Tissue culture banana adoption in Kenya: Overcoming barriers through integrated policy support

Introduction

Food is a basic human need and a fundamental pillar of a healthy and productive society. However, in Kenya, food availability remains elusive for many citizens. The country is food-deficient and relies heavily on market supply and a predominantly rain-fed agricultural production system to meet local food demand (Wambua, Omoke, & Mutua, 2014) [29] . The major staple foods produced locally include maize, rice, wheat, Irish potatoes, and bananas. Bananas are cultivated in Kenya for both household consumption and market sale. The average annual production exceeds 1.8 million metric tons (FAO, 2017) [9] , making the crop a significant contributor to national food security. Globally, bananas provide over 25% of the daily carbohydrate intake and are cultivated in more than 150 countries (WHO, 2023) [33] . In 2017, global banana production was estimated at approximately 125 million metric tons, with India accounting for about 25% of total output (Filipenco, 2023; FAO, 2018) [7,10] . While Asia remains the largest banana-producing region, Latin America and the Caribbean dominate global exports, contributing nearly 80% of the total (Voora, Larrea, & Bermudez, 2020) [28] . In Kenya, banana production is primarily undertaken by smallholder farmers, many of whom are women. Yields range between 4.5 and 10 tonnes per hectare (Jiménez, Mainar-Causapé, & Ferrari, 2021; Masinde et al., 2012) [23,17] . The key banana-growing regions include Kisii, Vihiga, Bungoma, Kakamega, and Kericho in Western Kenya, as well as Murang'a, Embu, Nyeri, and Meru in the Central region. Despite the favorable agro-ecological conditions, banana production remains low and unable to meet growing national demand. This deficit is exacerbated by rapid population growth, urbanization, unfavorable production conditions, labor and supply chain disruptions, and both macro-and microeconomic shocks that are linked to climate change (Jiménez et al., 2021) [23] . To understand adoption patterns in banana farming, this study draws on three theoretical paradigms: innovation diffusion, the socioeconomic perspective, and the technology acceptance model. The innovation diffusion theory, first introduced by Ryan and Gross and later expanded by Rogers, classifies adopters into innovators, early adopters, early majority, late majority, and laggards, based on socioeconomic status, personality traits, and communication behaviors (Ruzzante, Labarta, & Bilton, 2021) [31] . While widely used, this theory has been critiqued for assuming the universal appropriateness of innovations, regardless of context (Ruzzante et al., 2021) [31] . The socioeconomic perspective focuses on utility maximization and risk aversion within the framework of unequal access to resources. It emphasizes that farmers www.extensionjournal.com make adoption decisions based on economic constraints, resource endowments, and risk considerations (Agutu et al., 2022) [30] . This perspective is particularly useful in understanding why resource-constrained smallholders are often hesitant to adopt input-intensive technologies such as tissue culture bananas (Agutu et al., 2022) [30] . The technology acceptance model complements these perspectives by stressing the importance of perceived usefulness and ease of use in influencing technology uptake. Farmers are more likely to adopt innovations when they believe the technology will enhance productivity and is compatible with their farming systems. Banana remains a critical crop in Kenya's food security strategy, yet its production potential is underutilized. Understanding the multidimensional drivers and barriers to technology adoption can inform more effective agricultural policy. Addressing structural constraints such as land fragmentation, limited market access, and poor infrastructure-while promoting access to credit and technical support, particularly among youth-will be essential for improving banana productivity and overall farmer welfare. Finally, the individual adopter-perception paradigm captures the subjective attributes of innovations that influence adoption behavior. Together, the three paradigms determine the ultimate adoption patterns among individual farmers. Increased adoption of tissue culture banana (TCB) production holds significant potential as a game-changer for enhancing household food security and access (Wossen et al., 2017) [34] . These perspectives also offer pathways to higher incomes and contribute to diversifying diets away from traditional staple foods, whose production remains low and insufficient to meet local demand. However, adoption rates of TCB in Kenya remain relatively low compared to neighboring countries such as Uganda and Burundi (Wossen et al., 2017; Warinda et al., 2020) [34,8] . New technologies often present hurdles to adoption, either through specific constraints or prerequisite conditions. Constraints may include farm-level factors such as limited financial capital, labor shortages, or low soil fertility. Prerequisites, on the other hand, refer to broader institutional, economic, or social conditions that must be in place to facilitate adoption. If these prerequisite conditions, are not met, even a rational farmer may choose not to adopt the new technology (Wossen, Berger, & Di Falco, 2015) [35] . The conditions include among others the availability of quality inputs and the presence of functional markets, Some adoption studies have examined these prerequisite conditions at the individual level, focusing on factors such as awareness of the technology (i.e., non-exposure bias), access to inputs, and access to credit. This presents the central puzzle that motivated the current study: to identify the strategies that would effectively enhance the adoption and production of tissue culture bananas in Kenya. Where X is a matrix of socioeconomic, personality, environmental, farm financial, farm management, or external factors, which are gathered through surveys. The studies assume that farmers are rational actors who aim to maximize an unobserved expected utility function. Variables may influence expected utility either directly or indirectly leading to variable yields, which makes the potential revenues variable due to adopting improved technology.

