Author: Muriuki, J. M.
Language: English
Year of publication: 2010
Number of pages: 94
Type of publication: Paper
Publication location: Text on this website
Country: Kenya
Region: East Africa

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CHAPTER ONE

INTRODUCTION

1.1  BACKGROUND

Beekeeping is an old art in Kenya that has been practiced since time immemorial by most communities. In the old days, the production of honey was a major industry in the African economy and, as observed by Nightingale (1976), honey was a vital factor in African culture and was used in many ways as an article of trade. Currently, two types of production technologies, i.e. traditional and modern beekeeping are widely used in the country.

 

Traditional beekeeping, as defined by Crane (1999), is the form of beekeeping where activities or techniques are based on methods that are handed down from ancestors to posterity. Usually such methods are passed on from one generation to the next by means of oral instruction and also by practice. Traditional beekeeping systems in Kenya are based on the use of local materials for hive construction, the use of locally available resources, the application of old age indigenous technical knowledge and the experience of beekeepers.

 

Under the traditional production systems, most of the hives are made of logs, bark, woven fibre and clay. Usually no bee colony management is practiced and beekeeping involves preparing the hives, treating them with suitable bait and placing them in a position to await natural occupation by a swarm of honey bees.  Honey harvesting is done at particular seasons governed by custom and experience. The most common method of honey harvesting is by smoking the bees with a burning torch of dried twigs or bark at night. This kills many bees and contaminates honey leading to a low quality product. It may also lead to the destruction of the nest resulting in absconding.

 

Modern forms of beekeeping exist in the country which entails the use of top bar hives, frame- hives and accessories, e.g. bee protective clothing, bee smoker, bee brush and hive tool. Bee management and product handling techniques which improve the quantity and quality of hive products are also practiced under this production system.

 

Although honey production is an old activity among many of Kenyan communities, recognition of its potential as a commercial enterprise is a fairly recent phenomenon, dating to 1967 (Kigatiira and Morse, 1979). Over the years, however, beekeeping has developed in various aspects and is now an important component of the livestock sector particularly in the arid and semi arid areas. The national honey and beeswax production is currently estimated at 14,653 and 140 metric tonnes respectively with an estimated value of Kshs. 4.43 billion. This level of production is however far below the national potential which is estimated to be 100,000MT of honey and 10,000MT of beeswax annually (GoK, 2008a).  

 

The major drawbacks to improved productivity include environmental degradation, low apicultural research, inadequate bee management skills and low adoption of appropriate technology.

 

The Arid and Semi-Arid Lands (ASALs) constitute 80% of Kenya’s land mass and carry over 60% of the country’s livestock resources (GoK, 2008b).  These areas support about 10 million people and are home to 90% of the wildlife (GoK, 2009).  As noted by Herlocker (1999), rangelands are also a major source of useful products, e.g. honey, resins, gums and traditional herbal medicines, and a considerable amount of biodiversity.  Sustainable management of these areas is therefore important for the sustenance of the local human and animal populations, conservation of rare flora and fauna species, maintenance of water and carbon cycles, and preservation of cultural and natural landscapes.

 

Many different development projects have been initiated in the rangelands with varying degrees of success. These projects include, among others, beekeeping, dry land farming systems, agro- forestry, livestock production and water resource development. Beekeeping has in particular become popular due to its recognition as a sustainable form of agriculture that can provide rural people with a source of much needed income and nutrition. According to Jones (2006), it also provides an economic reason to retain natural habitats and has the potential to increase yields from food and forage crops.

 

1.2 STATEMENT OF THE PROBLEM

Although various beekeeping technologies have been available in the country for a number of decades, what is not clear is the effect of these technologies on the production levels of hive products and on the farmers’ social and economic conditions. As noted by Carroll (2006), some of the technologies have often resulted in little impact in terms of enhanced production.

 

Kenya has an annual estimated honey and beeswax potential of about 100,000 and 10,000MT respectively. However, a comparison between the potential and the estimated production shows that there is an annual deficit of about 75,000MT and 7,500MT of honey and beeswax respectively (GoK, 2008a).

Although beekeeping is a major activity in the country’s southern rangelands, its potential has not been fully exploited. With appropriate technology, the amount of honey produced could be increased from about 9.2 to over 100 tonnes per year (Mutungi, 1998). Currently, information is lacking concerning the adoption of the various beekeeping production technologies and their social, economic and ecological impacts.

 

1.3 JUSTIFICATION OF THE STUDY

Beekeeping can be practiced with the highest potential in the dry areas where crop farming is not possible and livestock rearing does not directly compete with beekeeping (Kigatiira, 1985). The actual production is carried out by households as a part-time income generating activity and food security.

 

According to Bradbear (2002), bees are the only livestock capable of harvesting nectar and pollen and converting them to high value products and without them, these valuable resources would go to waste. Most of the developing countries in the tropics are dominated by rangelands and have an urgent need to increase opportunities for employment, household incomes and food security. Beekeeping can make a useful contribution in this respect as research and development work already done suggests that it is a viable economic enterprise and a source of stable occupation for the rural communities (Mutungi, 1998).

 

There is therefore need to establish the various factors that determine adoption of the various beekeeping technologies in order to develop appropriate intervention strategies and packages as a way of boosting beekeeping and production of hive products in the rangelands.

 

1.4 OBJECTIVES OF THE STUDY

1.4.1 Broad Objective

The broad objective of this study was to establish the factors that determine the adoption of various beekeeping technologies and the impact of these technologies on the production of hive products.

 

1.4.2 Specific Objectives

The specific objectives were to:

1.      Determine the technical, ecological and socio-economic factors that influence the adoption of beekeeping technologies.

2.   Estimate the benefits in terms of incomes and productivity levels of adopting various beekeeping technologies.

3.      Determine the extent of adoption of beekeeping technologies in the study area.

 

1.5 HYPOTHESES

The study tested the following hypotheses:

1.   A combination of socio-economic, technical and ecological factors influences the adoption of various beekeeping technologies.

2.   The productivity of and returns from modern technologies are higher compared to traditional technology.

 

1.6 STUDY LIMITATIONS          

This study focused on the adoption of beekeeping technologies by sampling farmers from three locations in the study area due to limitations associated with time, finances and infrastructure. In this regard, the results cannot be representative of the whole district or the entire country due to the small size of the sample. Most of the data collected were based on the recall ability of the respondents who may not have given very accurate information due to memory lapses considering most of them had only basic education. However, the research recommendations may as well be applicable in other areas having similar ecological and socio-economic characteristics.

 

1.7 ORGANIZATION OF THE THESIS

The thesis is organized into five chapters. Chapter One comprises the introduction and highlights some background information highlighting importance of beekeeping. It also includes the statement of the problem, significance of the study, objectives, hypotheses and limitations of the study. The second chapter is on literature review and delves into past studies and available information relevant to this study. The third chapter is on research methodology and includes description of the study area, sampling techniques, methods of data collection and tools for data analysis.  In the fourth chapter the main findings of the study are discussed in detail. Chapter Five wraps up the thesis with conclusions and recommendations.

CHAPTER TWO

LITERATURE REVIEW

2.1 ECOLOGY OF KENYA’S RANGELANDS

Rangelands in Kenya comprise more than four-fifths of the country’s total land surface and carry about one quarter of the total human population (GoK, 2004). Rangelands are characterized by variable climatic conditions making them unsuitable for cultivation agriculture (Pratt and Gwynne, 1977). In the more arid areas, recurrent drought is an inevitable part of the rangeland environment. Historically, rangelands supported pastoral communities which relied on livestock and their products for their subsistence. Herlocker (1999) observed that the pastoralists were highly adapted to their environment and used various coping strategies to enhance their survival in the face of uncertainties associated with the range environment.

 

However, this situation has changed over time and there has been a general increase in both human and livestock populations in the Kenyan rangelands. This has been occasioned by various improvements in infrastructure, water resources and health facilities. Most of the high potential rangelands have also been alienated for other uses such as game parks and private land leaving less land available for the growing human population. As a result, overall productivity has diminished and range degradation has increased (Herlocker, 1999).

 

Despite the apparent importance of rangelands, they have often been marginalized by both policy makers and practitioners due to lack of a clear understanding of their ecological uniqueness. Both the colonial and post independence governments gave the rangelands low priority and it was only in the 1970’s that the government took a special interest in the potential contribution of these areas to the national economy (Mutungi, 1998).

 

Considering the extent of the rangelands in Kenya, their ecological and economic value and their importance as a means of improving livelihoods of large numbers of people, it is obvious that improved management methods of rangeland resources are needed (Mwanthi, 2009). These methods should consider resource conservation or restoration strategies, including adoption of appropriate production technologies in order to improve rangeland resource productivity.

 

2.2 CONCEPT OF TECHNOLOGY ADOPTION

Adoption of agricultural technologies has attracted considerable attention among development agencies. This can be attributed to the fact that a majority of the population in developing countries derives their livelihood from agricultural production. A new technology therefore offers opportunities to increase production and incomes substantially (Feder et al., 1985).

 

Rogers (1983) has defined adoption as a mental process through which an individual passes from first hearing about an innovation to the final adoption. This process, also known as the ‘innovation-diffusion model’ has five stages namely; awareness, interest, evaluation, trial and adoption. Adoption can be classified into individual and aggregate adoption according to its extent of coverage (Feder et al., 1985).  Individual adoption refers to the farmer’s decisions to incorporate a new technology into the production process while aggregate adoption is the process of diffusion of a new technology within a region or population.

 

Workneh (2008) noted that the adoption pattern to a technological change in agriculture is not uniform at the farm level. It is a complex process, which is governed by many socio-economic factors. These include awareness and attitude of the farmers towards the technology, institutional factors and the farmers’ resource endowment, e.g. land holding size and labour availability.

 

2.3 DETERMINANTS OF TECHNOLOGY ADOPTION

Social scientists investigating farmers’ adoption behaviour have accumulated considerable evidence showing that demographic variables, technology characteristics, information sources, knowledge, awareness, attitude, and group influence affect adoption behaviour (Oladele, 2005). Factors affecting technology adoption have been classified differently by various authors. Mwanthi (2009) classified them into social, management and institutional factors. Social factors include age of potential adopter, social status of farmers, education level and gender-related aspects, household size, and farming experience. Management factors include membership to organizations, the capacity to borrow, concerns about environmental degradation and human health of farmers while institutional factors include information access, extension services, and prior participation and training in resource management practices.

 

McNamara et al. (1991) grouped the factors into four broad categories namely farmer characteristics, farm structure, institutional characteristics and managerial structure. Yet other scholars classify the factors into human capital, production, policy and natural resource characteristics (Wu and Babcock, 1998).  Such categorization is usually done to suit the technology being investigated, the location, and the researcher’s preference, or even to suit client needs and as such, it may not be necessary to try and make clear-cut distinctions between different categories of adoption factors (Mwanthi, 2009).

