Factors affecting the adoption of beekeeping and associated technologies in Bushenyi District, Western Uganda

A Mujuni*, K Natukunda** and D R Kugonza*

* Department of Agricultural Production, School of Agricultural Sciences, College of Agricultural and Environmental Sciences,
Makerere University, P.0 Box 7062 Kampala - Uganda
** Department of Agribusiness and Natural Resource Economics, School of Agricultural Sciences, College of Agricultural and Environmental Sciences,
Makerere University, P.0 Box 7062 Kampala - Uganda
natukunda2007@gmail.com

Abstract

To assess the factors affecting the adoption of beekeeping and associated technologies in Western Uganda, this study was undertaken. A total of 100 farmer households were purposively and randomly selected from two sub-counties to respond to a standard questionnaire. The sample comprised of 50 farmers from each of the two sub-counties. During sampling, 30 beekeepers (adopters) and 20 non-beekeepers (non-adopters) were purposively and randomly selected.

Results revealed that majority (95%) of the farmers were male. More than 75% of the farmers were aged 30 years and above and the majority had attained formal education with 17.5% being tertiary education graduates. Most farmers (37.5%) in Bushenyi had less than 6 years of beekeeping experience while 15% had an experience of over 16 years. Though constrained by lack of equipment, bad weather, pests, lack of credit facilities, poor transport and poor extension services, the farmers do generate income, mainly from beekeeping, banana, tea and coffee. The major factors affecting the adoption of beekeeping enterprise included fear (phobia for bee stings), starting capital (to buy hives and hive equipment), inadequate knowledge and skills, and lack of land on which to set the apiary for the safety of the neighbouring environment. Other factors included: level of income, information about the technology, cost of technology, availability of the technology, adoption by neighbours, returns from the technology and technicalities involved in the technology. A multiple regression model revealed that adoption index is affected by age of the farmer (years), education level (primary, secondary or tertiary), experience in beekeeping (years), apiary size (number of hives), extension services (number of sources of information), and access to credit. A unit increase in any of these independent valuables had an effect on the adoption index. However, age of respondents, level of income, experience in beekeeping and size of the farm were non-significant (P >0.05). Extension services and access to credit were significant (P < 0.05). Farmers training in beekeeping, record keeping, use of modern technologies, control of weather effects and control of bee pests and diseases should be strengthened. Efforts should be put into empowering the farmers with knowledge and skills, ensuring availability of modern technologies and increasing the beekeepers access to credit facilities.

Key words: Adoption index, modern technologies, record keeping

Introduction

In order to promote diversification in agriculture and reduce poverty in Uganda, beekeeping is one of the major agricultural activities being upheld by the government programmes of poverty alleviation (MAAIF 2000). It offers a great potential for income generation, poverty alleviation, sustainable use of forest resources and diversifying the export base. The most important service the honeybees render to mankind is pollination of agricultural and forestry crops (FAO 1990; Commonwealth 2002). In contrast with other agricultural projects such as livestock, poultry and fish farming, beekeeping is a relatively low investment venture that can be undertaken by most people (women, youths, the disabled and the elderly). With beekeeping, there is no competition for resources used by other forms of agriculture. Additionally, it is environmentally friendly and can be productive even in semi-arid areas that are unsuitable for other agricultural use (FAO 1990). There is availability of market for bee products both locally and internationally (UEPB 2005), and it is important to note that pharmaceutical and cosmetic industries utilize bee products such as honey, royal jelly, beeswax and propolis (UEPB 2005).

In recent years, livestock production with potential application of modern technologies has technically advanced. However, satisfying the basic needs of the rural people to improve their standards of living is still a challenge despite technological advances (Kugonza 2009). Beekeeping as an important area of livestock agriculture has not received sufficient attention in the past (Matanmi 2008) as it does presently. It has been promoted widely in many countries as a major rural development engine (Bees for development 2000). Not only does the practice of beekeeping have intrinsic health benefits through providing a food source of great nutritional value which is lacking in rural areas, but also requires few inputs and capitalizes on a ready supply of pollen and nectar from crops they pollinate (NET Uganda 2002).