Methodology

Revenues is thus be used as a proxy for expected utility following adoption of TCB given existing resource constraints. The choice variables can influence influence the expected utility indirectly, through a relationship with risk. For example, education may reduce risk-aversion, and thus encourage more educated farmers to adopt innovations, which promise higher incomes but less security.

Adoption can thus be expressed as

There is a general belief that technologies being fronted as modern are better than what is existing. This fallacy needs to be discounted before any adoption process takes place. The effectiveness of a technology is realizing better performance depends on resource availability and suitable environment. Since farmers aim to maximize utility by choosing not to adopt, choosing to delay adoption, or choosing to partially adopt technologies based on their risk averseness.

Results and Discussion

Patterns of Adoption

The most commonly grown banana variety in Meru is Keganda, which accounts for 46% of the total production, followed by Kampala at 13%. The average annual yield per farm was 353 kg, with an average farm size of 1.7 acres. This translates to a yield of approximately 207 kg per acre. The level of adoption of tissue culture banana (TCB) among the sample drawn from Meru was about 3%. This is in sharp contrast to earlier studies, which reported adoption rates of between 62% and 70% for new technologies when they were first introduced in the early 1990s (Thuo, Nguluu, & Kisangui, 2017; Masinde, Obare, Owuor, & Wasilwa, 2013; Omari, Muna, & Mburu, 2024) [26,17,6] . Notably, although almost all farmers had banana crops on their farms, only a meagre 1% were cultivating TCB (see For business-oriented farmers, this trend is particularly troubling, as it signals a decline in the adoption of Tissue Culture Banana (TCB) farming, with only a few new farmers embracing the technology. The older generation shows a higher adoption rate compared to the younger generation. Notably, 62% of respondents had more than 10 years of experience in banana farming, indicating long-term engagement with TCB. However, the adoption rate declines significantly among farmers aged over 60. Younger farmers-particularly those under 35-represented less than 4% of the sample. The majority of current TCB farmers adopted the technology in the 1990s and have continued its cultivation since then. These findings suggest that willingness to adopt TCB increases with age up to a certain point, after which it declines. The initial wave of TCB adopters embraced the technology at a time when returns from small-scale agriculture could adequately support household needs under a less diversified production system. However, as population pressures on land have increased, the viability of growing long-maturing crops like TCB has decreased-especially in the absence of alternative livelihoods (Otieno, 2024) [4] . The current youths desire faster and higher means of getting incomes to sustain modern lifestyles. They had no time for ventures that take time to realize returns slowly. While earlier studies found that the desire for higher revenue was a key driver of adoption in the 1990s, this motivation appears less influential today. Facilitating conditions such good results from early adopter sand receive financial support to grow TCB. The younger generation did not eke enough returns for a living from alternative business business ventures where the also lacked skills (Otieno 2024) [4] . This deepened their misery and majority barely eke a living from those investment. Figure 2 shows that current adoption is characterized by a "wait and see" approach where the youth are willing to adopt new TCB technology only when it guarantees higher output and revenues. Pressure on land also denied the youth a major farming asset. The current generation has a high demand for higher incomes to meet the contemporary lifestyles and would thus engage in highly productive ventures that mature early. While most studies reported that the desire to earn higher revenues drove technology adoption agenda in the the early 1990s, that same does not hold now.

The adoption patterns among younger farmers revealed a generally risk-averse approach. Previous studies showed that adoption was conditional on access to extension support, financial assistance, and information from seed operators and even faith-based organizations (FBOs) conditions that are now less financially viable.