2.4 DECISION MAKING AND TECHNOLOGY ADOPTION

Technology adoption is a decision making process in which an individual goes through a number of mental stages before making a final decision to adopt an innovation. Decision making is the process through which an individual passes from acquiring knowledge of an innovation, forming an attitude towards an innovation, decision to adopt or reject implementation of the new idea and confirmation of the decision (Ray, 1999).

 

Within the farm household, the ability to make decisions regarding resource use and technology adoption varies according to age, gender and other categories. Actual decisions can depend on a complex bargaining process among household members. Beyond the household, group processes and the ability to harness them can also play a crucial role in adoption decisions. Moreover, decisions about new technology are frequently prompted by an intervention in the form of a project (Cramb, 2003).

 

Lionberger (1960) noted that the decision to adopt usually takes time. Normally people do not adopt a new practice or idea as soon as they hear about it. They go through a series of distinguishable stages which include awareness, interest, evaluation, trial and adoption. Another classification of innovation decision making is given by Rogers (1983) who identifies five stages, i.e. knowledge, persuasion, decision, implementation and confirmation.

 

A new technology alone does not guarantee a wide spread adoption and efficient use (Ehui et al., 2004). For efficient utilization of the technology, the fulfilment of specific economic, technical and institutional conditions are required. From the farmers’ economic perspective, the new technology should be more profitable than the existing alternatives. Technically, the new technology should be easy to manage and adapt to the surrounding socio-cultural situations. Similarly, the availability of the new technology and all other necessary inputs at the right time and place and in the right quantity and quality should be ensured.

 

In addition, the socio-economic and other demographic factors of a farmer may influence the farmer’s decision of either adopting a given technology or not. Hence, the farmer’s observed adoption choice for an agricultural technology is likely to be the result of a complex set of interactions between comparable technologies and the farmer’s socio-economic and demographic factors.

 

Wetengere (2010) observed that when a technology is introduced in a given area, the choices available to farmers are not just adoption or rejection. Some parts of a technology or modification and re-invention may be options too. Farmers’ choice whether to adopt an entire package of a recommended technology or just some parts of a technology is influenced by the following factors:

• The availability of household resources.
• The degree to which the technology is appropriate for the farmer’s farming environment.
• Economic motivation.
• Farmers’ characteristics (e.g. belief and gender).
• The farmers’ objective for undertaking the activity.

 

2.5 REVIEW OF PAST STUDIES ON DETERMINANTS OF TECHNOLOGY ADOPTION

Many studies on adoption of agricultural technologies have been undertaken in various disciplines in different parts of the world. Most of these studies however have tended to focus on the adoption of improved technologies such as improved seed varieties, use of fertilizer, soil and water conservation methods but have used variables similar to those used in this study.

 

In studies on determinants of agricultural technology adoption conducted in Mozambique, Uaiene et al. (2009) reported that households with access to credit and extension advisory services as well as members of agricultural associations are more likely to adopt new agricultural technologies. Households with higher levels of education are also more likely to adopt.

 

Mwanthi (2009) carried out a survey on rangeland resource management technology adoption among agro pastoral households in south eastern Kenya and found out that participation in project activities, gender of household head, and managerial skills had a positive significant effect on adoption. Type of information source and education level of household head had a negative significant effect on adoption.

 

Research on the determinants of adoption of a recommended package of fish farming was conducted in selected villages in eastern Tanzania (Wetengere, 2010). Findings revealed that access to resources is a key factor that determines the adoption of a recommended package of a technology and farmers allocate resources to activities which contribute to household food and income security. Farmers are likely to adopt a complete package of a recommended technology if household resources such as land, labour, cash income, knowledge and other inputs like feeds, fertilizers, water and seeds are forthcoming from the existing farming system.

 

Factors influencing adoption of conservation tillage in Australian cropping regions were evaluated by D’Emden et al. (2008). Results from the study indicated that perceptions associated with shorter-term crop production benefits under no-till, such as the relative effectiveness  of pre-emergent herbicides and the ability to sow crops earlier on less rainfall were influential.  Increased cropping extension activities were also strongly associated with no-till adoption.

 

While carrying out studies on determinants of adoption of improved box hive in the Tigray region of Ethiopia, Workneh (2007) found out that use of credit, perception, the education level of house hold head and practical knowledge of the technology were positively influencing adoption decision of improved box hive.

 

Demeke (2003) studied the factors influencing adoption of soil conservation practices in north western Ethiopia and observed that farm size and perceptions of benefit from conservation measures positively and significantly affected farmers’ decision to adopt conservation structures. The distance of a plot from the homestead, availability of off-farm employment and tenure insecurity were found to be significant and influenced the farmers’ adoption decision negatively.

 

Studies on the factors influencing the adoption of improved maize and fertilizer technologies were carried out in Embu District, of Kenya (Ouma et al., 2002). Analysis of the results using maximum likelihood estimation logistic regression model indicated that the agro-ecological zone, gender, use of manure and hiring labour influenced adoption.

Degu et al. (2000) carried out studies on the adoption of seed and fertilizer packages and the role of credit in smallholder maize production in Sidama and north Omo zones, Ethiopia. The analysis of factors affecting the adoption of improved maize showed that number of TLUs, agro- ecological zone, extension services, use of credit, and membership of an organization all significantly influenced the probability of adoption. Significant factors affecting the adoption of fertilizer were off-farm income, the use of hired labour, credit and being a contact farmer.

 

Makokha et al. (1999) carried out studies on farmers’ perception and adoption of soil management technologies in western Kenya and found out that farmers’ characteristics such as participation in field days and demonstration, attendance at workshops, seminars and contact with extension workers, and leadership position have significant influence on perception and hence adoption decisions.

 

In their study of adoption of agricultural innovation in developing countries, Feder et al. (1985) have listed the factors that influence technology adoption as credit, farm size, risk, labour availability, human capital and land tenure. The authors too note that education can also directly facilitate technology adoption, by increasing access to information about alternative market opportunities and technologies.

 

2.6 IMPORTANCE OF BEEKEEPING

Honey has for centuries been one of the most highly desired foods. Among the hunter-gatherer communities, it is the only readily available sweetening agent and tradable commodity. Hive products have been used by mankind for centuries, e.g. bee brood is traditionally eaten as a high protein food while beeswax is used in candle making. Other hive products are also drawing attention from the pharmaceutical and cosmetic industries. For instance propolis is now widely used in apitherapy for its anti-viral and anti-bacterial properties. Pollen on the hand has found its way to some health food outlets as a protein rich commodity (Paterson, 2006).

 

Beekeeping has immense benefits in terms of provision of pollinators, which enhance crop yield. It is estimated that one in every three bites of food we eat is a result of active pollination of plants in which bees play a very important part (Carroll, 2006). Adequate pollination leads to better quality seeds and fruits and is essential for sustaining biodiversity. Bradbear (2002) observed that although pollination is difficult to quantify, it is the most economically significant value of beekeeping.

 

The beekeeping industry contributes to the wider rural economy through trade (Paterson, 2006). Kigatiira (1976) noted that the beekeeping industry in Kenya is worth millions of shillings and plays an important part in the economy of arid areas. The livestock sub-sector in Kenya of which bees are part, contributes about 10% of Kenya’s GDP. Beekeeping alone contributes about 1.89% of this amount (Muya, 2004).

 

Beekeeping is a family level exercise which has some distinct advantages over other agricultural activities (Crane, 1976).  For instance, beekeeping requires very little financial or labour input. It is a flexible and gender friendly enterprise which does not compete for resources with other agricultural activities. Beekeeping is possible in arid areas and places where other crops have failed (Bradbear, 2002). It is therefore feasible in marginal conditions and a suitable activity where people need to restore their livelihoods or create new opportunities.

 

2.7 DEVELOPMENT OF BEEKEEPING INDUSTRY IN KENYA

Although beekeeping is a traditional practice which has been developed and tried for thousands of years, efforts to increase honey production in Kenya through improved methods were initiated in 1967 by way of a two year pilot project through a grant by Oxfam (Kigatiira, 1976). By the end of the Oxfam exploratory project in 1970, the government requested the Canadian government to provide technical assistance to establish a permanent apicultural section in the Ministry of Agriculture. The assistance came in by way of a cooperative program incorporating training, extension, research, equipment design, and promotion of markets through establishment of beekeepers’ cooperatives.

 

Because of the success of this initial intervention, the Canadian supported project was eventually extended and in 1982, a National Beekeeping Station was established to consolidate the gains so far achieved and to further develop the industry. Since then, beekeeping has progressively grown to become an important component of the livestock sub sector particularly in the more marginal areas where other forms of livelihoods cannot be sustained effectively.

 

2.8 BEEKEEPING TECHNOLOGIES IN KENYA

2.8.1 Traditional Technology

Majority of beekeepers in Kenya still use traditional production systems which comprise mainly hollow log hives (Carroll, 2006). These hives constitute the single largest number of hive types in the country estimated at 1,273,000 with 73% of the hives concentrated in the eastern part of the country (Muya, 2004). Other traditional hives include the bark hives made of bark that has been peeled from the trunk of a tree.

 

Honey harvesting is normally done at night and it sometimes involves stripping naked before climbing the trees on which the hives are hanging (Patterson, 2006). However, many of the old and experienced traditional beekeepers have abandoned the practice due to various reasons as named below:

• Increased human population which has opened up natural bee habitats for cultivation.
• Vandalism.
• Frequent and severe droughts that have resulted in a significant decline in honey bee population.

 

A variety of indigenous hard wood tree species are used in making of traditional hives. The common ones include Terminalia brownii, Delonix alata, Cordia africana and Albizia gummifera. The hives are made of pieces of logs measuring 1.0-1.5 metre. They can be of uniform diameter or sometimes narrowing towards one side with the walls made as thin as possible in order to reduce the overall weight of the finished product (Nightingale, 1976). In communities like the Kamba and their close neighbours living in eastern Kenya, the whole log is hollowed out from end to end. The openings at both ends are usually closed with wooden planks. One of the planks, normally at the narrower end is provided with bee entrances and fixed while the other is removable and has no entrance holes. This is the opening through which the beekeeper can access the inside of the hive during honey harvesting (Kigatiira, 1976).

 

In some other communities living in the Rift Valley, the log is split lengthwise and the two sections hollowed out into troughs. The two sections are then fitted together and, as observed by Patterson (2006), the inside is accessed by way of a trap door cut into the base of the hive. After the hives are well seasoned, they are usually baited with suitable materials, e.g. beeswax, propolis or leaves of some plants like the Ocimum kilimandscharicum or Ocimum basilicum before they are placed on trees. Hives are hanged either horizontally or at an angle. The Kamba beekeepers hang their hives at an angle in order to prevent any moisture resulting from condensation collecting at the bottom of the hive. The hives are placed such that the bee entrance faces away from the prevailing wind (Nightingale, 1976).