Beekeeping is emerging as a very successful agricultural practice for rural area based people in less developed countries mainly due to its economic benefits from the products of this practice (Kugonza 2009). In Uganda, honey, beeswax, propolis, royal jelly and bee venom are the major financial products (Kamatara 2006), with pollination as the major biodiversity benefit (Delaplane 2008). Since food security cannot be achieved without income security, beekeeping could be a useful tool for improving rural economy; however, people are reluctant on taking up this enterprise.

Agricultural research has not given due emphasis to assessment and understanding of modern methods of bee farming especially in developing countries where the scholars and policy makers have not been able to adequately demonstrate the importance of these modern methods to livelihoods. Modern beekeeping can easily be embarked on because investment is low; it does not require large area of land and there is no need for daily care (Matanmi 2008). Adopting improved technologies and improved management practices would greatly improve the yields and quality of honey (Bees for development 2000). Even though considerable attention is given in reports and documents to the significance of beekeeping in Uganda, little research and development in beekeeping has been conducted. It is estimated that Uganda produces 5,000 tonnes (MAAIF 2008) which is only 1% of the national annual production potential estimated to be 500,000 tonnes (Horn, 2004). Efforts to increase production would require proper assessment of the factors affecting the adoption of beekeeping and associated technologies. It is this research gap that prompted the curiosity of this study.

Materials and methodology

Study area

This study was conducted in Bushenyi (0^o 32'N, 30^o 11'E), located approximately 65km North-west of Mbarara, the largest city in the sub-region; while Kampala, the national capital lies 337km to the East. Bushenyi has a great potential in agriculture, as it ranks high amongst the producers of banana and tea; with coffee, cotton and fruits such as pineapples and passion fruits also being important crops in this region. Cattle, fish and honey also raise significant amounts of money to the district’s economy (UBOS 2004).

The study was conducted in the two Sub-counties of Bumbaire and Kyamuhunga because of their known high potential in honey production. Bumbaire is situated in Igara County and is well known for banana production, accounting for a quarter of the districts production. Kyamuhunga boasts of its famous tea production and processing, with almost every household having at least an acre of tea plantation as a common source of income. However, some families also venture into other enterprises like fish farming, dairy farming and beekeeping.

Sample selection and sampling technique

A total of 100 farmer households were purposively and randomly selected from two sub-counties to respond to a standard questionnaire. The sample comprised of 50 farmers from each of the two sub-counties. During sampling, 30 beekeepers (adopters) and 20 non-beekeepers (non-adopters) were purposively and randomly selected. Local Council Leaders actively participated in the selection of representative farmers in the study area. For beekeepers (adopters), active participation in beekeeping was the main criteria considered in the selection of representative farmer households in the study area. For non-beekeepers (non-adopters), farmers who had never carried out beekeeping were selected. From lists of active beekeepers obtained from the two Sub-county Agricultural Officers of the study area, 20% of them were chosen to represent the sample. A simple random technique was used where each farmer in the adopter or non-adopter group had equal chances of being selected to represent the population.

Data collection

A pre-tested structured questionnaire with close ended and open ended questions was used in collection of primary data. Observations involving systematically watching, listening and recording of the respondent’s words, expressions and characteristic features of interest were also done. A closer visit in and around the apiary sites of selected households was made in order to obtain first hand observation on all aspects of beekeeping. Photos were taken during these visits.

Data analysis

Filled questionnaires were coded and keyed into Statistical Package for Social Sciences (SPSS 2007) computer software. Ms-Excel was used to calculate the adoption index of each household and results were fed into SPSS to be able to run the model. Data was then analyzed using descriptive statistics, correlation and regression analysis.

