UNIVERSITY OF NAIROBI
COLLEGE OF AGRICULTURE AND VETERINARY STUDIES DEPARTMENT OF AGRICULTURAL ECONOMICS
ASSESSING ADOPTION OF BANANA MACRO-PROPAGATION BY SMALL-SCALE FARMERS IN KISII COUNTY
BY
OTIENO HENRY WASALA REG NO: A87/3552/2010
SUBMITTED TO MR. KENNEDY PAMBO
SPECIAL PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENT
FOR THE DEGREE IN BACHELOR OF SCIENCE IN AGRIBUSINESS MANAGEMENT.
i Table of Contents
Acknowledgements ... ii
LIST OF ACRONYMS ... iii
LIST OF TABLES ... iv
1.0 INTRODUCTION ...1
1.1 Background information ...1
1.2 Problem statement. ...2
1.3 Purpose of the study...3
1.4 Specific objectives ...3
1.5 Hypotheses. ...4
1.6 Justification of the study. ...4
1.7 The study site. ...4
1.8 Organization of the research project ...5
2.0 LITERATURE REVIEW ...6
3.0 METHODOLOGY ...9
3.1 Data collection and sampling procedure ...9
3.2 Model used ...9
3.3 Definition of variables used for analysis ... 10
4.0 RESULTS AND DISCUSSIONS ... 13
4.1 Socio-economic characteristic of banana farmers who practice banana macro-propagation ... 13
4.2 Factors affecting the extent of adoption of the banana macro-propagation ... 16
5.0 CONCLUSION AND RECOMMENDATIONS ... 19
ii Acknowledgements
First and foremost I thank God for the energy and good health He provided during the whole process of writing this research paper.
Special appreciation to my supervisor Mr. Kennedy Pambo for his much support and guidance from proposal development through data collection and analysis and to finally this research paper writes up. He added much value to my work and may the Almighty God bless the work of his hands.
Finally, I wish to express my sincere appreciation to my fellow classmates who offered their support through advice and guidance. Just to mention a few, I appreciate the likes of Kevin Oluoch and Francis Maina. May God bless you all.
iii LIST OF ACRONYMS
FAO- Food and agriculture organization TC- Tissue culture
ASK- Agricultural society of Kenya
SPSS- Statistical packages for social sciences
FaCT Ltd- Fertilizer and chemicals Travancore limited OLS- Ordinary least square regression model
iv LIST OF TABLES
Table 1.Table of description of variables………..10
Table2. Table of descriptive analysis results……….………..13
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1.0 INTRODUCTION 1.1 Background information
Bananas are among the most important crop in the developing countries as food and as a source of income. In some countries like Uganda, banana is the staple food. It is produced in countries across the tropics and sub-tropics with the small holder farmers as the majority of the producers (Hanumantharaya, 2007). According to the findings from Njau (2011), the highest consumption of banana worldwide was recorded in the Eastern and Southern Africa. Banana production contributes greatly to the economic development of a country, nutrition, food security among other components. Bananas are eaten either cooked or ripened depending on the varieties but most of the consumers prefer dessert bananas to cooked ones.
In Kenya bananas are produced mainly in western, central, Nyanza and parts of eastern province. It is an important source of food and income to people staying in these regions (Mbaka et al, 2008). These regions have high potential for banana production due their agro ecological characteristics which greatly supports the growth of banana crop.
Despite the importance of the bananas, there is a decline in production of banana especially in the Kisii regions. According to Mainaand Muthoni (2008), factors that led, to decline banana production included pests and diseases, poor agronomic practices and access to clean and affordable planting materials. Getting access to clean and healthy planting materials which are free from pests and diseases was the major constraint. Findings from (Mbaka et al, 2008) indicated that farmers relied heavily on the traditionally regenerated suckers which they obtained from their own farms and from their neighbours for planting. The continued use of those suckers resulted to increased spread of pests and diseases hence leading to the decline in production. To ensure the production of clean and healthy planting materials, tissue culture technology was introduced to Kenya in late 90’s (Qaim, 2011). Tissue cultured planting materials were able to perform agronomically better than the naturally regenerated suckers (Mensah, 2012).The TC technology ensured production of plantlets that were free from pests and diseases. The plantlets also had faster maturity period, were more vigorous and were able to multiply faster and produce multiple plantlets. He further stated that the TC technology led to increase in productivity to farmers who adopted it. Findings from Muyanga (2008) indicated that the adopters of the
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technology increase their income due to the reduction in the cost of controlling pests and diseases.