 

The traditional hives are placed high up on trees by means of a hooked pole or placing them between suitable tree branches and left to be occupied by wild swarms. Honey harvesting is normally done at night when the bees are less aggressive.  Hives can be worked up the trees or lowered to the ground by means of a rope. The honey is usually stored away from the hive entrance. This is the end from which the harvesting starts, moving towards the opposite side. Smoke, which has the effect of mollifying the usually aggressive bees, is provided by a traditional torch made of dried bark or other suitable material. Once the honey has been removed, the hive is hoisted back to its place. Since this type of hive has only one chamber with fixed combs, the honey, wax, pollen and brood are all removed together during harvesting greatly compromising the quality of the final product.

 

Very little or no routine colony management is practiced under the traditional system. Colony management is often limited to harvesting honey and rebating hives with suitable bee lures to enhance occupation. The harvesting methods employed by traditional beekeepers may lead to the loss of a substantial number of bees, thus reducing the strength of individual colonies and the potential number of feral swarms. The marketable honey quantity is affected by quality, which in turn is affected by simple, sometimes crude methods in handling bees (Kigatiira, 1976).

 

2.8.2 Modern Technology

Modern beekeeping practice involves the use of improved technologies which are easy to manipulate and manage. The main types of hives used are the movable comb hives and the movable frame hive. Other accessories that go together with modern beekeeping include the catcher box, protective clothing, smoker, hive tool, bee brush and the honey extracting and refining equipment. Some management practices are also considered as part of the improved beekeeping technology and include seasonal management, routine colony inspection, colony division, artificial feeding and pest control.

2.8.2.1 Movable-comb hives

The invention of the movable-comb hive is the work of the ancient Greek beekeepers who used a basket hive in which a series of bars were used to form the top of the hive (Mann, 1976). These types of hives are designed to allow the combs to be removed, inspected and returned back to the hive. The Kenya Top Bar Hive (KTBH) designed in the 1970s, is a modification of the Greek basket hive with movable, interchangeable top bars. The hive is basically a one chamber wooden box, with the sides sloping inwards at an angle of 120 degrees to the horizontal. This design ensures that the bees do not attach combs to the sides of the hive.

 

The hive accommodates 26 top-bars which are 48 cm long and 3.2 cm wide with the underside fitted with a strip of beeswax to act as a starter comb and guide the bees in comb construction. The lid is made of a timber frame covered with a light gauge galvanized iron sheet. The KTBH has a number of advantages over the traditional log hive namely:

• Combs can be easily removed for inspection and returned to the hive.
• Honey combs can be removed without interfering with the brood nest.
• Honey quality is improved since pollen and brood combs are not included with the harvested honey.
• Improved pest control. 
• The low hanging height makes it easier and faster to carry out various management operations.

 

Despite these advantages, this technology has a number of weaknesses as highlighted below:

• The attachment of the comb to the top bar is weak and breaks easily.
• The hive can fill very fast during a good honey flow season leading to overcrowding and swarming of the colony since the volume of the hive is fixed.
• Control of hive temperature is difficult due to the low hanging height (one meter from the ground) and this has resulted in low occupation rates in the ASAL due to frequent absconding.

 

2.8.2.2 Movable-frame hives

These are the most advanced hives in design and are used by commercial beekeepers in many parts of the world (Patterson, 2006). The first movable frame hive was designed by an American clergyman, the Revered Langstroth in 1851. This invention by Langstroth, and the patenting of the artificial comb foundation by Melhring in 1857, revolutionalized beekeeping and put it on a commercial footing (Mann, 1976). The frames can be removed, inspected and when full of honey, extracted and returned to the hive for the bees to continue filling with honey. Other frame hives include the Dadant developed also in the USA and the Smith hive developed in the UK.

The basic principle in all frame hives is the same in that the frames are movable and the bee brood and the honey are kept in separate chambers. The Langstroth hive is the most popular of the frame hives and is used in various parts of Kenya. The key components of this hive include the bottom board, the brood chamber, queen excluder and the top cover.

 

The Langstroth has a number of advantages compared to other types of hives found locally. The frames make the combs strong hence minimizing breakage. Moreover, the honey can be extracted and the frames returned to the hive leading to higher yields and honey quality is enhanced due to the use of a queen excluder. However, they are more expensive than the traditional or top bar hives. The Langstroth hives require more management skills and the comb foundation frames are prone to attack by wax moth.

 

Some multi-chambered top bar hives have also been developed and are in use in various parts of the country. These hives have some advantage over the single chamber type in that the honey and the brood are kept separate. The hives have similar measurements to the Langstroth but have top bars instead of the standard frames and are in some instances wrongly referred to as ‘Top-Bar Langstroth’.

 

Frame hives have been successful in the cooler parts of Africa where there is an abundance of bee forage and are managed by experienced beekeepers. However, they have had limited success in general and in most cases, the yields obtained do not justify the additional capital and management requirements (Patterson, 2006). The last decade has seen a tremendous growth in the number of Langstroth hives in Kenya. However these hives are not necessarily better than either the traditional or top bar hives and their potential for better yields and quality depends very much on good management practices (Carroll, 2006). Nevertheless, use of modern beekeeping technology encourages better bee management and aims at higher success than can be hoped for by the exclusive use of traditional methods (Kigatiira, 1976).

 

2.8.3 Housed Beehives

Rearing bees in houses is a new approach to beekeeping in the ASAL. Bees are kept in houses to protect them from adverse weather, predators and vandals. The bees can access their hives through holes in the wall that lead to each hive. According to Paterson (2006), bees are more manageable when kept in a bee house because the more aggressive guard bees will remain outside the bee house while the hive is being manipulated. Another advantage is that this method of beekeeping has the possibility of increasing the carrying capacity of small pieces of land since a small house can take up to ten hives. However, it should be noted that a secure bee house can be expensive to construct.

CHAPTER THREE

METHODOLOGY

3.1 GEOGRAPHICAL LOCATION

This study was conducted in Kikumbulyu and Ngwata Locations in Kibwezi Division and Utithi Location in Machinery Division of Kibwezi District, Eastern Province of Kenya (Figure 3.1). The district lies between latitudes 2º 6′S and 3ºS, and longitudes 37º 36′E and 38º 30′E respectively. Situated about 200km south east of Nairobi, Kibwezi is a typical semi-arid district and one among the 12 districts that comprise the Ukambani region. The district borders Kajiado District to the west, Taita District to the south, Mutomo to the east and Nzaui and Makueni Districts to the north. The district covers an area of 3954.6km² (GoK, 2008c).

 

3.2 TOPOGRAPHY AND CLIMATE

The major land form in the study area includes the Chyulu hills which lie along the south western border of the district. The land rises slightly below 600m above sea level to the south to about 1100m in the northern part of the district. Kibwezi receives bimodal rainfall with an average of 600mm annually and an average annual temperature of 23⁰ C. Long rains are received from March to May and short rains from November to early January. Sixty per cent of the annual rainfall in the study area is received during the short rains, with the long and dry season rains contributing 37% and 3% of the annual rainfall, respectively (Gichuki, 2000). Short rains are therefore more reliable in terms of time and spatial distribution than long rains.

Figure 3.1: Location of the Study Areas

Source: Makueni District report, 2008

 

 

3.3 GEOLOGY

The geology of the area comprises recent volcanic rocks and a basement complex system. Granite rocks are found around the Chyulu hills which is a major water catchment area in the district. The rest of the area is almost entirely built up of recent lava flows and some volcanic cones. The flood plains and bottom lands occupy only minor portions (Mganga, 2009). The rocks are broadly sub-divided into basement system rocks, volcanic and superficial deposits (Touber, 1983). Volcanic activity significantly enriched large areas of basement system rocks with volcanic material. This enrichment coincided with major volcanic activities in Pleistocene and recent times (Musimba et al., 2004).

 

The soils comprise ferrasols, nitisols, luvisols and cambisols (Touber, 1983). The ferrasols are found on flat interfluves and are well drained. Most of these soils are compact and have a massive structure with strong surface sealing which causes much runoff during heavy rains. The flood plains and bottom lands in the district have soils which range from calcareous and non-saline to extremely calcareous and saline. Pockets of black cotton soils rich in clay content can also be found scattered in the district (Musimba et al., 2004).

 

3.4 VEGETATION

Vegetation in the area is influenced by a number of factors such as climate, geology, soil type and presence or absence of ground water (Gachimbi, 1990). Kibwezi District is a typical semi-arid savanna dominated by Adansonia digitata, Commiphora, Acacia and allied genera, mainly of shrubby habitat (Touber, 1983). Perennial grasses include Cenchrus ciliaris, Enteropogon macrostachyus Chloris roxburghiana and Pennisetum mezianum. However, much of the original vegetation has been modified through cutting of trees, clearing, burning and grazing.

3.5 SETTLEMENT PATTERNS

The district is mainly occupied by the Kamba community which forms approximately 94% of the total population. However there is a substantial percentage of people from other communities especially in some of the major towns of the district. According to the 1999 population and housing census, Kibwezi District registered 200,616 people with this number projected to reach 258,120 people by the year 2008 (GoK, 2008c).

 

The district is generally sparsely populated with an average population density of 108 persons per km². Machinery Division has the highest population density of 175 per km² with the lowest being Makindu with 73 persons per km².  The high population density and consequent scarcity of land in the larger Machakos District has led to increased migration by people seeking new settlements in Kibwezi. This situation is expected to increase the population density to 121 persons per km² by the year 2012 (GoK, 2008c).

 

3.6 ECONOMIC ACTIVITIES

Majority of the people in the district depend on agriculture and livestock related activities for their livelihood. The main food crops include; sorghum, millet, cowpeas, pigeon peas and beans. Sisal is a major cash crop in the area. Livestock herds are composed of cattle, goats and sheep. Rearing of indigenous chicken and beekeeping are also important farm enterprises. Majority of the farmers (97%) keep poultry, a few (9%) keep donkeys for transport and about 35% keep bees using the traditional log hives (Mutungi, 1998).

 

Irrigation, mainly for horticultural crops, is undertaken along the Athi River notably in Ngwata Location. However, most of those involved in this type of farming are people who have migrated from other places including Nairobi and have leased land from the local inhabitants.  Other small scale income generating charcoal burning is a major activity especially in Utithi Location.

 

3.7 METHODS OF DATA COLLECTION

3.7.1 Types of Data

Two types of data, i.e., primary and secondary data were collected during the study. Primary data were collected from farmers through formal interviews by administering questionnaires and on- the- spot field observations. In addition, a focus group discussion was conducted with a group of farmers from the three locations and pertinent issues concerning adoption of beekeeping technology in the study area were delved into. Secondary data were sourced from previous published research reports, NGOs and relevant government departments. Local leaders especially the village elders and assistant chiefs were particularly helpful.