Calculation of the Adoption Index

The following selected beekeeping practices were used in calculating the adoption index: Baiting, hive inspection, use of modern beekeeping equipment/hives, apiary cleaning, record keeping and pest management. The scoring of each practice was done on a scale with two-point continuum (doing the practice and not doing the practice). In this method a score of 1 was assigned to every beekeeping practice being carried out and 0 to every practice that was not being carried out. Thus, every individual respondent was capable of obtaining a score ranging from 0 to 6 for their responses. Total score of the individual household was arrived at by adding the scores obtained on the different practices. These total scores were later converted to a standardized score of adoption index. The adoption index of selected beekeeping practices were calculated using following formula below:

Model specifications of the factors affecting the adoption of beekeeping and associated technologies

In order to assess the factors affecting the adoption of beekeeping and associated technologies, a Multiple Linear Regression Model was run using the Ordinary Least Squares (OLS) method. The level of significance of the variables was tested using a t-test at a 5% level of significance. A constant (α) indicates the rate of adoption of a farmer holding other factors constant. The error term (µ) was included to account for the other factors other than the tested variables.

A Multiple Linear Regression Model of the factors affecting the adoption of beekeeping and associated technologies was specified as below:

A.I. = α +β₁X₁+ β₂X₂+β₃X₃ +β₄X₄+ β₅X₅+ β₆X₆+µ

Where:

A.I. = Adoption Index (dependent Variable)

α = Constant (intercept)

X₁ = Age of the farmer (years)

X₂ = Education level (primary, secondary or tertiary)

X₃= Experience in beekeeping (years)

X₄= Apiary size (number of hives)

X₅ = Extension services (number of sources of information)

X₆ = access to credit (1 for access to credit; 0 other wise)

µ = Random error term

Adoption index is expected to change by a certain factor, β (coefficient) if any of the above variables increases by one unit.

Results and discussion

Social demographic characteristics of beekeepers

Age, gender, experience in beekeeping, level of education and source of livelihoods (Table 1) were the socio demographic characteristics studied.

Table 1. Socio demographic characteristics of beekeepers in Bushenyi district
Variables (n= 60)	Percentage of farmers(%)
	Bumbaire		Kyamuhunga
Gender of beekeepers
Male	95		95
Female	5		5
Age groups (years)
20–29	15		0
30–39	30		30
40–49	10		15
> 50	45		55
Highest level of education
Primary	50		30
Secondary		30
Tertiary	20		15
Experience in beekeeping (years)
<5	40		35
6–10	30		30
11–15	20		15
>16	10		20
Sources of capital for beekeeping
Savings	95		100
Friends	15		0
Financial institutions	20		25
Sources of household income
Beekeeping	90		85
Bananas	90		40
Tea	0		85
Coffee	65		8
Piggery	15		4
Dairy	5		0
Poultry	5		0
Salary	5		0

The majority of the beekeepers in the study area were male with only 5% being female. Similarly, Matanmi (2008) found out that majority of the respondents in Nigeria (90% bee hunters and 80% of beekeepers) were male. The conventional siting of the hives too high makes it impossible for women to operate them thus reducing women’s participation. Farmers are advised to place the hives at heights convenient for them to work on their feet and to cater for women who are not permitted to climb trees in some Ugandan cultures (Kugonza 2009). In addition, Uganda rural women work 16-18 hours a day (UNFA 1999), therefore, involving of women in beekeeping projects adds to their work load. Traditionally males own most of the profitable enterprises in the household and to smaller extent accessibility to land by women is limited. Physically the beekeeping enterprise is relatively labour intensive and along with the other reasons this may explain why it is dominated by men. The majority of Ugandan men still hold onto tradition of keeping women out of most men’s activity (Margaret 2002) calling for the need to blend culture to increase women participation in beekeeping.

Half of the farmers were above 50 years of age, relatively higher than ages reported for beekeepers elsewhere, for instance Farinde et al (2005) who reported that 73.8% of beekeepers were aged 30 years and above. Bumbaire had a larger percentage of young beekeepers aged below 30 years. This indicates an ageing farming population (Matanmi 2008), showing that farming in the rural areas of Uganda is dominated by older farmers because of the migration of youths to urban centers in search of white-collar jobs. The high percentage of older farmers is further explained by the tendency of people to get involved in productive activities as they grow older. The need to cater for their demanding families drives them into looking for the profitable ventures to engage into.