Even though TC technology ensured production of clean planting materials and increased production, the adoption rate is low (Acharya and Mackey, 2008). The low adoption rate is attributed to the high cost of TC. Findings from Mbaka et al, 2008), indicates that the TC seedlings costs sh.120 per seedlings which was unaffordable to the local small holder farmers. Due to this, farmers continue to rely on the conventional suckers which are more susceptible to pests and diseases and hence continued decline in banana production is realized.
To increase the adoption of the use of clean planting materials, a cost effective method should be employed in producing these planting materials. Findings from Njau, (2012) highlighted that macro propagation is one of the cost effective method that ensured production of clean planting materials that are pocket friendly to small-scale farmers in the central and eastern provinces of Kenya. Macro propagation is cheaper since according to her findings, the price per plantlet is sh.50 which most farmers can afford.
Despite the effectiveness of the banana macro propagation in the central and eastern Kenya, there is still low adoption rates in other banana producing areas in the country especially Kisii regions. More studies have not been done to find the reason as to why the macro propagation technology has not been adopted by farmers in KisiiCounty even though the technology is pocket friendly and can ensure production of clean multiple planting materials hence reversing the decline in production. I am focusing my study in Kisii so as to assess how farmers in the region perceive banana macro propagation in terms of its cost effectiveness in production of clean planting materials which leads to increase in production.
1.2 Problem statement
Increase in population has increased the demand for banana which has become more important in consumption. Due to this there is need for increased production which in turn increases demand for clean planting material to ensure high yields (Kasyoka et al, 2011). The decline in banana production in Kenya has been recorded in the past two decades which is attributed to lack of clean planting materials, pests like the banana weevil and diseases like Fusarium wilt and black sigatoka, poor agronomic and crop husbandry practices, declining soil fertility among others
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(Mwangi and Muthoni, 2008). The major factors contributing to the decline are pests and diseases and lack of access to clean planting materials to farmers.
To address this constraint banana TC technology was introduced in Kenya in the late 90’s (Qaim, 2011). The adopters of this technology realized huge gains from banana technology since the TC technology was able to produce multiple clean planting materials, TC plantlets had faster maturity period hence reducing the time to harvesting. They also benefited from income increases which were as a result to the reduction in the cost of pest and disease control (Muyanga, 2008).
Despite the importance of this technology, there is still low adoption rate majorly attributed to the high cost of TC investment yet the majority of the farmers cannot be able to afford to adopt this technology (Acharya and Mackey, 2008). Due to this, farmers have continued relying on the natural regeneration of suckers by obtaining the suckers either from their own farms or from their neighbours. This has still led to continued drop in production. More studies have been put in place to identify the factors that can lead to increased adoption of the technology, one of them being finding a cost effective method of producing banana plantlets. Findings from Njau (2011), demonstrated that macro propagation technology was one of the cost effective methods of producing clean banana planting materials but no studies have been done on the adoptability of this technology; that is, the study on the factorsaffecting the adoption on this technology in Kisii County.
1.3 Purpose of the study.
The purpose of this study is to determine the factors affecting the adoption of banana macro-propagation among small scale farmers in Kisii County.
1.4 Specific objectives
To determine the socio-economic factors of small-scale farmers practicing banana macro-propagation in Kisii county.
To assessthe relationship between the factors influencing the adoption of macro propagation by the small scale farmers in Kisii County.
4 1.5 Hypotheses
Most small-scale farmers prefer to use indigenous methods of banana production due to inadequate information on the modern techniques.
Farmers’ perception about macro propagation is not directly related to their level of education.
1.6 Justification of the study
Banana production is an important farming enterprise in Kisi County due to its agro ecological characteristics. Banana macro propagation has been identified as a cheaper method of producing clean banana plantlets as compared to TC hence many small scale farmers can afford to adopt it. It is superior to conventional suckers since alongside ensuring clean multiple banana plantlet production, it also ensures high yield. This study targets to generate information on the factors influencing small scale farmers’ adoption of cost effective methods of banana propagation and the importance of applying cost effective methods in banana production.
