improved Food Security and Livelihoods in Eastern Africa” for the logistical support during data collection.
This study was financially supported by the Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) under the Austrian Partnership Programme in Higher Education and Research for Development (APPEAR) (Grant Number 101, 2015–2018).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
- Amankwah A (2016) Subsidies, aquaculture technology adoption and welfare impacts in Ghana and Kenya. PhD Dissertation, Purdue University, West Lafayette, IndianaGoogle Scholar
- British Council (2018) Youth employment in Kenya: literature review. https://www.britishcouncil.co.ke/sites/default/files/ng_kenya_youth_employment_in_kenya.pdf. Accessed on 28 September 2018
- Cofre-Bravo G, Engler A, Klerkx L, Leiva-Bianchi M, Adasme-Berrios C, Caceres C (2018) Considering the farm workforce as part of farmers’ innovative behaviour: a key factor in inclusive on-farm processes of technology and practice adoption. Exp Agr:1–15. https://doi.org/10.1017/S0014479718000315 CrossRefGoogle Scholar
- Corp IBM (2013) IBM SPSS Statistics for Windows, Version 22.0. IBM Corp, Armonk, NYGoogle Scholar
- FAO (2016) The state of world fisheries and aquaculture 2016 - contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
- FAO (2017) Regional review on status and trends in aquaculture development in sub-Saharan Africa - 2015. FAO Fisheries and Aquaculture Circular No. 1135/4, Rome, ItalyGoogle Scholar
- FAO (2018) The state of world fisheries and aquaculture 2018 - meeting the sustainable development goals. Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
- Hartung C, Anokwa Y, Lerer A, Brunette W, Tseng C, Borriello G (2010) Open data kit: tools to build information services for developing regions. In: Dell’Osa P, Unwin T, Kleine D (eds) Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, December 13–16, 2010, London, United KingdomGoogle Scholar
- Irungu FG, Mutungi CM, Faraj AK, Affognon H, Tanga C, Ekesi S, Nakimbugwe D, Fiaboe KKM (2018) Minerals content of extruded fish feeds containing cricket (Acheta domesticus) and black soldier fly larvae (Hermetia illucens) fractions. Int Aquat Res 10:101–113. https://doi.org/10.1007/s40071-018-0191-8 CrossRefGoogle Scholar
- Kenya Bureau of National Statistics (KNBS) (2015) Economic survey 2015, Nairobi, KenyaGoogle Scholar
- Kenya Bureau of National Statistics (KNBS) (2018) Economic survey 2018, Nairobi, KenyaGoogle Scholar
- Macharia S, Kimani A (2016) Kenya fish farming enterprise productivity capacity assessment and gap analysis report. State Department of Fisheries and Blue Economy, Nairobi, KenyaGoogle Scholar
- Meijer SS, Catacutan D, Ajayi OC, Sileshi GW (2015) The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub- Saharan Africa. Int J Agr Sustain 13:40–54. https://doi.org/10.1080/14735903.2014.912493 CrossRefGoogle Scholar
- Msangi S, Batka M (2015) The rise of aquaculture: the role of fish in global food security. In: Fan S (ed) 2014-2015 Global Food Policy Report. International Food Policy Research Institute, Washington, DC, pp 61–72Google Scholar
- Munguti JM, Obiero KO, Orina PS, Mwaluma J, Mirera D, Ochiewo J, Kairo J, Njiru MJ (eds) (2017) State of aquaculture in Kenya (1st ed). WestLink Services Limited, Nairobi, KenyaGoogle Scholar
- Mwima H, Monor GV, Mbilinyi HG, Rathacharen S, Omanyi PB, Katonda KI (2012) Regional assessment of fisheries issues, challenges and opportunities for Eastern Africa region. African Union. Interafrican Bureau for Animal Resources, Nairobi, KenyaGoogle Scholar
- Ndah HT (2015) Adoption and adaptation of innovations - assessing the diffusion of selected agricultural innovations in Africa. PhD Dissertation, Humboldt-University of Berlin. https://doi.org/10.13140/2.1.2452.9285