According to the Unified Theory of Acceptance and Use of Technology (UTAUT), technology adoption is driven by www.extensionjournal.com expected performance. However, the reluctance younger farmers' to adopt TCB contradicts this theory, even with public and private sector support in place (Otieno and Mose, 2025) [5] . The United Theory of Acceptance and Use of Technology is well aligned with the Chi-square results showing that adoption of new technology has a strong relation with extension service providers, financial assistance and information sources.

Considering the descriptive data Table 1, adoption patterns is influenced by education level. Majority of TCB farmers have primary and secondary level education yet those willing to adopt are the younger generation who account for a large percentage of willing adopters. While the younger population that is more more educated is largely noncommittal about adoption. This suggests a conditional willingness to adopt, hampered by barriers such as high input requirements, seed costs, and lack of credit access. Importantly, farmers perception influence the adoption patterns. Approximately 65% of respondents believed TCB to be genetically modified (GMO), perceiving it as a potential threat to human health. This misconception has contributed significantly to the low adoption rates. On whether TCB affects the cultural production practice, the farmers did not have conclusive decision as they all agree to some extent. Equally, lack of credible information about TCB hinders adoption. [5] . These theories expound that farmers will only adopt new technologies based on their utilities. Where farmers, adoption of new technology Omari et al, (2024) [6] found out that bout above 62.2% for both adopters and non-adopter of farmers desired to adopt tissue culture banana. This high preference was dependent among others on high average yield reported for TCB whose productivity was about ten tons of fruit per acre compared to five tons by native bananas. This supports the hypothesis that the adoption of agricultural innovations follows predictable patterns rooted in these established theories (Otieno and Mose, 2025) [5] . Therefore aligning tissue culture technologies interventions for improved food access with social, individuals construct and their malleable perceptions can enhance adoption and food access outcomes.

Correlation between the variables

A correlation was established between the variables that were to be included in the regression. These variables addressed the risk factors that farmers face, market risk, labour risk, information risk, financial risk, production risk, health risk and group dynamics. Using the United Theory of Acceptance and Use of Technology (ATUT), these variables were regressed on farm level income which determined to a large extent whether farmers would benefit from adoption. The results are presented in Table 2 below. The data fits the model quite well with a high adjusted-R 2 of 0.88. This implies that the appropriate variables were used in the analysis.

Table 3 presents an analysis of the key factors influencing income expansion. These include productivity, number of extension visit, farm size and household sizes. The data show that productivity, number of extension visits, farm size, and household size have a significant and positive effect on household income growth. These variables are not only directly associated with higher productivity but also with enhanced engagement in agricultural markets (Otieno and Mose, 2025 and Haile et al; 2022). [5,16] Increased market participation, a critical component for income generation, was further influenced by factors such as education level, farming experience, banana yield, market access, amount of credit received, and farmers' perception of market prices. These findings are consistent with previous studies (Otieno & Mose, 2025; Haile et al., 2022) [5,16] , which emphasized the role of socioeconomic and market-related factors in shaping farmers' decisions to commercialize their production.