The information obtained from this study will be useful to the policy makers and various government agencies in provision of initiatives that will ensure transition of small scale farmers from subsistence production to commercial production in tandem with vision 2013.
1.7 The study site
This site for this study is Kisii region since it is one of the major banana producing areas in Kenya. This region receives adequate amount of rainfall that is well distributed throughout the year and a temperature which ranges from 16 0C to 27 0C. It has fertile soils that are responsible for the higher agricultural production realized in the area. The county has five districts which are Kisii central, Kisii south, Gucha, Gucha south and Masaba. It has a population density of 874 people per km2. Farmers in this area grow crops such as coffee, tea, maize, bananas among others which greatly contribute to the economy of the area (softkenya). Banana farming is majorly done by small scale farmers which highly act as a food and cash crops to most of these farmers (ASK, 2011). Banana yields used to be good but have declined for the past years due to lack of clean planting materials, pest infestations and diseases, lack of credit facilities and information on modern methods of production, poor agronomic practices among others (Mureithi, 2005). This study site was chosen because of its high potential in banana production and the decline of production which has been realized due to low adoption of modern technology
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in producing clean banana plantlets. Furthermore, there is no study that has focused on assessing factors influencing the adoption of the low cost technology of banana macro propagation in this region.
1.8 Organization of the research project
The rest of this research project is organized as follows; chapter 2 covers review of literature. Chapter 3 covers methodology applied in this study. Chapter 4 gives results and discussions of the analyzed data; chapter 5 gives conclusions and recommendations and lastly Chapter 5 gives the list of references for this study
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2.0 LITERATURE REVIEW
This chapter contains a review of the research work done in the past relating to the current study. Several studies have been done on the impact of modern methods of banana macro propagation and the factor influencing the adoption of these modern propagation methods. A few numbers of studies relating to macro propagation as a cost effective method of banana propagation were done. Other methods of propagation in banana production are also reviewed.
(a) Banana production
Hanumantharaya, (2007), indicates that banana belongs to the family musaceae and provides carbohydrates when cooked to the consumers. Banana is the fourth most widely consumed crop after maize, rice, and wheat. It is used as staple food for most countries in the tropics and sub tropics regions for instance Uganda (Rarieya and Schmidt, 2009).
Banana production worldwide is estimated to be 97.5 million tones (FAO, 2004). India is the leading banana producing country in the world followed Uganda which is the second producer globally (Rarieya and Schmidt, 2009). Most of the production is done in the developing countries which accounts for 98% of global production and this offers food security to these developing countries (Hanumantharaya, 2007). It is evident from the findings from Hanumantharaya (2007), that there is increase in demand for the banana due to increase in population hence the need to increase banana productivity through the use of modern technology of production such as the use of the tissue cultured technology.
In Kenya banana is largely grown in four provinces which are Nyanza, Western, Central and Eastern provinces which makes of about 90% of the total national banana production with Nyanza leading by 56% (Qaim, 1999). Both the cooking and dessert varieties are produce but of the consumers prefer dessert varieties to cooked varieties. It is important food crop and cash crops for many individuals in the country.
(b) Constraint to banana production
Mwangi and Muthoni, (2008) stated that over the past two decades, decline in banana production globally has been realized. This is majorly attributed to pest infestations and diseases. The major banana pest includes the banana weevil and the major banana diseases are found to be fusarium
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wilt and black sigatoka. Other factors that have caused the decline in banana production are poor agronomic practices, declining soil fertility, lack of clean planting materials among others. (c) Rejuvenating production.
The major cause in the decline is pests and diseases. Getting access to clean banana planting materials reduces the chances of pest infestation and disease attacks to some level. Tissue cultured banana production method of producing banana plantlets has been introduced in the late 90’s to aid in producing clean banana planting materials (Njuguna et al, ) .Banana TC technology ensured production of clean planting materials which in turn led to increase in banana yields to the adopters of the technology (Muyanga, 2008). The adopters of TC technology also benefited from increased incomes due to reduction in cost of controlling pests and diseases.
Mbogo et al, (2002) studied impact of biotechnology applications on banana production. They found out that modern methods of propagation were capital intensive but leads to higher output while the traditional methods required less production but the yield was too low. TC banana required more input as compared to sucker propagation. Higher output was realized in TC banana than in sucker propagated bananas.