 

3.7.2 Preparation and Administration of Questionnaire

A draft questionnaire was prepared and pre-tested in a preliminary survey conducted in 15 households before the main study. These households belonged to the same area of survey but did not come from the main study sample. The main reason for the preliminary survey was to test the relevance of the questions. This was in an effort to ensure that only relevant and well phrased questions were to be posed to the interviewees during the main study. Transects used during the main study were also established during the preliminary survey. The questionnaire incorporated dichotomous, multiple choice and open-ended questions. This was necessary due to the diverse nature of the issues that were being investigated.

3.7.3 Recruitment and Training of Enumerators

Four enumerators with at least an ordinary level of education were recruited from the local community to assist in data collection. This was to ensure that there was no language barrier and that the information obtained would be as accurate as possible. Being residents of the area, the enumerators knew the terrain of the study area very well and easily created rapport with the interviewees. Training on the subject matter and on techniques of administering questionnaires was provided to the enumerators before embarking on the exercise. The researcher worked with and monitored each enumerator during the entire period of collecting the data.

 

3.7.4 Interviews

Interviews were conducted in the morning and afternoon sessions for five days a week with a maximum of four respondents per enumerator per day. Efforts were made to keep the interview as short as possible while at the same time capturing all the desired information. Questions were posed in the local dialect and the answers recorded in English. The sequence of the questions was such that those that would easily establish rapport with the farmers came first while the more sensitive questions came towards the end of the interview.

 

3.7.5 Sampling Technique

A sample of 170 households was interviewed from three locations of Utithi, Ngwata and Kikumbulyu. The size of the sample depends on various variables, e.g., the availability of funds, time, infrastructure and terrain, and not necessarily on the total population. Sampling was such that each location provided about the same number of households. The locations were purposively selected based on the presence of adopters of beekeeping technologies.

 

Motorable tracks were used as transects with each location constituting about a 40-50km long transect.  Systematic sampling was then taken at every other homestead along the identified transect. In Utithi Location, the transect ran from ‘Bosnia’ village up the Chyulu hills to Kinyambu market along the Nairobi - Mombasa highway. The transect in Ngwata Location ran from Machinery trading centre to the river Athi. In Kikumbulyu Location, it was from Kisayani market to the river Athi, via Kathyka market. The sampled farmers were then stratified into adopters and non-adopters of beekeeping technologies. Adopters were further categorized as either adopter of modern or traditional beekeeping technologies.

 

3.8 METHODS OF DATA ANALYSIS

3.8.1 Descriptive Analysis

Data collected through personal interviews and group discussions were subjected to statistical analysis using the Statistical Package for Social Sciences (SPSS) and summarized in terms of percentages, frequencies and charts.

 

3.8.2 Regression Analyses

Estimation of productivity and the establishment of factors affecting adoption of beekeeping technology were done by employing methods of regression analysis. The Cobb-Douglas production function was used to estimate productivity levels while binary logit model was used for determinants of adoption.

 

3.8.2.1 The Cobb-Douglas production function

In economics, the Cobb-Douglas production function is widely used to represent the relationship of an output to inputs. In formulating the model, Cobb and Douglas considered a simplified view of the economy in which production output is determined by the amount of labour involved and the amount of capital invested (Tan, 2008). The general form of the function is expressed as:

Where:

Q = total production (the monetary value of all goods produced in a year).

L = labour input (the total number of person-hours worked in a year).

K = capital input (the monetary worth of all machinery, equipment, and buildings).

b = total factor productivity.

a and b are the output elasticities of labour and capital, respectively and are determined by the available technology.

 

Output elasticity measures the responsiveness of output to a change in levels of either labour or capital used in production, ceteris paribus. Further, if a+ b =1, the production function has constant returns to scale. However, if a+ b < 1, returns to scale are decreasing, and if a+ b > 1, then returns to scale are increasing (Tan, 2008).

 

In this study, the differences in output between modern technology on the one hand and traditional technology on the other were estimated using the Cobb-Douglas production function. The effects of labour, size of land holding, capital input, practical hands-on experience and managerial skills on honey output were estimated using the production function.

 

 

Following Urama and Mwendera (2005), a stochastic form of Cobb-Douglas production function was used as specified below:

Where:

Q = Output.

A = Scale factor (level of technology).

X_1, X_2, X₃ = Factors of production (e.g. labour, land and variable costs).

m = Error term.

e = Base of natural logarithm.

b_1, β_{2, . . .,} β₃ = Parameter estimates (coefficients).

 

However, in linear regression analysis, the production function may be transformed into a logarithmic form for convenience and ease of computation. Hence the function can be expressed as:

                            (1)

Where:

Q = honey output in kg per year.

a = intercept.

X₁ = amount of labour (Kshs per year) measured as the value of the number of person-hours per household per year.

X₂ = area of land owned per household in hectares.

X₃ = value of beekeeping equipment owned at un-depreciated initial cost (Kshs per year).

X₄ = managerial skills with the level of education as a proxy indicator.

X₅ = practical experience represented by number of years of practicing beekeeping.

μ = error term (takes into account other variables affecting honey production).

b_1, β_{2, . . .,} β₅ = parameter estimates (coefficients) and indicate the elasticity of output with respect to changes in the input variables.

 

3.8.2.2 Variables included in the Cobb-Douglas production function

In this study, the factors of production to be included in the production function were selected on the basis of the availability of data collected during the survey. Those included in the model are outlined below.

 

Output: This is the dependent variable and was measured by taking into account the total amount of honey produced per household in one year in kilograms.

 

Land: This is a primary factor of production and it was measured in hectares owned. The forage available for honey production and the necessary space for setting up the apiaries is dependent on individual household land holding. Farmers with bigger land holding are therefore expected to have higher outputs.

 

Labour: This was estimated by taking into account the various seasonal management operations undertaken by the beekeepers within the year. Labour inputs were considered for operations such as routine colony inspection, apiary maintenance, colony division and honey harvesting. It was measured as the value of the cumulative number of person-hours spent on these operations and then valued at the prevailing rates for the study area. A higher labour input is expected to improve yields.

 

Capital: This was calculated on the basis of the number and type of beekeeping equipment used in each household such as hives, protective clothing, smokers and hives tools. The initial un-depreciated value of this equipment was taken to be a fairly good representative of the farmers’ capital input.

 

Farmer’s practical experience: This was measured by the number of years the respondents had practiced beekeeping and was used to rate the respondents experience and competence. It was assumed that experience leads to enhanced skills and better management of the enterprise which would result in higher productivity.

 

Managerial skills: The educational level of the household head was taken as a good proxy indicator of management abilities. It was assumed that those who had attained secondary school level and above were better managers than those without formal education or those with only primary level education. Education enhances farmers’ ability to access and use information relevant to the enterprise. A higher level of education is therefore expected to increase the production level.

 

3.8.2.3 Logit model

Logistic regression allows one to predict a discrete outcome, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. Generally, the dependent or response variable is dichotomous, such as adoption or non-adoption. Various adoption studies on crop, livestock, and soil conservation have used Probit and Logit models for identifying the impact of independent variables on dependent variables.

 

The outputs of Probit and Logit models are usually similar (Shariff et al., 2009; Aldrich and Nelson, 1984). These authors observed that, there is little to distinguish between Logit and Probit models since both curves are so similar as to yield essentially identical results.  Even though their outputs are similar, the Logit model is computationally easier than the Probit and was therefore used in this study to evaluate the decision by farmers to adopt or not to adopt beekeeping technologies. The base model is represented by the general equation:

Where:

i = 1, 2,…, N

Y_i = the dichotomous (dummy) dependent variable for household i representing adoption or non-adoption.

X_i = the i^th observable explanatory variable.

a = captures the household specific unobservable explanatory variables.

b = the estimation parameter.

m_i = the error term.

 

For ease of estimation of the logit model, the dependent variable is normally transformed  by taking natural logarithms of both sides of the base equation to yield the ‘’log odds’’.

The model can thus be written as shown:

Where:

L_i = the log of the odds ratio, is the Logit.

P_i = is the probability of an event occurring; (in this case adoption of beekeeping technology).

1-P_i = is the probability of the event not occurring (in this case non-adoption of beekeeping technology).

 

3.8.2.4. Selection of variables used in the Logit model

Adoption is considered discrete rather than continuous in nature such that the dependent variable takes a limited set of values. The dependent variable in this case can be characterized as binary, taking the value of 0 or 1. The dependent variable thus takes the value of 1 if technology has been adopted and 0 if not. The independent variables that influence the adoption of beekeeping technology were selected based on literature, survey results and personal experience. The hypothesized variables are briefly discussed below.

 

Age of household head: This is a continuous variable and was measured by ranking on a scale of 1-3 with the highest figure representing the oldest category. Literature reveals that aged persons are less prone to change and reluctant to adopt new technology in their farms while young people are more flexible (Rahman, 2007). Therefore, it was anticipated that younger people would adopt beekeeping more than the elderly.

 

Level of education of household head: Education improves the decision making process (Feder et al., 1985). Education level was therefore hypothesized to positively influence the adoption of technologies. This variable was measured by ranking using ranges from 1-4, with the largest representing the highest level of education attained.

Gender of household head: Depending on the nature of the technology, female and male farmers are likely to play different roles in technology adoption. The effect of this variable may either be positive or negative. The variable was measured by allocating male-headed households a value of one and female-headed a value of zero. It was hypothesized that male-headed household would have a higher adoption rate than female-headed households.

 

Household size: This variable was measured by the total number of household resident members. Farmers with large family size might significantly adopt the technology, to satisfy the needs of their families. They are also able to provide the extra labour that the technology may demand. Hence, it was hypothesized that the larger the household size, the higher the likelihood of beekeeping adoption.

 

Land holding: Land was measured by ranking on a scale of 1-5, with the largest representing the highest acreage. Farmers with big tracts of land are considered wealthy and can therefore afford to invest in new ventures in an effort to fully utilize their land and to diversify their income base. It was hypothesized that the larger the land holding, the higher the probability of beekeeping adoption.

 

Group membership: Membership to self-help groups could influence the adoption of technologies since most technologies are introduced through organized groups. This variable was measured by allocating membership to self-help groups a value of one and zero for non-membership. It was hypothesized that belonging to a self-help group would have a positive influence on adoption of beekeeping technologies.

 

Livestock holding: Livestock is an important source of income, food and draught power for households in the study area. The number of livestock is an important indicator of wealth status of the respondents in the study area. The livestock variable was measured in Tropical Livestock Units (TLU). Livestock holdings were hypothesized to be positively related to the adoption of agricultural technologies because it serves as proxy for wealth status (Freeman et al., 1996).

 

Participation in off-farm activities: Additional income earned from activities outside the farm e.g. engaging in business or employment increases the farmers’ financial capacity and hence the probability of investing in new technologies (Negash, 2007). It is, therefore, hypothesized to affect adoption positively. It is treated as a dummy variable taking a value of one if a household head participated in off-farm income generating activities and zero otherwise.