Regarding adoption the study further reveals that high TCB yields confers a positive attribute to increased adoption of the TCB technology translating to better returns hence meeting the objective of the small scale farmers. In addition, the theories defined the apriori sign of the endogenous variables. Farm size, productivity and market distance had strong positive correlation and can thus be used a proxy for each other. However, market distance had a negative impact on farm incomes since cost of services and inputs increase with distance. This reduces the potential total returns. The characteristics of TCB banana that lead to increased adoption is the high yield characterized by early maturing, high bunch weight, and uniform development and growth characteristics. TCB banana matures in 300 days unlike the other traditional varieties which take 400 days (Omari, et al, 2024) [6] . The net effect is higher returns on investment with a shorter payback period. As such, TCB venture is a suitable business venture for small holder farmers since it does not lock seed capital for long. The growth characteristics also makes it easier for farmers to control field practices, enabling simultaneous harvesting, planning and operate an efficient TCB marketing activates. corroborated these results and reported that the desire of local farmers to adopt a new technology is influenced by high production that must meet their consumption needs with the surplus being marketed. The marketable surplus provides income used to cover expenditures for enhanced food access and other household requirement. Th figure shows the traits of TCB that endeared them to adopters. The most important were early maturity, high yielding and boosting incomes. With increased consumption of plantain as an alternative to the main staple foods in www.extensionjournal.com Kenya, increased productivity and adoption of TCB banana promises a better future for farming households. Small sized housholds have low consumption and hence a bigger marketable surplus for sale and household income. In the local markets, TCB banana fetches three times more income than ordinary banana. The crop has a uniform growth pattern that allows simultaneous harvesting, Further, the bundles are larger and heavier than traditional ones, which do not mature uniformly. TCB banana therefore made marketing easy and therefore supported the claim by farmers that the adoption of tissue culture banana significantly increased returns from market sales. This motivated farmers to participate in credit financing to improve their investment portfolio. Consequently, any efforts to boost household income from both agricultural and non-agricultural sources such as credit will spur the uptake of new technology, like tissue culture bananas. Tissue culture banana technology not only transcend the benefits of the green revolution by providing high yielding genetically identical plantlets that were disease resistant (Thuo el al,2017; Masinde, et al, 2013, and Omari, et al, 2024.) [26,17,6] , but that produce had a high market demand. Omari (2024) [6] reported that on average, 57.5% of respondents, felt that the disease resistant tissue-culture bananas have a greater demand. This consists of 60.4% of non-adopters agreed and 72.2% of adopters ((Thuo el al,2017) [26] , Masinde, et al, 2013 [17] , and Omari, et al, 2024) [6] . This indicates that TCB is a low cost technology and could be favoured by commercial farmers who were targeting high returns. The low cost production technology therefore incentivized increased adoption. Therefore, The combined ability of farmers to use TCB technology to realize increased yields, high returns market at a low cost are critical cognitive elements that contribute to increased farmers adoption (Ţiţan, 2015) [27] .

Conclusion

The study reveals that while Tissue Culture Banana (TCB) technology holds considerable potential for increasing agricultural productivity and household income, its adoption is heavily skewed toward older generations of farmers.