All beekeepers who participated in this study had attained formal education, with the highest percentage (42.5%) having attained secondary education, while 17.5% had gone further to attain tertiary education. Interestingly, some of the respondents with tertiary education are professionals in other fields rather than agriculture. This shows that beekeeping is undertaken by the educated, which may stimulate their acceptance of improved technologies since education facilitates farmers’ adoption of innovations (Onemolease 2005; Natukunda et al 2011).

Bearing in mind that experience is the best teacher, most farmers (37.5%) in Bushenyi had less than 6 years of beekeeping experience and only 15% of them had an experience of more than 16 years. Either, these businesses show a new enthusiasm towards beekeeping, hence the finding that many respondents had been in the business for a few years; or the finding could be in line with a common adage that “most of the businesses in Uganda do not live to celebrate their fifth birthday”. Nevertheless, the industry still has a lot of potential and as the farmers gain more experience in this enterprise, more output in terms of beekeeping products will be registered.

Capital injected into most of the enterprises was mainly got from personal savings and to a smaller percentage from borrowing (Table 1). Of the households that borrowed, 7.5% got start-up capital from friends while 22.5% got it from financial institutions. Borrowing was not highly embraced by the farmers mainly as a result of the high interest rate attached to the loans procured; and at the time of this study was averaging 36% of the principal amount, among most commercial banks, and yet farmers are known to be credit shy.

Beekeepers are engaged in many enterprises for sourcing household income. Interestingly, most of them intimated that beekeeping was their major source of income because of its low input demand and high output. In Kyamuhunga for instance, 85% of the households derived their income from tea farming while Bumbaire recorded none but more of the farmers there earned strongly from the banana enterprise. Coffee still carries great importance generally in Uganda and Bushenyi in particular, and it did rank the fourth source of income even after the wilt destroyed more than half of the plantations in the region and country at large in the past decade.

Apiary characteristics

Across the Sub-counties, most beekeepers (56%) owned traditional hives with less than 1% owning a Langstroth hive (Table 2). Traditional hives were quite productive and popular due to their low cost and high colonisation rates. Langstroth hives were the most productive (9.1 ± 0.01 kg) and were also associated with yielding the best quality honey and beeswax, due to their superior design. Interestingly for this study area, Langstroth hives had the best colonisation rate, contrary to findings for central Uganda where Kugonza et al (2009) found that traditional hives had the best colonisation rates.

Table 3. Hive baiting, inspection, cleaning and protection
Variables (n = 60)	Proportion of beekeepers (%) per sub-county
Variables (n = 60)	Bumbaire	Kyamuhunga
Are bee hives usually baited?
Yes	85	95
No	15	5
Type of hive bait used
Beeswax	75	80
Cattle dung	25	20
Inspection frequency of colonized hives
Daily	15	30
1-3 times a week	65	70
Once a month	15	0
Twice a month	5	0
Inspection frequency of un-colonized
Daily	60	40
1-3 times/week	30	60
1/month	10	0
What is checked at inspection^§
Hive arrangement	90	80
Honey deposition	60	75
Colonization	47.5	45
Absconding	50	40
Disease and pest presence	57.5	55
Apiary cleaning frequency
Once a month	75	70
Twice a month	15	30
Thrice a month	5	0
How the security of the bees is ensured
Fencing the apiary	55	65
Putting the hives in a bee house	50	50
^§Values are not mutually exclusive, respondents gave more than one aspect that is inspected per visit

In Kyamuhunga, 70% of the farmers inspect the colonized hives at least once per week; checking mainly on the arrangement of hives, to make sure they are in position and are intact, in case animals or weather has disturbed them. In general, beekeepers were over inspecting their hives (Table 3); for instance, daily inspection, done by almost a quarter of all respondents is not recommended as it may cause stress to the bees.

Un-colonized hives were being inspected more often than colonised hives, as recommended, so as to make sure the hives stay in a clean and colonisable condition most of the time. It was also done to ensure that the hives are transferred to the apiary as soon as they get colonized. Security of the apiaries was observed mainly by fencing (60%), while cleaning of apiary was being done at least twice a month (Table 3). Cleaning of the apiary entails slashing the grass in the apiary, sweeping in the bee houses and particularly cleaning inside the un-colonised hives to remove vermin debris (cob webs, dead insects, vermin beddings).