 

Access to extension services: Extension services have a direct influence on the adoption behaviour of farmers. Feder et al. (1985) noted that extension efforts increase the probability of adoption of new technologies. It was treated as a dummy variable, assuming a value of one if the farmer had access to extension services and zero otherwise. In this study, it was hypothesized that farmers who had contact with extension service providers would more likely adopt beekeeping technologies.

 

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 INTRODUCTION

In this chapter, the results of the study are represented and discussed in two parts. The first section covers the general descriptive analysis and the beekeeping situation in the study area. The second part is on the regression analysis and focuses on estimation of productivity levels and the factors affecting the adoption of beekeeping technology.

 

4.2 DESCRIPTIVE ANALYSIS

4.2.1 Demographic Characteristics of the Respondents

Of the 170 respondents, 51.2% and 48.8% were male and female respectively, with 51.8% falling in the 31-50 years age bracket. Further, 52.4% of the respondents had attained at least a primary level of education. The study revealed that most respondents are agro-pastoralists with 79.4% involved in crop and livestock production.

 

The majority of the respondents (37.1%) own between 0.4 and 2 hectares of land.  The household size averaged 7 people with 81.8% of the respondents indicating that working on their farms on a full time basis was their main occupation.  Self-help groups are active in the study area with 50.6% of the respondents being members of such groups. However, it was observed that more women were members of these groups compared to men. Women who were members accounted for 58% compared to men whose membership to self-help groups stood at 41.9%. Table 1 summarizes some main socio economic characteristics of the sampled households.

 

Table 1: Summary Statistics of Some Continuous Variables

Variable	Frequency (n=170)	Percentage
Age Years
18-30	25	14.7
31-50	88	51.8
51 and above	57	33.5
Education Level
None	18	10.6
Primary	89	52.4
Secondary	40	23.5
Tertiary	23	13.5
Occupation
Farming	139	81.8
Employment	3	1.8
Farming and business	20	11.8
Farming and employment	8	4.7

 

4.2.2 Socio-Economic Characteristics and Adoption of Beekeeping Technology

The link between household socio-economic characteristics and beekeeping technology adoption was examined with respect to characteristics such as age and gender of a household head, level of education of a household head, farm size, access to extension services, access to credit and membership to self-help groups. The community inhabiting the study area is recognized as one of the most knowledgeable and progressive on many aspects of apiculture. The study found out that 90 out of the 170 respondents, representing 52.9%, are beekeepers. This is an indication that beekeeping is still regarded as an important socio- economic undertaking in the area.

 

4.2.2.1 Age of household head

The survey found that most of the adopters (47.8%) were in the age category of 31-50 years. The rest of the beekeepers were in the 51 years and above (37.8%), and 18-30 year (14.4%) categories respectively. These findings are consistent with the results of Mwanthi (2009) who found out that the adoption of range resource management technologies in Kibwezi was by those in the age category of 31-50 years.

 

This is an indication that advancing age negatively influences the adoption of new technology. The results also confirm recent observations that the youth in the 18-30 year age bracket may often have a negative attitude towards beekeeping and are reluctant to take up the practice. This has been suggested as one of the reasons why beekeeping is on the decline in many parts of the country.

 

4.2.2.2 Level of education of household head

According to the survey results, 10.6% of the respondents had no formal education, 52.4% had attained basic primary education, 23.5% had secondary education and 13.5% had tertiary level education. Among those who had acquired primary level education, 55.6% were adopters of beekeeping technology while 48.8% were non-adopters. For those who had attained secondary education, 14.4% were adopters while 33.8% were non- adopters. This indicates that a higher education did not necessarily translate into a higher adoption rate. Since beekeeping is a traditional art among the local community, adoption is based on skills which are passed-on from generation to generation rather than through formal education. These observations are summarized in Table 2.

Table 2: Education Level and Adoption of Beekeeping Technology

Education level	Frequency (n=170)	% Total	% Adopters	% Non-adopters
None	18	10.6	7.1	3.5
Primary	89	52.4	29.4	22.9
Secondary	40	23.5	7.6	15.9
Tertiary	23	13.5	8.8	4.7

 

4.2.2.3 Household farm size

Farmers were grouped into five categories based on the sizes of their individual land parcels. These parcels of land ranged from 0.4 hectares at the lowest to more than 8 hectares on the highest. Results indicate that a majority of the respondents (37.1%) own between 0.4 and 2 hectares.  Those owning between 2.4 and 4 hectares are 29.4% while only 8.8% own tracts of land larger than 8 hectares. Of the 37.1% who own between 0.4 to 2 hectares, 14.1% and 22.9% are adopters and non adopters of beekeeping technology respectively. (Table 3).

 

Results show that 11.8% of the farmers sampled own between six and eight hectares of land. Out of this group, 9.4% were found to be adopters and 2.4% were non-adopters. This analysis shows that land size is an important factor when it comes to the adoption of beekeeping, as 16% of the non-adopters cited small land holdings as one of the reasons for not taking up the technology

 

Table 3: Land Size and Adoption of Beekeeping

Land size (Hectares)	Frequency (n=170	% Total	% Adopters	% Non-adopters
0.4 -2.0	63	7.1	14.1	22.9
2.4- 4.0	50	29.4	14.7	14.7
4.4- 6.0	22	12.9	7.6	5.3
6.4- 8.0	20	11.8	9.4	2.4
8.4 and above	15	8.8	7.1	1.8

 

4.2.2.4 Membership to self-help groups

The survey results show that 53.3% of the beekeepers are members of a self-help group. Of the total number of beekeepers who were members of these groups, 36.7% and 16.7% are adopters of traditional and modern technologies respectively. However further analysis of these results indicate that, of 46.7% of beekeepers who are not members of a SHG, 38.9% use  traditional technology while 7.8% are adopters of modern technology. This is an indication that being a member of a SHG does not influence the adoption of traditional beekeeping but it may have a significant influence on the adoption of modern beekeeping. This is because modern beekeeping is usually introduced through groups as opposed to traditional beekeeping which is passed-on from one generation to the next along family lines.

 

Degu et al. (2002) noted that membership to an association or group is an important factor in technology adoption. Membership to groups enhances cooperation and interaction within the group and also between the group and other players e.g. donors and extension service providers. The study results indicate that self-help groups are also involved in social and financial activities which help in building the capacity of their members.

 

 

4.2.2.5 Gender of household head

Most of the households sampled were male-headed (91.2%) while the rest were female-headed. Of the male-headed households, 57% were adopters while 46% were non adopters. Among the female-headed households, 42% were adopters while 58% were non-adopters. These results indicate that a larger proportion of adopters were among the male-headed households compared to the female-headed households.

 

This trend may be attributed to the cultural norms among the local community where beekeeping is still strongly regarded as a man’s job, and more so among the traditional beekeepers. These hives are normally placed high up the trees and the owner climbs up to harvest the honey usually at night. This operation would under normal circumstances be viewed as not suitable to women beekeepers.

 

It can, therefore, be concluded that the gender of the household head is a likely determinant of beekeeping technology adoption. This observation is consistent with the findings of Volenzo (2006), who did some work on the factors affecting organic farming in Western Kenya. Also, Ouma et al. (2002) found out that gender was significant in explaining the adoption of improved maize variety in Embu District, Eastern Kenya.

 

4.2.2.6 Access to extension services

 For those who had taken up beekeeping, 23.3% had been visited by agricultural extension providers and had benefited from on-farm extension in the previous one year. Among the recipients of extension services, 22.2% were adopters of modern technology while the rest (1.1%) were adopters of traditional technology. Therefore a large proportion of those who had received extension services were adopters of modern technology. This conforms to observations by Ouma et al. (2002) that access to extension services plays an important role in influencing the adoption of agricultural innovations.

 

4.2.2.7 Access to credit

 A mere 1% of the adopters had received credit for beekeeping activities in the previous one year. This is a clear indication that lack of credit could be a key constraint to the adoption of modern beekeeping technology in the area. Feder et al. (1985) observed that credit programmes may enable farmers to purchase inputs or acquire physical capital needed for technology adoption. Credit may be essential to acquire farm technologies like modern beekeeping which the farmers perceive to be a costly activity to engage in (Workneh, 2007).

 

4.2.2.8 Access to market

Marketing plays an important role in agricultural production and adoption of technology (Mwanthi, 2009). Lack of market or low prices may act as a disincentive towards the adoption of technology.  Results show the main market outlet for honey was the middle-men accounting for 63.6%. Others were local consumers and deliveries to the local refinery, accounting for 30.9% and 5.5% respectively.

 

4.2.2.9 Training on bee management

Among the adopters of beekeeping technology, only 30% had received some training in bee management in the previous one year. Of those who had been trained, 7.8% were adopters of traditional technology while a majority (22.2%) had adopted modern technology. These results suggest that acquisition of technical skills and knowledge on bee farming were likely to influence the adoption of modern beekeeping technology. This observation is consistent with that of Zegaye et al. (2001) who reported that training contributed positively to farmers’ adoption decision. Table 4 shows a summary of these observations.

 

Table 4: Summary Statistics of Some Non-Continuous Variables

Variables%	%Total	Technology adopters (%)
		Traditional	Modern
Beekeeping Training (%Yes)	30.2	7.8	22.2
Access to extension (%Yes)	23.3	1.1	22.2
Access to credit (%Yes)	1.1	1.1	0.0
Membership to SHGs (%Yes)	53.3	36.7	16.7

 

4.2.3 Types of Beekeeping Technologies Adopted

The study found that two types of technology, i.e. traditional and modern beekeeping have been adopted by farmers in Kibwezi.  While traditional beekeeping using log hives has been practiced in the area for hundreds of years, modern practices of rearing bees in improved hives was first introduced in the area in the 1970’s through women groups by the Council for Human Ecology and Action Aid Kenya (Kigatiira, 1976).

 

During the survey, 75.6% of the adopters were found to be using traditional technology, while 24.4% were using modern technology. Further analysis reveals that among adopters of modern technology, 23.3% use Langstroth hives and only 1.1% use Kenya Top Bar Hives (KTBH). This finding confirms field observations which indicate the growing unpopularity of the KTBH, especially in hot areas, due to high rates of absconding associated with this type of hive as a result of unbearable hive temperatures.

 

4.2.4 Factors Affecting Choice of Various Technologies

Respondents gave varied reasons why they prefer a particular type of technology. These included affordability, availability, management regime, size of land holding, productivity level and quality of the products. Low cost, availability, and ease of occupation by honey bees were the most important considerations for adopters of traditional technology. Adopters of improved technology cited ease of management, less land requirement and quality of hive products as the major reasons for using the technology.