References

1. Asaaga FA, Hirons MA, Malhi Y. Questioning the link between tenure security and sustainable land management in cocoa landscapes in Ghana. World Dev. 2020;130:104913. https://doi.org/10.1016/j.worlddev.2020.104913
2. Caniglia EC, Murray EJ. Difference-in-Difference in the time of cholera: A gentle introduction for epidemiologists. Curr Epidemiol Rep. 2020. https://doi.org/10.1007/s40471-020-00245-2
3. Cunningham S. Causal inference: The mixtape. New Haven London: Yale University Press; 2021. https://mixtape.scunning.com/
4. Otieno D. Policy options for enhanced access to agricultural land for migrating youths in densely populated rural Kenya. Int J Recent Adv Multidiscip Res. 2024;5426.
5. Otieno D, Mose G. Promoting the adoption of tissue culture banana through increased market participation by smallholder farmers in Kenya. 2025.
6. Omari EN, Mucheru-Muna M, Mburu BK. Socioeconomic factors influencing the uptake of tissue culture banana technology in Kisii County, Kenya. Environ Chall. 2024;14:100812. https://doi.org/10.1016/j.envc.2023.100812
7. Filipenco D. Can universal basic income eradicate poverty? Pros and cons of monthly cash payments. Development Aid. 2023.
8. Warinda E, Nyariki DM, Wambua S, Muasya RM, Hanjra MA. Sustainable development in East Africa: Impact evaluation of regional agricultural development projects in Burundi, Kenya, Rwanda, Tanzania, and Uganda. 2020. https://doi.org/10.1111/1477-8947.12191
9. FAO. Trade Policy Briefs: Agricultural Export Restrictions. 2017. http://www.fao.org/3/a-i8006e.pdf
10. FAO. Banana Market Review: Preliminary results for 2018. Rome, Italy; 2018.
11. Fischer E, Qaim M. Gender, agricultural commercialization, and collective action in Kenya. 2018.
12. GoK. Kenya National Bureau of Statistics (KNBS). Statistical Abstract, 2018. Nairobi, Kenya; 2018.
13. Greifer N, Stuart EA. Choosing the causal estimand for propensity score analysis of observational studies. arXiv. 2023. https://doi.org/10.48550/arXiv.2106.10577
14. Hernán MA, Robins JM. Causal inference: What if. Boca Raton, Florida: Chapman and Hall / CRC; 2024. https://www.hsph.harvard.edu/miguel-hernan/causalinference-book/
15. Huntington-Klein N. The effect: An introduction to research design and causality. Boca Raton, Florida: Chapman and Hall / CRC; 2021. https://theeffectbook.net/
16. Haile K, Gebre E, Workye A. Determinants of market participation among smallholder farmers in Southwest Ethiopia: Double-hurdle model approach. Agric Food Secur. 2022;11:18. https://doi.org/10.1186/s40066-02200358-5
17. Masinde W, Obare GA, Owuor G, Wasilwa L. Assessing farmer perceptions, attitudes and preferences for tissue culture banana technology in Kenya. Afr Crop Sci Conf Proc. 2013;11:729-738.
18. Kabunga NS, Dubois T, Qaim M. Yield effects of tissue culture bananas in Kenya: Accounting for selection bias and the role of complementary inputs. 2012.
19. McElreath R. Statistical rethinking: A Bayesian course with examples in R and Stan. 2nd ed. Boca Raton, Florida: Chapman and Hall / CRC; 2020.
20. Morgan SL, Winship C. Counterfactuals and causal inference: Methods and principles for social research. 2nd ed. Cambridge University Press; 2014. https://doi.org/10.1017/CBO9781107587991
21. Pearl J, Mackenzie D. The book of why: The new science of cause and effect. New York: Basic Books; 2020.
22. Sitko NJ, Chamberlin J, Hichaambwa M. Does smallholder land titling facilitate agricultural growth? An analysis of the determinants and effects of smallholder land titling in Zambia. World Dev. 2014;64:791-802. https://doi.org/10.1016/j.worlddev.2014.07.014
23. Jiménez S, Mainar-Causapé AJ, Ferrari E. Analysis of the Kenyan economy: An input-output approach. Agrekon. 2021;60(4):480-495. https://doi.org/10.1080/03031853.2021.1984957
24. Statistica. Food and agricultural organization of the United Nations. WB. 2024.
25. Statistica. World food production data 2024.
26. Thuo CN, Nguluu S, Kisangau P. Factors affecting adoption of tissue culture bananas in the semi-arid areas of lower eastern region of Kenya. Int J Recent Res Life Sci. 2017;4(3):1-26. www.paperpublications.org
27. Ţiţan AG. The efficient market hypothesis: Review of specialized literature and empirical research. Procedia Econ Finance. 2015;32:442-49. https://doi.org/10.1016/S2212-5671(15)01416-1
28. Voora VA, Larrea C, Bermudez S. Global Market Report: Banana. 2020. https://www.researchgate.net/publication/378140382_G lobal_Market_Report_Banana
29. Wambua BN, Omoke KJ, Mutua TM. Effects of socioeconomic factors on food security situation in Kenyan drylands ecosystem. Asian J Agric Food Sci. 2014;2(1):52. www.ajouronline.com
30. Agutu FO, Ondiek JO, Mbuku SM, Bebe BO. Willingness to pay for assisted reproductive technologies by pastoral herd owners in Southern Rangelands of Kenya. Int J Vet Sci Anim Husbandry. 2022;7(1):26-31. DOI:10.22271/veterinary.2022.v7.i1a.401.
31. Ruzzante S, Labarta R, Bilton A. Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Dev. 2021;146:105599. https://doi.org/10.1016/j.worlddev.2021.105599
32. Walisinghe BR, Ratnasiri S, Rohde N, Guest R. Does agricultural extension promote technology adoption in Sri Lanka. Int J Soc Econ. 2017.
33. WHO. Carbohydrate intake for adults and children: WHO guideline. 2023. https://www.ncbi.nlm.nih.gov/books/NBK593396/pdf/ Bookshelf_NBK593396.pdf
34. Wossen T, Abdoulaye T, Alene A, Haile MG, Feleke S, Olanrewaju A, et al. Impacts of extension access and cooperative membership on technology adoption and www.extensionjournal.com household welfare. J Rural Stud. 2017;54:223-33. https://doi.org/10.1016/j.jrurstud.2017.06.022
35. Wossen T, Berger T, Di Falco S. Social capital, risk preference and adoption of improved farm land management practices in Ethiopia. Agric Econ. 2015;46(1):81-97. https://doi.org/10.1111/agec.12142