Record keeping

For each type of record kept, there were higher proportions of beekeepers from Kyamuhunga (Figure 1). This is mainly because record keeping is a requirement by the cooperative association where the beekeepers sell their production. However, there is a general low trend of record keeping in Bushenyi district; and in fact Uganda, as farmers tend to rely on memory records rather than written records. Farmers are not conversant with the importance of the records on their farms coupled with their rampant illiteracy. At least 10% of the farmers in both sub counties kept records on the number of hives colonised and more than 35% of the farmers kept records of the total number of hives owned.

Constraints faced in beekeeping

Similarly, Monga and Monocha (2011) reported that management of colonies during extreme weather conditions was a constraint experienced by 58.3% of respondents in an Indian study.In the case of our study, heavy rains destroyed the traditional earthen hives further intensifying the problem. This leads to over working the bees in an effort to fill the created cracks and holes with propolis (Plate1).

Pests that mainly affected the area include wax moth, termites, red ants and the vermin. This constrains a large number (70%) of farmers from Bumbaire Sub-county and is explained by the relatively low apiary cleaning frequency by these farmers (Table 3). Similar to these findings, it has been reported that ants cause most of absconding with a prevalence of 50.1% in on-station hives in central Uganda (Kamatara 2006). Also, Monga and Monocha (2011) reported that majority of respondents (86.7%) listed attack of honeybees by pests and diseases as a major constraint in district Panchkula (Haryana), India.

Transport difficulty was given as a major problem because generally the area is remote with mainly low grade murram roads and in poorly kept condition. The transport difficulties were higher in Kyamuhunga (60%) due to the hilly nature of this sub-county as mentioned earlier. This percentage is far greater than Matanmi (2009) findings where only 20% of the farmers in Nigeria recorded transport problems. Also, Kugonza and Nabakabya (2008) reported a smaller percentage (34.5%) in a study conducted in four districts one from each of the four regions of Uganda.

However, it was not apparent why labour shortage was ranked high as a constraint by more than half of the beekeepers yet; beekeeping is considered one of the least labour intensive farming activities. It is therefore plausible that the labour so demanded was for the un-necessary and too frequent apiary inspection. IPMS (2005) documented that highest labour is involved in watching swarming times, beehive construction, honey extraction and colony multiplication.

Source of information

Information on farming was mainly being conveyed from farmer to farmer (Table 5); probably because of the trust they put in each other compared to trust in extension workers. Less than 3% of the farmers access information from newspapers mainly due to their high cost and remoteness of the household location, however, the high literacy observed earlier enable a relatively high proportion of farmers getting information from books. In all, it is observed that extension workers are recognised as a major source of information on farming, their contribution needs to be strengthened especially in light of the foregoing discussion on knowledge and skills of the beekeepers.Our findings agree with Adereti et al. (2006),that majority of the farmers (at least 60%) rely on group discussions/meetings with fellow farmers as their major source of technical information.

Marketing

Table 6. Location and form in which hive products are marketed
	Proportion of beekeepers (%)^§
Variables	Bumbaire (n=30)	Kyamuhunga (n=30)
Where hive products are sold
Cooperative Association	70	95
Farm gate	50	65
Local shop	30	15
What is done to honey harvested
Sell	100	100
Home consumption	90	90
Give friends	20	20
Factors considered in setting sell price
Current price	55	50
Quantity harvested	10	20
Demand	0	20
Form in which the products are marketed
Semi refined honey	95	90
Crude honey	15	10
Comb honey	20	5
^§Values are not mutually exclusive, as respondents gave more than one location where they sell their hive products, what they do with honey harvests, and the form of hive products marketing

Factors affecting adoption of beekeeping

This question was administered to both the beekeepers and the non-beekeepers and the results obtained are presented in Table 7. Several factors were found to be affecting the adoption of beekeeping enterprise as reported by both beekeepers and non-beekeepers.