Hanumantharaya, (2007) studied a comparative economic analysis of TC banana production in Karnataka. The findings were that the adopters of this technology were educated farmers. The technology ensures high yields per hectare (50.04 tones per hectare) as compared to the natural regenerated sucker production which was 40.05 tones per hectare. Apart from high yield realization, the technology was effective in production of clean and healthy plantlets which were free from pests and diseases.
Muyanga, (2008) studied smallholder adoption and economic impacts of TC banana. He found out that the high production cost of the TC technology in banana production led to its low adoption since most farmers could not afford to purchase the plantlets. The production cost of TC bananas as a percentage of the total production was 5.1% which was5 high compared to 4.1% of the naturally regenerated suckers. This made more farmers to opt for traditional sucker banana regeneration production because the technology proved expensive.
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Lefranc et al., (2007) studied macro propagation as an innovative technology: lessons and observations learnt from projects in Cameroon. They found out that macro propagation technology led to improved quantity and quality of banana planting materials. The technology can lead to faster multiplication of plantlets, that is, in three months the plantlets can be multiplied to yield eight to twenty times the number of plants with better quality. They also found out that the costs associated with this technology are within the reach of many smallholder farmers. Therefore it was found to be a cost effective method of banana propagation as compared to TC technology.
Maina and Muthoni, (2008) studied potential and challenges of implementing banana macro propagation in Kenya. They stated that has been largely done in Cameroon and Nigeria and has ensured increase production. It has further spread to other West African countries and east African countries like Uganda, Tanzania and Rwanda. Findings indicate that FaCT limited, a private company, has been implementing macro propagation technology on a pilot basis in Kenya since 2007. The macro propagated seedlings have been availed to the market since 2008 and they stated that the uptake of those seedlings has been high in the locations where they were sold. The seedlings were priced at 40-50% less than TC plantlets due to low production cost involved in producing these seedlings. Macro propagation structures can be constructed at a lower cost using the cheaper locally available materials. The cost of this technology can be further lowered by constructing the nurseries nearer to demanding customers to avoid costs that may be incurred due to transportation.
Njau, (2012) studied the effectiveness of macro propagation technique in production of healthy banana seedlings in eastern and central Kenya. Major constraints to production was found to be pests and diseases at 57% of farmers that were surveyed, inadequate extension services were 33% and declined soil fertility at 28%.She found out that macro propagation can be a cost effective method of ensuring that clean planting materials are made available to small scale farmers. There was 100% survival of seedlings after transplanting in all the study sites. The cost producing and maintaining macro propagated seedlings were considerably low hence the price of plantlet could be Kshs. 50.
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3.0 METHODOLOGY 3.1 Data collection and sampling procedure
Primary data for this study were collected from the sampled farmers through well-developed questionnaires, interview schedules and focus group discussions. Primary data collected included total farm income, farmer’s contact to extension agents, different kinds of production technologies employed by farmers, extent of new technology adoption, farmers obtaining credit facilities, different agricultural and non-agricultural practices that brings income among others. Secondary data for this study was obtained from government publications.
Stratified random sampling method was applied which targeted banana farmers in the county. The respondents were then selected randomly from the targeted banana farmers. A sample of 30 farmers was used in the region of the study. The sample size was arrived at using central limit theorem which states that, as the number of occurences increases, the expected result move nearer to the actual result.
3.2 Model used
Ordinary least square regression model was used to determine the relationship of factors influencing the adoption. The OLS regression model employed is given below:
Yi=a+bXi+e
Where Yi- dependent variable (adopting banana macro-propagation)
a- constant term (other factors affecting the dependent variables but were not included in the variable)
b- Coefficient determined by the variable
Xi- Independent variables affecting the dependent variables. e- Error term
10 3.3 Definition of variables used for analysis
Table 1. Definition, unit of measurement and expected effect of hypothesized variables.
Variables Definition of Variables Expected Sign
AgeHH Age of household -
GenderHH Gender of household +
Edulevel Education level of household +
Farmincome Total farm income of household +
Nonfamincome Participation in non-farm activities +
Accinfo Access to agricultural information +
Acccred Access to credit facilities +
Farmexp Banana farming experience +
Farmgrp Membership to farmers’ groups +
Extserve Obtaining government extension services +
Age is an important factor in explaining adoption decision. The negative sign shows that age has an inverse relationship to adoption. It is expected that as the age of the farmer increases, the rate of adoption decreases. Young farmers are most likely to adopt technology than older farmers. This is can be attributed to the fact that older people are more conservative.