 

4.2.5 Traditional technology

Beekeepers cited various reasons for using the traditional technology. About 36% mentioned the low management regime that goes into the practice. About 29% cited its affordability and availability as the main advantages of this technology. Since the materials and skills for making these hives are locally available, a log hive may cost three times less than a modern hive. Other factors include the longevity of the log hives (10%) and the fact that these hives are hung high up the trees where they are usually occupied more easily by swarms (25%). Mutungi (1998) noted that tree apiaries are safe from fires, floods and attacks from pests and predators. Bees from such hives cause less disturbance to pedestrians, traffic and farming activities. Hives placed high on trees are cooler during the day which enhances occupation by bees than those closer to the ground. Figure 4.1 illustrates the major considerations on choice of type of technology.

 

Figure 4.1: Households Reporting Reasons for Choosing TLH

 

4.2.6 Modern technology

In this study, adoption of modern beekeeping technology was marked by use of improved hives, e.g. the Kenya Top Bar Hive (KTBH), the Langstroth hive and accessories, such as bee protective clothing, smoker, bee-brush and hive tool. Among the 90 adopters of beekeeping, about 24% were practicing modern beekeeping with a majority (36.4%) in this category citing the small land area required combined with ease of colony management as the major advantages of the technology. Farmers reported that since these hives are placed only about two meters from the ground, they find it much easier and more convenient to work the bees compared to the traditional hives, which are hung high up the trees. Those reporting high yield and improved quality of products as reasons for choosing the technology accounted for 14%.

Only 4.5% of the adopters of modern technology cited the desire to acquire new knowledge as the reason for adopting the technology. This is an indication that few farmers will adopt a technology for its sake, but rather a new technology must come with tangible benefits.

4.2.7 Other Aspects Analysed Using Descriptive Statistics

The study also analysed other issues pertaining to the beekeeping industry in the study area. These included bee management, occupation rates of various types of hives, number of harvests and yield and presence of pests and predators.

 

4.2.7.1 Bee management practices

The survey results show that 86.7% of adopters practice some form of bee management. Of the farmers who use modern hives, 95% were found to undertake bee management while 84% of those using traditional technology undertook some form of bee management. The management aspects practiced by a majority (45%) of the adopters were found to be colony inspection, apiary management and pest control. Only 1.3% of the beekeepers were found to be feeding their colonies despite that the area suffers frequent droughts. This could be one of the reasons of the observed high rates of migration by the honey bee colonies.

 

4.2.7.2 Use of accessories

Effective bee colony management requires the use of appropriate accessories, e.g. the protective clothing, bee smoker, bee brush and hive tool. Lack of these equipment, and especially protective clothing, has been a big hindrance to the adoption of beekeeping and the resultant low productivity.  This is due to the fact that the local bee species is very aggressive and tends to sting excessively. This aggressive behaviour is the main cause of api-phobia, one of the leading drawbacks to beekeeping in the country. The results show that only 25.6% of the adopters were using the accessories, with 15.6% being adopters of modern technology and the rest using the traditional technology. The major reasons behind the limited use of beekeeping accessories were found to be mainly their unavailability and high cost with about 67% of the beekeepers citing these as the key limiting factors.

 

4.2.7.3 Hive occupation

The study results indicate that a high proportion of the beekeepers (84.4%) reported having experienced a problem of persistently low occupation of their hives by honey bee colonies during the previous one year. Of those reporting low occupation, 61.1% and 23.3% were adopters of traditional and modern technologies respectively. The main factors behind the observed low occupancy were the behavioural aspects of honey bees mostly absconding and migration. Some of these factors have been discussed by Mwangi (1985). About 90% of the beekeepers reported having lost colonies as a result of absconding or migration. Further analysis of the results indicates that within the type of technology used, modern technology had higher levels of low occupation of 95% compared to 81% in the traditional technology. This observation is consistent with field observations which show that traditional log hives tend to be better occupied by honey bee colonies compared to modern hives.

 

This observation is similar to that of Mutungi (1998) who observed that, despite advantages associated with modern hives (e.g. ease of manipulation and higher yields), bees do not like them due to the high temperatures leading to absconding. Among the modern hives, the KTBH was the most affected by low occupation compared to the Langstroth. None of the KTBH was found to be occupied raising concern pertaining to the suitability of this hive, especially in the drier parts of the country.

 

 

4.2.7.4 Number of harvests and yield

The number of harvests in the previous one year ranged from nil to a maximum of four times. About 50% of the beekeepers reported having harvested some honey at least twice and only 3.2% reported harvesting four times in the previous one year.  Those reporting four harvests per year were all found to be using traditional technology. This could be attributed to the higher occupation rates of the traditional log hives and the fact that the traditional beekeepers in the area are more experienced compared to those using modern technology which was only introduced in the area in the last 3-5 years.

 

The total honey yield per household ranged from zero to 203kg per year with a mean yield of 16kg. A majority (46.4%) of the beekeepers using traditional technology reported that they did not harvest any honey at all in the previous one year. Those who harvested less than 10kg were 21.4% while about 11% got between 41-50kg. For the adopters of modern technology, about 73% reported no harvests at all and 9% reported getting yields of up to 10kg. The annual average harvest per household was 17.4kg for those using traditional technology and 6.2kg for modern technology. The low yields reported could be attributed to the long drought that had ravaged the area for close to two years, leading to the migration of honey bee colonies.

 

4.2.7.5 Presence of pests and predators

The existences of honeybee pests and predators can be an obstacle in the adoption of beekeeping technology. Some pests such as the safari ants attack honeybees and consume the hive products while predators such as the honey badger can cause serious damage to the hives leading to huge losses to the farmer. All adopters reported the existence of pests and predators on their farms. This was a common problem affecting both categories of technology adopters. Table 5 shows the major pests and predators ranked depending on the extent of damage caused to the honey bee colonies.

 

Table 5: Major Honey Bee Pests and Predators

Pest and Predator	Rank
Honey badger	1
Safari ants	2
Black ants	3
Wax moth	4
Lizards	5
Baboons and Monkeys	6
Birds	7
Kabare rats	8
Snakes	9

 

4.2.7.6 Absconding and migration of honey bee colonies

Absconding refers to the sudden departure of the whole colony from a hive while migration is the seasonal movement of bees from one area to another. These two phenomena are not desirable since they lead to loss of bee colonies and hence income by the farmer. The results indicate that this is a major problem in the area with 88.9% of the adopters reporting to have lost colonies in the previous one year. This problem affects all adopters regardless of the type of technology adopted.  The main causes of absconding and migration were lack of forage and water, attack by pests and predators, poor harvesting methods and the effect of pesticides accounting for 66.7%, 17.3%, 14.8% and 1.2% respectively.

 

4.2.7.7 Value addition

The value of primary hive products can be improved by processing, purification, packaging and labelling.  Adding value to hive products plays an important role in enterprise development and employment creation. The technologies used in honey processing include extraction, pressing and straining. The majority of producers depend on simple methods such as cloth or net straining. The results indicate that 45.9% of the beekeepers processed their honey.

 

Within the category of adopters of modern technology, 93.1% undertook honey processing, while in the category using traditional methods, 45.6% undertook processing.  Simple sieving accounted for 87.9% while the water-bath method and net-and -bucket method accounted for 9.1% and 3% respectively. These results imply that most of the beekeepers sell their honey in raw form which fetches a lower market price than the processed honey. The adoption of modern beekeeping also tended to encourage value-addition and improved quality products which would ultimately translate into better returns to the farmers.

 

4.2.7.8 Non-cash benefits from beekeeping

Of the adopters, 75.6% acknowledged that, cash income aside, there were many other benefits accruing from adopting beekeeping. A majority (61.8%) mentioned honey, the primary product of beekeeping to be a source of nutrition while honey as a source of medicine, bees for crop pollination, and honey as an ingredient during socio-cultural events followed. These results are illustrated in Figure 4.2.

 

 

 

 

 

 

Figure 4.2: Households Reporting on Major Non-Cash Benefits of Beekeeping

 

 

4.2.8 Constraints to the Adoption of Beekeeping Technologies

Although attempts have been made to improve the adoption and productivity of beekeeping by various organizations, some social, ecological and climatic factors, as illustrated in Figure 4.3, were identified as constraints which hinder farmers from adopting the available beekeeping technologies. The major ones are discussed below.

 

4.2.8.1 Recurrent droughts

Mwanthi (2009) noted that droughts in the study area have increased from once every ten years to once every two years and that they are likely to increase further both in frequency and intensity due to the effects of climate change. The recurrent droughts are associated with scarcity of suitable forage and water leading to absconding and migration of the colonies. This translates to substantial losses to the farmers who may not obtain any honey crop for a number of seasons.

 

4.2.8.2 Attack by pests and predators

The results indicate that many bee enemies exist in the area and cause considerable damage to both the hives and products. Respondents who have adopted modern technology reported incurring huge losses as a result of hive damage by the honey badger. Considering that modern hives and accessories are quite expensive, this can hamper the adoption of this technology in the area. During the survey and from focus group discussions, it was noted that the pest and predator control methods usually advocated for by extension service providers are not very effective.

 

4.2.8.3 Vandalism

Though traditionally uncommon and a taboo in many communities, theft of honey and hives is becoming a major problem in many beekeeping areas. In severe cases, vandals at times use poisonous chemicals to subdue the bees before robbing them of the honey, and in some instances carrying away the hives. This problem is often associated with the rising poverty due to unemployment, especially among the youth. Measures to counter vandalism may prove to be expensive as they may include constructing bee houses to secure the hives or investing in extra measures to enhance security. These extra costs and the attendant losses act as a disincentive to the adoption of beekeeping technologies.

 

4.2.8.4 Deforestation

Bee plants are a pre-requisite to successful beekeeping as bees depend wholly on plants for their food. In the rangelands, indigenous plant species constitute a major source of nectar and pollen for honey bee colonies. However, the influx of people into these areas and the rise in the population have had an adverse effect on the floral resources resulting in poor bee nutrition and reduced honey production.

 

Nightingale (1976) observed that an increase in human population and the greater freedom of movement of people has become a great enemy to the bees. Field observations and the results of group discussions indicate that in the recent past, the study area has witnessed unprecedented cutting down of valuable bee plants due to increased demand for fuel wood and timber. Unfortunately, most of the trees that have been felled include indigenous species that usually take a very long time to mature.

 

Figure 4.3: Households Reporting on Beekeeping Adoption Constraints

 

4.3 RESULTS OF REGRESSION ANALYSES

4.3.1 Estimation of the Cobb-Douglas Production Function

The influence of labour, size of land holding, capital input, hands-on experience and managerial skills on honey output were estimated using the Cobb-Douglas production function. As shown in Table 6, labour, capital and managerial skills were significant at 5% level. The F-statistic was significant at 5% level, an indication that the factors as a group had a significant influence on the output. The value of adjusted R² indicated that 59.8% of the variation was explained by the variables.