Phobia for the bees was identified as the most serious factor effecting adoption of beekeeping business. This is due to the aggressive behaviour of the bees that tend to sting whatever crosses their boundaries. Lack of start-up capital to buy hives and tools was also ranked very highly followed by lack of land on which to set up apiaries. Inadequacy of skills and knowledge on the art and science in bee farming was a limiting factor to over one third of both beekeepers and prospective beekeepers. Lack of land on which to set up apiaries was an issue yet average land holding in the study area is not very small. This however may have been an issue of the available land being under intensive use and high population density such that the available land is not secluded enough to ensure limited contact between bees and humans dwelling in the neighbourhood.

Table 7. Factors affecting adoption of the beekeeping enterprise and help that the government can offer
	Beekeepers		Non-beekeepers
Variables	Bumbaire (n = 30)	Kyamuhunga (n = 30)	Bumbaire (n = 20)	Kyamuhunga (n = 20)
Factors affecting adoption
Fear of the bees	90	82	80	66
Lack of start-up capital	40	42	46	40
Inadequate skills	40	33	20	30
Lack of land for setting up apiary	40	30	40	33
Lack of safety equipment	40	36	50	45
Government help needed
Provision of start-up capital	80	90	100	93.3
Training of beekeepers	85.4	90	80	86.7
Provide/strengthen extension service	30	15	40	46.7
Ensure price control of hive products	10	15	13.3	20

Respondents in this study stated that government can go a long way to ameliorate the problems that hinder adoption of beekeeping. Start-up capital is the main form of help the farmers would wish to receive from the local/central government in form of hives and hive equipment. Farmers also suggested that the training offered to them should be improved by making it more practical, and this should be coupled with increasing the number of extension workers / service providers in the beekeeping domain. Controlling of bee product prices was also believed to be able to turn around the fortunes of beekeepers.

Regression analysis of factors affecting the adoption of beekeeping and associated technologies

A regression model was run to find out how a set of variables affect the adoption of beekeeping and associated technologies (Table 8). Additional results about factors affecting adoption were acquired using an open ended question (Table 9).

Given the above results, the function of the factors affecting the adoption of beekeeping and associated technologies is as shown below:

A.I. = 12.1 – 0.051X₁+ 0.913X₂+ 0.22X₃+ 0.027X₄+ 2.69X₅+ 5.66X₆+ µ

The regression model explains 41% of the extent to which the selected factors affect the rate of adoption of beekeeping technologies in Bushenyi district (Table 8). The constant value 12.1 indicates the adoption index of the farmers, holding other factors constant.

Age of the respondent was found to be negatively related to the adoption index though it was not significant to the test. A unit year increase in age leads to 0.051 decrease in the adoption index of the farmer; older farmers are less interested in the new technologies compared to the young and innovative farmers. The level of education was insignificantly related to adoption index in a positive manner showing that the higher the education level the more likely the adoption as indicated by the model where a unit increase in level of education leads to a 0.913 increase in the adoption index.

Experience was found to be positively related to adoption index. Results indicated that for every year’s experience gained by the farmers, there is an increase of 0.22 in the adoption index. As experience grows, the farmers become very conversant with old methods and are willing to try out new and challenging technologies. From the results of the model, farm size was positively related to the adoption index though not significant at 5%. A unit increase in the number of hives leads to a 0.027 increase in adoption index. This is because increase in number of hives leads to increase in production and the overall income earned from the enterprise. As earlier stated, levels of income are positively related to the adoption index.

In this study, the following factors affect the adoption of beekeeping and associated technologies: age of the farmer (years), education level (primary, secondary or tertiary), experience in beekeeping (years), apiary size (number of hives), extension services (number of sources of information), and access to credit (1 for access to credit; 0 other wise). A unit increase in the independent variables had an effect on adoption index. However, age of respondents, level of income, experience in beekeeping and size of the farm were insignificant at 5%. Extension services were significant at 10% whereas, access to credit was significant at 5%.