The rate of male adopting new technology is higher than that of females. The positive sign show s that males are expected to adopt new technology as opposed to females. This is brought about by the fact that male have high degree of mobility in terms of providing extension services hence easily acquire information as opposed to females. Secondly, extension providers usually contact
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the males who are the head of the families who at times fails to disseminate the information to their fellow female.
Adoption correlates positively with education. Educated farmers are expected to adopt new farming techniques as opposed to the illiterate ones. This is because educated farmers are able to process information and search for appropriate technologies to improve productivity.
Total farm income is expected to correlate positively with the adoption of banana macro-propagation. The positive sign shows that as farm income increases, the level of adoption increases.
Participation in off-farm activities is also expected to correlate positively with adoption. The positive sign shows that as farmers engage in more non farming activities to supplement their farming activity then they tend to adopt new technology. This arises because participation in off –farm activities increases finances of the farmer hence able to invest in new technology.
Getting agricultural information by farmers is expected to correlate positively with adoption. The positive sign shows that as farmers frequently get agricultural information; their adoption probability increases. This is because farmers who get access to information becomes aware of the new technology thereby are likely to adopt the new technology as opposed to those farmers who do not get access to agricultural information.
Farmers’ access to credit facilities is expected to correlate positively with the adoption of banana macro-propagation. The positive sign shows that accessing credit by a farmer increases his ability of adopting new technology. This is because credit expands the funds needed by the farmer to invest in a new technology.
Farmers with higher experience of banana farming are expected to have high probability of adopting banana macro-propagation. The positive sign shows a positive correlation between farming experience and adoption. This is because farmers with many numbers of years of banana farming are able to evaluate the advantages of employing new technology in production as opposed to those who are less experienced.
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Being a member of a farmer group is expected to positively influence farmer’s decision to adopt new technology. This due to the fact that it exposes farmers who learn from other farmers who have adopted that new technology.
Frequent access to extension agents by farmers is expected to increase the adoption rate of the farmers. This is because extension agents will disseminate the rightful information to the farmers thereby enhancing their adoption.
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4.0 RESULTS AND DISCUSSIONS 4.1 Socio-economic characteristic of banana farmers
Table 2 Descriptive statistics for frequencies and continuous variables
Variables Description Age (% above 25 years) 73.3
Gender (%female) 63.3
Education level (%above higher level) 63.3
Occupation (%farming) 66.7
Awareness (%yes) 60.7 Access to credit (%yes) 76.7
Access to extension services (%yes) 80
Membership to farmers’ groups (%yes) 56.7
Participation in off-farm activities (%yes) 43.3
Practice banana macro-propagation (%yes) 33.3
Total farm income (mean) 2.1(1.029) Farming experience (mean) 1.87(0.9)
N/B: Standard deviations for continuous variables are in brackets.
Table 2 above analyzes the socio-economic characteristics of the small-scale farmers who practice banana macro-propagation in Kisii County. It was used to answer the specific objective one of this study.
According to the result in this table, majority of the farmers who practice banana farming were those with the age range of above 25 years old. They represented 73.3%. This means that most of the people who undertake banana farming activities are the older individuals.This is because banana farming was viewed by younger individuals as an activity for the older people hence they left it for the older individuals to undertake it. Most of the farmers interviewed had many number of years in banana farming experience with a mean of 1.87 with a standard deviation of 0.9. This also clearly indicates that those involved in banana farming are older people since experiences increases with increase in age.
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According to the result of table 2, majority of the people who were found to undertake banana macro-propagation farming were females as they represented 63.3%.This was because most of the homes visited were headed by the females due to widowhood. Also most of the males had moved to towns in search of white collar jobs as they viewed banana farming as a non-enterprising activity.
Education level was another important level explaining the attributes of banana farmers practicing macro-propagation. Majority of the banana farmers had higher level of education as they represented 63.3% shown it table 2 results. This increases their probability of practicing banana macro-propagation since most technologies requires higher level of understanding before they can be practiced hence educated individual are at upper hand of processing and evaluating the newer production technologies.