 

Table 6: Estimated Coefficients of Cobb-Douglas Production Function (Adopters n=90)

Variables	Β	SE	T
Constant	- 1.804	0.785	- 2.298*
Labour  cost (X1)	0.934	0.089	10.489*
Land size (X2)	- 0.141	0.125	0.262
Capital cost (X3)	- 0.298	0.092	-3.232*
Managerial skills (X4)	0.617	0.281	2.199*
Experience (X5)	0.130	0.106	1.232

 Notes: *Significant at 5%, F=27.492, R²=0.621, Adj. R²=0.598

 

If the parameter estimates in Table 6 are substituted in Equation (1), the logarithmic form of the production function may now be expressed as follows:

LnQ_Adopters = -1.804+0.934LnX₁+-0.141LnX₂+-0.298LnX₃+ 0.617LnX₄+ 0.130LnX₅       (2)

The parameter estimates measure the responsiveness of output to a change in the levels of respective variables (Tan, 2008). The cost of labour and managerial skills had a positive and significant influence on output. This means that a percentage increase in these variables would increase honey output by 93.4% and 61.7% respectively. This shows that it would be profitable to expand investment on these two factors of production. Capital costs had a significant but negative influence on output, an indication that more investment on capital costs would not necessarily result in increased output and may even lead to a decrease in honey output.

 

A plausible explanation for this observation is that since the capital costs in this study were estimated by considering the bee equipment invested per household, the main ones being hives, a higher number of hives would lead to a higher stocking rate in the area. This would result in an increase in competition for forage by the honey bee colonies leading to reduced performance and reduced honey output. The low hive occupation observed in the area due to prolonged drought may also be another reason behind this observation. The returns to land and beekeeping experience are rather low and statistically insignificant indicating that compared to the other variables, these two were not limiting factors in honey production.

 

The Cobb-Douglas production function was also used to assess the returns to scale of the beekeeping enterprise. The sum of the elasticity coefficients was 1.2, indicating that there was increasing returns to scale for the enterprise. The beekeepers were thus operating below the optimal production level and had an opportunity to increase output levels.

 

4.3.2 Comparison of Resource Productivity of Different Technologies

The Cobb-Douglas production function was used to evaluate and compare the effects of labour, land, capital, managerial skills and beekeeping experience on honey output between the adopters of modern and traditional beekeeping technologies. Tables 7 and 8 summarize the estimated coefficients for adopters of modern and traditional technology respectively.

Table 7: Estimated Coefficients of Cobb-Douglas Production Function (Modern Technology =22)

Variable	Β	SE	t
Constant	-6722.	3.223	-2.101*
Labour costs (X1)	1.173	0.324	3.624*
Land size (X2)	-0.179	0.332	-0.541
Capital costs (X3)	0.099	0.347	0.284
Managerial skills (X4)	0.269	0.826	0.326
Experience (X5)	0.126	0.294	0.428

Notes: *Significant at 5%, F=4.118, R²=0.563, Adj. R²=0.426

 

Table 8: Estimated Coefficients of Cobb-Douglas Production Function (Traditional Technology n= 68)

Variable	Β	SE	t
Constant	-0.770	0.187	-0.942
Labour costs (X1)	0.991	0.102	9.738*
Land owned (X2)	-0.168	0.136	-1.235
Capital costs (X3)	0.154	0.143	-3.602*
Managerial skills (X4)	0.601	0.285	2.109*
Experience (X5)	0.300	0.145	2.068*

Notes: *Significant at 5%, F=25.255, R²=0.671, Adj. R²=0.644

 

If the parameter estimates in Tables 7 and 8 are substituted in Equation (1), the Cobb-Douglas production functions for the adopters of modern and traditional technologies can be expressed as shown in Equations (3) and (4):

LnQ _{Modern-technology} = -6.772+1.173LnX₁+-0.179LnX₂+0.099LnX₃+0.269LnX₄+0.126LnX₅         (3) 

LnQ _{Traditional technology} = -0.770+0.991LnX₁+-0.168LnX₂+0.154LnX₃+0.601LnX₄+0.300LnX₅       (4) 

The coefficients represent the relative proportion of output (in kgs of honey) contributed by unit increases in the respective variables. For the adopters of modern beekeeping technology, only the value of labour with a production elasticity of 1.173 had a significant contribution to output at 5% level. Thus a percentage increase in investment on labour by the beekeepers would result in increasing honey output by 117.3%. The returns to land, capital, managerial skills and experience in bee management were insignificant.

 

Four out of five variables were significant at 5% for the adopters of traditional technology. These were labour cost with an elasticity coefficient of 0.991, capital cost with an elasticity coefficient of 0.154, managerial skills with an elasticity coefficient of 0.607 and experience with an elasticity coefficient of 0.300. Only the labour cost was significant across both technologies although its elasticity of production was lower for the adopters of traditional technology. This means that investing more in labour is fundamental to increasing honey productivity for both adopters of both modern and beekeeping traditional technologies. For increased honey output, more time should be expended on aspects of colony management activities e.g. hive inspection, pest control, colony multiplication and colony feeding. Although labour in the study area may not be scarce per se, results indicate that farmers allocate very little time to beekeeping activities. This may due to lack of technical skills leading to the perception that bees do not require constant attention. Some beekeepers only visit their apiaries when they anticipate to harvest some honey.

 

For the adopters of modern technology, the independent variables of the model (X₁, X₂, X₃, X₄ and X₅) explained only 42.6% of the variation in the dependent variable (output Q),  compared to 64.4% for the adopters of traditional technology. The adjusted R² values suggest that about 57% and 36% of the variations were not captured for the adopters of modern and traditional beekeeping technologies respectively.

 

One observation of this analysis is that land area had no significant influence on output for both adopters of modern and traditional technologies.  Considering that honey bees can forage well beyond individual farm boundaries, this may imply that land, a key factor of production was adequate and not limiting compared to other variables.

 

The Cobb-Douglas production function was also used to estimate the impacts of adopting the different technologies on output. The sums of the elasticity coefficients were 1.5 for the adopters of modern technology and 1.9 for the adopters of traditional technology, indicating increasing returns to scale for both technology types. These results were not supportive of the set hypothesis that the productivity and rates of returns from modern technologies are higher compared to traditional technology.  Increasing returns to scale suggest that doubling of inputs would more than double output, an indication that the beekeepers were producing at stage one of the production function as illustrated by Figure 4.4. At this stage, the efficiency of the variable inputs has not reached a maximum and could be increased by applying more of the variable factors of production so as to produce at the optimal level.

 

Figure 4.4: Production Function Illustrating Returns to Scale for the Technology Adopters

4.3.3 Binary Logistic Regression Analysis

The binary regression analysis was used to test the influence of a number of variables on household beekeeping technology adoption or non-adoption. The Chi-square statistic was found to be significant at 5%, an indication that the model parameters were jointly significantly different from zero for the adoption of beekeeping technology. Table 9 gives a summary of the results. The results show that gender of a household head, size of  household, size of land, livestock holding and access to extension services were significant at 5% level as indicated by the Wald statistic. The explanatory variables that had a significant influence on adoption of beekeeping are discussed below.

 

 

 

Table 9: Maximum Likelihood Estimates for Beekeeping Technology Adoption Model

Variable	β	SE	Wald	Exp(β)
Constant	-4.463	1.440	9.608*	0.012
Age of household head	0.141	0.320	0.195	1.152
Gender of household head	2.171	0.939	5.348*	8.766
Size of household	0.134	0.066	4.077*	1.143
Size of land	0.231	0.068	11.395*	1.260
Livestock holding	0.110	0.052	4.503*	1.117
Off-farm income	-0.551	0.586	0.885	0.576
Membership to SGH	-0.140	0.414	0.115	0.869
Access to extension  services	5.028	1.178	18.207*	15.161

Notes:  ^*Significant at 5%, -2 Log likelihood=151.658, Model Chi-square= 6.575

 

4.3.3.1 Gender of household head

Depending on the nature of the technology, gender of farmer is likely to play different roles in technology adoption. The effect of this variable may either be positive or negative. The variable had a positive coefficient, an indication that male-headed households were likely to adopt beekeeping technologies compared to female-headed households as hypothesized. This may be due to the fact that beekeeping is still regarded as a man’s job in many rural communities. Hive making and placement may require some substantial labour and skills which favour the males. Honey harvesting, especially from traditional log hives, is considered risky and the domain of males.

 

4.3.3.2 Size of household

This influenced the adoption positively as hypothesized, implying that large households were more likely to adopt beekeeping technologies compared to small households. This may be due to the fact that larger families have higher financial and nutritional requirements. They are also able to provide the extra labour that the technology may demand. These results are consistent with the findings of Juma et al. (2003) who assessed production risk and farm technology adoption in Machakos and Taita Taveta Districts in Kenya. They reported that a marginal increase in household membership increases the probability of the household adopting terracing as a means of soil conservation.

 

4.3.3.3 Size of land holding

This had a positive coefficient as hypothesized indicating it influenced the adoption of beekeeping technologies positively. Those with large pieces of land are thus expected to adopt beekeeping more than those with smaller pieces of land. Farmers with big tracts of land are considered wealthy and can therefore afford to invest in new ventures. These findings are in conformity with those of Demeke (2003) who observed that farm size positively and significantly affect farmers’ decision to adopt soil conservation practices in Ethiopia.

 

4.3.3.4 Livestock holding

Livestock ownership, which had a positive influence, was hypothesized to be positively related to the adoption of agricultural technologies because it is a representation of wealth status (Freeman et al., 1996). Well endowed farmers have extra resources to invest in new ventures and to bear any risk that may occur. This observation is in line with the findings of Degu et al. (2000) who carried out studies on the adoption of seed and fertilizer packages and the role of credit in smallholder maize production in Ethiopia.

 

 

 

4.3.3.5 Access to extension services

This had a positive influence on the adoption as hypothesized. A majority of those who had access to extension services had adopted modern beekeeping technology. Extension services had the highest positive coefficient among all the variables. This may be due to the fact that modern beekeeping was introduced in the area by a private organization that also offers follow up extension services. This observation is consistent with the findings of other adoption studies (Uaiene et al., 2009; D’Emden et al., 2008; Makokha et al., 1999).

CHAPTER FIVE

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 SUMMARY

This study was conducted in the semi-arid district of Kibwezi, south eastern Kenya. It analysed the technical, socio-economic and ecological factors that determine the adoption of beekeeping technologies by the agro-pastoral households. Data were collected in Utithi location in Machinery division and Ngwata and Kikumbulyu locations in Kibwezi division. The data were collected through formal interviews using a structured questionnaire, observations and focus group discussions. The analysis of data consisted of descriptive statistics (such as percentages, frequencies and means), the estimation of a Cobb-Douglas production function and a binary logistic regression to identify the determinants of beekeeping adoption.