Majority of the farmers (62%) would consider the level of income at the house hold level before they decide to adopt a new technology (Table 9). This is mainly due to the fact that the level of income dictates the level of expenditure, and since most of the new technologies are money demanding, then a household with high levels of income is likely to adopt the new technologies compared to those with less income. In relation to income, access to credit facilities was found to be positively related to adoption index at a 5% significant level (Table 8).In Nigeria, only 13.3% of the farmers receive agricultural credit (Agwu et al 2008), and the situation elsewhere in developing countries does not seem to differ. Lack of access to credit facilities constitutes a constraint to purchase of beekeeping materials. This implies that access to funds in form of loans to inject into the project is capable of increasing the rate of adoption of the various beekeeping technologies.

The level of information disseminated to the people about a given technology would affect 55% of the farmers (Table 9) on their ability to take up the technology. Information on a technology makes it more understandable to the farmer hence increasing their zeal to adopt it. Good extension services pray a major role in dissemination and hence adoption of technologies. This was further confirmed by the regression model which indicated a significant and positive relationship (P<0.01) between extension services and adoption. For every unit increase in the number of sources of information, the adoption index increases by 2.69 units (Table 8). The study of Karki (2004) in Nepal also revealed that a unit increase in extension services increases adoption rate by 3.32 units (P<0.05).Majority of the farmers confessed to receiving information mainly from extension workers (Table 5) implying that awareness does not necessary lead to adoption (Adereti et al 2006), as the farmers were largely aware of the innovations yet they did not adopt all the innovations.

Connected to the level of income, the cost of the technology also affects the rate at which it is adopted. More than half of the farmers agreed to this, and the effect could be due to the costly technologies like Langstroth hives which are adopted at a lower rate compared to the fairly cheap technologies like Top bar hives. This implies that wealthier farmers are more likely to adopt capital intensive technologies (El Oster and Morehart 1999) compared to poor farmers.

Few farmers (35%) reported availability of the technology to be affecting their adoption of it. Conventional technologies are usually adopted at a higher rate (Seon-Ae et al 2006) compared to those that are newly introduced or not locally available. For example, local hives are easier to adopt compared to improved hives because every beekeeper has got some knowledge on how to construct local hives and the materials are locally available (Kugonza 2009). Top bar hives which can be constructed locally in Bushenyi had a higher adoption rate, in our study, reflected by the 19.5% ownership level among households compared to 0.5% rate of Langstroth hive presence (Table 2).

The more technical an innovation appears to be, the less it’s likely to be adopted by the farmers. More farmers in Bumbaire reported this compared to those in Kyamuhunga, attributable to the high literacy levels in Kyamuhunga. There is a high possibility that educated farmers will adopt technically difficult technologies than the less educated due to their ability to read and appreciate the technicalities involved. Nevertheless, in this study, the correlation between education level and use of improved technologies such as top bar hives was not significant (r = – 0.106, P = 0.62, n = 32).

The potential of the technology in terms of inputs, outputs and specifically profit returns is also an important factor though portrayed by only 32.5% of the farmers in the this study. Most farmers consider how long it takes them to break-even after adopting a given technology. Therefore, the quicker the farmer can start profiting from the technology, the higher the rate of adoption. It is likely that if the level of adoption of beekeeping is compared uptake of other agricultural technologies, beekeeping would have an edge on the other technologies, due to the faster rate of profiting from the beekeeping business. However, this could not be ascertained in this study. Abebe (2008), in his study “Adopting improved box hive in Atsbi Wemberta district” reported the higher the yield obtained from the introduced technology, the easier it is to convince the farmers to adopt the technology.

Farmers in a given group tend to have and use the same methods of production hence adoption of a technology by a group member would greatly influence the interests of other members in the particular technology. Only 18.3% of the farmers recorded this as a factor affecting adoption. This is mainly due to the group discussions held during meetings where members share information on how to operate effectively and profitably about the newly acquired techniques. In the current study, the level of adoption of the various hive types did not associate (P > 0.05) with particular villages or a particular sub-county, for instance, the correlation between a particular village with number of traditional hives was r = –0.26 and with Top bar hives was r = –0.198. However, top bar hives are adopted at a relatively same rate (Mean = 10.08, SEM = 1.32). In Nepal, Ethiopia, Bhusal and Thapa (2004) revealed that mobilized farmers practiced beekeeping technology with the higher index of adoption (77.44%) which was 1.29 times higher as compared to non-mobilized farmers.