- Ngugi CC, Fitzsimmons K, Manyala J, Bundi JM, Kimotho AN, Amadiva JM, Ndogoni JN, Munguti J (2018) Assessment of growth performance of monosex Nile tilapia (Oreochromis niloticus) in cages using low-cost, locally produced supplemental feeds and training fish farmers on best management practices in Kenya. Project Report. Sustainable Feed Technology and Nutrient Input Systems/SFT/13SFT06AUGoogle Scholar
- Nyandat B, Owiti G (2013) Aquaculture needs assessment mission report. Report/Rapport: SF-FAO/2013/24. September/Septembre 2013. FAO-SmartFish Programme of the Indian Ocean Commission, Ebene, MauritiusGoogle Scholar
- Nyonje BM, Opiyo MA, Orina PS, Abwao J, Wainaina M, Charo-Karisa H (2018) Current status of freshwater fish hatcheries, broodstock management and fingerling production in the Kenya aquaculture sector. Livest Res Rural Dev 30, Article #6. Retrieved August 1, 2018, from http://www.lrrd.org/lrrd30/1/mary30006.html
- Obiero K, Cai J, Abila R, Ajayi O (2019b) Kenya: high aquaculture growth needed to improve food security and nutrition, Rome, Italy http://www.fao.org/3/ca4693en/ca4693en.pdf
- Obwanga B, Lewo MR (2017) From aid to responsible trade: driving competitive aquaculture sector development in Kenya; Quick scan of robustness, reliability and resilience of the aquaculture sector. Wageningen University and Research, Report 2017-092 3R KenyaGoogle Scholar
- Ole-Moiyoi LK (2017) Fishing for answers: can aquaculture transform food security in rural Kenya? PhD Dissertation. Standford University, StanfordGoogle Scholar
- Onsongo VO, Osuga IM, Gachuiri CK, Wachira AM, Miano DM, Tanga CM, Ekesi S, Nakimbugwe D, Fiaboe KKM (2018) Insects for income generation through animal feed: effect of dietary peplacement of soybean and fish meal with black soldier fly meal on broiler growth and economic performance. J Econ Entomol 111:1966–1973. https://doi.org/10.1093/jee/toy118 CrossRefPubMedGoogle Scholar
- Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New YorkGoogle Scholar
- Rothuis A, Duijn AP, van Rijsingen J, van der Pijl W, Rurangwa E (2011) Business opportunities for aquaculture in Kenya: with special reference to food security. LEI report 2011-067/IMARES report C131/11. Wageningen UR, Wageningen, p 2011Google Scholar
- Rothuis A, Turenhout M, van Duijn A, Roem A, Rarangwa E, Katunzi E, Shoko AP, Kabagambe JB (2014) Aquaculture in East Africa; a regional approach. Wageningen UR (University and Research Centre), LEI Report IMARES C153/14| LEI 14-120. 54 pp.Google Scholar
- StataCorp (2013) Stata Statistical Software: Release 13. StataCorp LP, College Station, TXGoogle Scholar
- Uaiene RN, Arndt C, Masters WA (2009) Determinants of agricultural technology adoption in Mozambique, Discussion Paper No. 67E, Ministry of Planning and Development, Republic of MozambiqueGoogle Scholar
- UNDP (2018) Cultivating youth entrepreneurship through agribusiness. http://www.ke.undp.org/content/kenya/en/home/ourwork/inecgr/successstories/cultivating-youth-entrepreneurship-through-agribusiness/. Accessed on 20 Sept 2018
- Waite R, Beveridge M, Brummett RE, Castine S, Chaiyawannakarn N, Kaushik S, Mungkung R, Nawapakpilai S, Phillips M (2014) Improving productivity and environmental performance of aquaculture. Working Paper, Installment 5 of the Creating a Sustainable Future. World Resources Institute, Washington, DCGoogle Scholar
- Wandji DN, Pouomogne V, Binam JN, Nouaga RY (2012) Farmer’s perception and adoption of new aquaculture technologies in the Western Highlands of Cameroon. Tropicultura 30:180–184Google Scholar
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.