Those of the respondents interviewed do banana farming as their major means of occupation as they represented 66.7% of the total respondents. This means that farming contributed largely to the livelihood of the people in this county hence most farmers preferred it to other enterprises. There was low participation by the banana farmers in off-farm activities. The result in table 2 shows that only 43.3% of the banana farmers participated in off-farm activities. This is because most farmers put their focus on banana farming.
The level of awareness creation was higher as 66.7% of the banana farmers were actually aware of new production methods. Most of the respondents obtained the information from government extension agents, fellow farmers and media. The education levels also increases the awareness of different production technologies since those with the education levels above higher level were found to be 63.3% as shown in table 2. The government extension services were also as high as 80% which clearly indicates that there is high level of awareness creation among the banana farmers in the county.
The result in table 2 shows that there was higher access to credit by the banana farmers as 76.7% of the interviewed respondents had access to credit. Access to credit increases small holder farmers’ ability of practicing banana macro-propagation because credit enables the farmers to be able to invest in the technology. It expands small-scale farmer’s financial ability hence the
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farmers are able to purchase the necessary inputs needed to compliment the technology to realize maximum benefit.
Access to government extension services by banana farmers was as high as 80% as shown in table 2. This is because most farmers rely ongovernment extension agents to obtain agricultural informationwhich exposes them to different technologies thereby increasing their probability of trying new production techniques.
Belonging to farmers’ groups by a banana farmer was at an average of 56.7% as shown in table 2. This was because belonging to these groups improves farmers’ ability to obtain technical advice on farming, financial support and farming techniques information.
There was still low percentage of banana farmers who practice banana macro-propagation as the represented 33.3% according to results in table 2. This was because most banana farmers lacked technical knowledge on applying banana macro-propagation due to insufficient training.
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4.2 Factors affecting the extent of adoption of the banana macro-propagation technology Table3 Regression results for the estimation of the rate of adoption and the factors influencing adoption of the banana macro-propagation technique.
Variables Coefficients Standard Error Significance
Constant 1.151 0.154 0.000 AgeHH 0.003 0.053 0.149 GenderHH -0.016 0.048 0.742 Edulevel 0.352 0.143 0.024 FarmIncome 0.022 0.030 0.468 Accinfo 0.174 0.076 0.035 Accred 0.048 0.059 0.421 Extserve -0.238 0.090 0.016 Farmgrp 0.167 0.059 0.011 Farmexp 0.274 0.047 0.000 Nonfamincome 0.045 0.102 0.064
Source: Field survey data, 2014
Factors which were found to affect banana macro-propagation included household characteristics and institutional factors. The result in table 3 shows that the significant variables which affected the adoption of the banana macro-propagation were education level of the respondents, farming experience, farmer belonging to farmers’ groups, awareness and provision of the government extension services. The rest of the variables as indicated in the result in table 2 were insignificant to the adoption of the technology.
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Age of the respondent has a positive relationship with the adoption of banana macro-propagation unlike earlier expected. Increase in age from age range of 0-25 to 25 and above years increases the adoption level by 0.3%. This may be due to the fact that older farmers have longer experience in farming thereby have countered many challenges in production. They are more likely to respond to these challenges by trying out new techniques as opposed to young farmers who lacks experience.
Gender of the respondent has a negative relationship with the adoption of banana macro-propagation unlike earlier hypothesized. Moving from male to female reduces the probability of practicing banana macro-propagation by 1.6%.This is as a result of the fact that most males view banana farming as a female enterprise thereby considers it as not important hence the females who engage in banana production are able to adopt new technology to counter the banana low productivity challenge.
Education level of the farmer has a positive relationship with the adoption of banana macro-propagation like earlier expected. The result in table 2 shows that as education level increases from lower levels to higher levels, the adoption increases by35.2%. This arises due to the fact that educated individuals are at upper hand of getting new agricultural information than the ones with low level of education. They are also in a position to evaluate the benefits of adopting new technology before adopting it.
Total farm income has a positive relationship with the practicing of banana macro-propagation just like earlier expected. As the total income from the farm produce increases to high level, the adoption increases by 2.2%. Farmers who receive higher incomes from the farm produce are higher adopters of new farming techniques than those who receive low incomes from the total agricultural produce. This is because those who receive higher incomes are motivated by that hence tending to employ all resources necessary to maximize their output.