 

The results of descriptive analysis indicate that beekeeping is an important socio-economic undertaking in the area, with 90 out of the 170 respondents (representing 52.9%) being adopters. The majority of the adopters (62.2%) were in the age category of 18-50 years.  The adopters of beekeeping technology had larger pieces of land than the non-adopters. They also had more livestock and majority were members of self-help groups compared to the non-adopters.

 

Reasons for non-adoption was mainly due to lack of skills, capital, small land sizes and cultural inhibitions while the adoption of beekeeping technology was driven by increased income, modest start-up capital requirements and bees as a source of food and medicine. Two types of technology i.e. traditional and modern beekeeping have been adopted by farmers in the district with about 75% of the beekeepers using traditional technology. The main reasons behind the preference of this technology include low cost, ease of availability and low management requirements.  The high cost and unavailability of modern technology are the key constraints to its adoption in the area. The other constraints hampering adoption of beekeeping in the study area include recurrent droughts, ineffective control measures for bee pests and predators, vandalism, deforestation and lack of bee protective clothing. The study also revealed that most of the beekeepers had no access to extension services and credit facilities. For instance, only 30% of the beekeepers had received any form of beekeeping training.

 

The Cobb-Douglas production function was used to estimate the effects of the factors of production on honey output of the households adopting various technologies. The results show that variable labour costs, capital costs, and managerial skills had significant contributions to honey output and expanding these variables would be profitable to the farmers. Further, the results indicated that adopters of either technology were experiencing increasing returns to scale.

The increasing returns to scale imply that the beekeepers in the study area are producing at an inefficient level, in other words, they apply too little of the variable inputs compared to fixed resource outlays. These farmers can thus obtain more output per unit if they increased the level of variable inputs.

 

The results of the binary logistic regression indicated that gender of a household head, size of the household, size of land, number of livestock owned (a proxy indicator of wealth) and access to extension services had a positive and significant effect on adoption. These variables therefore enhance adoption of beekeeping technology.

 

 

5.2 CONCLUSIONS

The findings of this study indicate that beekeeping is a suitable farming activity in the study area and has the potential to enhance environmental conservation as well as improve household income, nutrition and health, hence leading to poverty alleviation. The adoption of improved technology is low as a majority of the beekeepers prefer the old-age traditional technology which often leads to low quality products. Lack of an organized marketing structure has also given the middle-men an opportunity to exploit the beekeepers by offering low prices for hive products.

 

Although beekeeping is an important livestock enterprise among the agropastoral households in the study area, there has been a notable decline in productivity in the last decade. This can be attributed to recurrent droughts, deforestation, and inefficiency in the allocation and utilization of resources by the farmers.

 

The existence of self-help groups has played a positive role in enhancing the adoption of beekeeping technology particularly by women and the youth. Such groups if strengthened will in the long run be instrumental in increasing production, self-employment and food security.

 

5.3 RECOMMENDATIONS

In view of the study findings, a number of recommendations are suggested as follows:

 

·      There is need to enhance extension services through practical on-farm demonstrations, field-days, exchange visits and study tours. Extension services were found to have the highest significant impact on the adoption of beekeeping technologies. Appropriate packages targeting women and the youth need to be developed in an effort to encourage adoption by these groups.

·      The capacity of the existing farmer groups and associations involved in beekeeping activities should be strengthened through training on issues like leadership and group dynamics. Through these groups, farmers will be able to access credit. This will also make it easier for the farmers to access markets as a group by bulking their products and pooling their resources in order to benefit from the economies of scale.

·      Value addition of primary hive products through product processing and packaging should be promoted. The beekeepers can greatly improve their income by acquiring skills in simple processing methods instead of selling their honey in raw form. Processing of other hive products and by-products should also be promoted.

·      There is need for more investment in labour and management skills by the farmers. These inputs were found to have a high and positive influence on honey production and expanding investment on them would result in better profits.

·      There is need to preserve the indigenous vegetation in an effort to slow down land degradation and thus ensure sustainability of beekeeping.

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APPENDIX: SURVEY QUESTIONNAIRE

 

1.      Date of the interview: ……………………………………  Questionnaire No………………...

2.      Name of enumerator……………………………………………………………………………

3.      Name of respondent……………………………………………………………………………

4.      Relationship of respondent to household head…………………………………………………

5.      Division ……………………………………………....Location………………………….......

6.      Sub-location …………………………………………….Village…………………………….. 

7.      Age: …………………………………………………………………………………………….

8.      Gender:                  Male                            Female         

9.      Marital Status         Married                         Single

10.  Education level of respondent: (a) None (b) Primary (c) Secondary (d) Tertiary           

11.  Number of dependents in family: ……………………………………………………………...

12.  What is your primary occupation? ……………………………………………………………..

13.  What is the total size of your land?  ………………………acres

14.  What are the major activities on the farm:

(i) ………………………………..          (ii)…………………………………………………

(iii)……………………………....            (iv) ……………………………………………….

15.  What are the major crops you grew during the cropping seasons (2008/2009)?

Crop type	Acreage	Harvest (in bags)	Price per Bag

16.  What is the type and number of Livestock did you keep in the last one year?  

Type of livestock	Breed	Number	Value in Kshs.
Cattle
Goats
Sheep
Poultry
Donkey
Other (specify)

17.  Did you belong to a Community Group in the last one year? Yes                       No

18.  If Yes, name of group: ……………………………………………………………………

19.  What have been the main activities of the group in the last one year?

(i) ………………………………ii)…………………… (iii) ……………………………..

20.  When did you start using the keeping bees? (year)…………………………………………….

21.  How many hives did you have in the last one year? …………………………………………...

22.  What type(s) of hive(s) did you have in this period?

 

Type of hive	Number
Traditional
Kenya Top Bar Hive
Langstroth
Clay hive
Box hive

 

 

23.  Why did you prefer this type of hive (s)

Type of hive	Reasons choosing type of hive
Traditional	i) ii) iii) iv) v)
Kenya Top Bar Hive	i) ii) iii) iv) v)
Langstroth	i) ii) iii) iv) v)
Clay hive	i) ii) iii) iv) v)
Box hive	i) ii) iii) iv) v)
Other (Specify)	i) ii) iii) iv)

 

24.  How many of your hives were occupied in the last one year occupied? 

Type of hive	Number Occupied	Number Unoccupied
Traditional
Kenya Top Bar Hive
Langstroth
Clay hive
Box hive
Other (specify)

 

25.  Did you have a persistent problem of low / non occupation in the last one? Yes             No     

26.  If yes, which hive type(s) were seriously affected?

Hive Type	Rank
Traditional
Kenya Top Bar Hive
Langstroth
Clay hive
Box hive
Other (specify)

 

27.  What are the main reasons behind the observed occupation rates amongst the various hives?

Type of hive	Reasons behind observed occupation rate
Traditional	i) ii) iii)
Kenya Top Bar Hive	i) ii) iii)
Langstroth	i) ii) iii)
Clay hive	i) ii) iii)
Box hive	i) ii) iii)
Other (specify)	i) ii) iii)

 

28.  How many times did you harvest per season during the last one year? ………………………

29.  What are the yields of hive products from each hive type per year?

Type of hive	Honey (kg)	Beeswax (kg)	Other products (specify)
Traditional
Kenya Top Bar Hive
Langstroth
Clay hive
Box hive
Other (specify)

 

30.  Did you undertake any bee management practice in the last year?  Yes                         No

If yes, please indicate by Ö the practices carried out

Management practice	Under taken( Ö)	Not undertaken ( x)
Routine colony inspection
Apiary management.(clearing, shading) e.t.c
Division making
Swarming control
Feeding
Pest control

 

31.  Do you have any beekeeping accessories?  Yes                           No

If yes, which accessories did you have and use in the last one year?

a)      ………………………………………………………………………………………………

b)      ………………………………………………………………………………………………

c)      ………………………………………………………………………………………………

d)     ………………………………………………………………………………………………

31. If No, why did you not use these accessories?

a)      ………………………………………………………………………………………………

b)      ………………………………………………………………………………………………

c)      ………………………………………………………………………………………………

32.  Was there any absconding?        Yes                                            No

If yes, what were the main reasons for the absconding?

a)      …………….……………………………b)………………………………………………..

c)      ………………………………………… d)……………………………………………....

d)     …………………………………………..f).………………………………………………

33. What bee pests /predators did you encounter in your apiary/hives?

 

Pest/Predator (Local Name)	Common Name	Scientific Name

 

 

34. What the major bee plants in your farm / area during the last one year?

Local name	Common Name	Botanical Name

35. Have you had any training on beekeeping? Yes                           No

36. Did you have regular contact with extension service providers promoting beekeeping in the       last year?   Yes                                     No

37. If yes, how many times did you have contact in the last year? …………………………

38. Are you expanding your beekeeping activities? Yes                    No  

39. If yes, how are you doing it?

a)      ………………………………………………………………………………………………

b)      ………………………………………………………………………………………………

c)      ………………………………………………………………………………………………

d)     ……………………………………………………………………………………………....

e)      ………………………………………………………………………………………………

40. If No, what are the reasons for not expanding?

a)      ………………………………………………………………………………………………

b)      ………………………………………………………………………………………………

c)      ………………………………………………………………………………………………

d)     ………………………………………………………………………………………………

e)      ………………………………………………………………………………………………

41. How much honey did you sell in the last year?  (Kgs) …………………..

42. Where did you sell the honey and at what price?

Market	Price per kg. (Kshs.)	Remarks (raw or refined honey)
Local consumers
Middle men

 

43. How is the demand for honey in your area?  High                       Low

44. Did you process your honey in the last year?   Yes                      No

45. If yes, which methods did you use?

i)                    ………………………………………………………………………………………………

ii)                  ………………………………………………………………………………………………

iii)                ………………………………………………………………………………………………

46. What was your average income from the beekeeping enterprise during the last year? Kshs…..    

47. Are there any other benefits (non cash) that you can attribute to the beekeeping?

Enterprise?  Yes             No

48. If yes, which were the major ones?

i)                    ………………………………………………………………………………………………

ii)                  ………………………………………………………………………………………………

iii)                ………………………………………………………………………………………………

49. What constraints did your beekeeping enterprise face in the last one year?

…………………………………………………………………………………………

…………………...……………………………………………………………………

50. Did you ever receive any credit / loan for your beekeeping project in the last one year?

Yes                                    No

51. If yes, from which institution did you get the credit? …………………………….

i) Government agency                                ii) Non Government Organization (NGO)

iii) Group (e.g. Merry go Round)                iv) Bank

       v) Any other source (specify) ………… ………………………………………………………

52. What is your view on the requirements of practicing modern beekeeping techniques compared to the traditional practices?  Modern beekeeping requires:

Requirement	More	Less	Equal
Mgt time
Cost
Knowledge
Land
Labour
Others, specify