Access to agricultural information by banana farmers has a positive relationship to adopting banana macro-propagation technology as earlier expected. As farmers access more agricultural information, their adoption increases by 17.4% as shown in table 2. This is due to the fact that getting agricultural increases farmer’s awareness hence leading to increased adoption.
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Accessing credit facilities by small scale farmers has a positive relationship with the adoption of banana macro-propagation as expected. Increasing access to credit by one unit increases the adoption by 4.8%. This is because accessing credit enables small-scale farmers to acquire funds necessary for investing in new technology and for the purchasing for the complimentary inputs needed to realize the maximum benefit of the technology.
Farmers contact with government extension agents has an inverse relationship with the adoption of the banana macro-propagation technology. As the number of extension agent visits increases, the adoption decreases by 23.8%. This is because most of the government extension agents tend to focus on the production of other farm produce such as maize rather than focusing on banana production hence most farmers tend to shift on producing of other produce.
Farmer belonging to farmers’ groups and adopting banana macro-propagation has a positive relationship. Being a membership of a farmer group increases the adoption by 16.7%. Those who practiced banana macro-propagation were mostly those who belonged to different farmers’ groups. This is because being a member of farmers’ groups exposes farmers to different production technologies and also a farmer obtains adequate training on the application of new technology and finances to employ in the new technology.
Farming experience and adoption of banana macro-propagation has a positive relationship as expected earlier. As the year of experience in banana farming increases by one year, the adoption increases by 27.4%. This is due to the fact that experienced farmers has encountered many challenges in production hence are more likely to adopt new technology to address the challenges as opposed to the less experienced ones.
Farmers’ participation in off-farm activities has a positive relationship with the adoption of banana macro-propagation as expected earlier. Farmers’ engagement in off-farm activities increases the adoption by 4.5%. This arose due to the fact that participation in off-farm activities led to expansion of farmers’ income hence able to adopt new technology effectively since farmers can afford to invest in this technology and to purchase complimentary inputs.
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5.0 CONCLUSION AND RECOMMENDATIONS
The aim of this research was to study the factors affecting the adoption levels of banana macro-propagation by small-scale farmers in Kisii County.The data was analyzed with the help of SPSS version 14 where descriptive statistics and regression analysis were used. The descriptive statistics was used to analyze the factors influencing adoption while the regression analysis analyzed the relationship between factors influencing adoption and the degree of adoption. According to the results of the regression analysis, the variable that affected the degree of adoption of banana macro-propagation included level of education, awareness of the techniques, government extension services, farming experience and a farmer belonging to farmers’ groups. The other variables were insignificance to adoption.
The policy recommendation should include effective provision of extension services by the government to increase farmers’ awareness on the new production technologies. Proper training should also be given to farmers on how to employ banana macro-propagation techniques in production. This can be achieved through organized training programs to the local farmers who lack enough exposure. The government should also create an enabling environment to encourage the formation of community based organizations to enhance dissemination of information to farmers. Farmers should also be educated on the importance of forming and joining farmers’ groups and encourage them to join these groups so as to make it easier for offering training and giving information.
20 References.
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Dayarani,M.,Dhanajaran,M. and Durai,P. (2013). Macro propagation for regeneration of wild bananas.
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Kasyoka, M., Mwangi, M., Koni, N. and Muasya,R. (2010). Evaluating the macro propagation efficiency of banana varieties preferred by farmers in Eastern and Central Kenya.
Kasyoka,M. (2011). Comparing initial performance of macro propagated, tissue cultured and naturally regenerated banana seedlings.
Mbaka,J.,Mwangi,M. and Mwangi,N. (2008). The role of tissue cultured planting materials in Kenya. Applied biosciences, 9(1):354-361.
Mensah,B.,Quain,D.,Kwame. and Kodjo,S. (2012). Comparative study on the field performance of hybrid dessert banana propagated from tissue culture and conventional sucker in Ghana.Plant development, 19(2012):41-46
Muyanga,M. (2008). Small holder adoption and economic impacts of tissue culture banana in Kenya.African Journal on biotechnology, 8(23):
Mwangi,M. and Muthoni,S. (2008). Implementing banana macro propagation in Kenya-potential and challenges.
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Njeri,N. (2012). Effectiveness of macro propagation technique in production of healthy banana seedlings in Eastern and Central Kenya.
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