Indigenous innovations by beekeepers in the study area

Queen excluder

After learning about the advantages of the queen excluder in the Langstroth hives, 4% of the farmers have come up with a way through which they locally construct a queen excluder in the traditional hives. Their innovation has enabled the production of brood-free honey, thus improving on the quality of honey produced and marketed in the area.

Local hive catcher box

Some farmers (2%) have turned beekeeping into an innovation ground after developing a swarm catcher for local hives. With a size of one foot in length and a diameter of 0.75 ft, a small basket hive which upon colonization is ready to be attached to a bigger traditional hive (Plate 2) is a major innovation. After attachment, the swarm catcher remains in position for at least four weeks and the bees extend their colony into the new bigger room on their hive. After fully inhabiting the hive, the box is detached and transferred to another hive. Unlike the Top bar and Langstroth catcher boxes, the local hive swarm catcher is colonized once and serves for a life time.

Space utilization

While other beekeepers lack space to expand their apiaries, some farmers (1%) have figured out a way to build permanent hive houses (Plate 3) where each house accommodates at least 5 hives on an area of one square meter. In addition to minimizing space, this innovation ensures enough warmth inside the hives and absconding of colonies is minimal.

Conclusions

Recommendations

References

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Table 2. Types of hive, average production per hive and colonization rates
	Proportion (%) of beekeepers		Average production per hive (kg ± SEM)		% of hives colonized
	Bumbaire	Kyamuhunga	Honey	Beeswax
Types of hives	(n = 30)	(n = 30)
Traditional	76	36	6.2 ± 0.35	0.4 ± 0.01	90
Top bar	21	18	4.5 ± 0.19	0.3 ± 0.01	89
Johnson	2	46	3.3± 0.17	0.3 ± 0.01	92
Langstroth	1	0	9.1±0.01	0.6 ± 0.03	100

Table 8. Results of the multiple regressions
Variables		Β	Std. Error	T-statistic	Sig.
(Constant)	α	12.1	5.86	2.08	0.045*
Age of respondent	X₁	- 0.051	0.084	- 0.605	0.549
Highest level of education	X₂	0.913	1.36	0.672	0.506
Experience in beekeeping	X₃	0.22	0.183	1.19	0.239
Farm size	X₄	0.027	0.058	0.476	0.637
Extension services	X₅	2.69	1.44	1.87	0.070**
Access to credit	X₆	5.66	2.39	2.36	0.024*
Dependent Variable: Adoption index; R² = 0.41, significant at 5%, ** significant at 10%*

Table 9. Factors affecting adoption of new beekeeping technologies
	Respondents per Sub county (%)^§

Variables	Bumbaire(n = 20)	Kyamuhunga(n = 20)
Level of income	60	64
Information about the technology	50	60
Cost of technology	50	53
Availability of the technology	30	40
Technicalities involved	40	25
Returns from the technology	35	30
Adoption by neighbours	15	22
^§Values are not mutually exclusive

Table 4. Constraints faced in beekeeping
Variables	Proportion of farmers (%)#
Variables	Bumbaire	Kyamuhunga
Lack of equipment	70	60
Bad weather	65	62
Pests and diseases	70	55
Transport difficulties	45	60
Labour shortages	45	55
Inadequate skill/knowledge	25	30
Thieves	20	10
Low prices	0	6
# Values not mutually exclusive

Table 5. Source of information on farming
	Beekeepers^§		Non beekeepers^§
	Bumbaire (n = 30)	Kyamuhunga (n = 30)	Bumbaire (n = 20)	Kyamuhunga (n = 20)	Total
Variables	Proportion (%) of respondents
Fellow farmers	100	100	86.7	86.7	93.3
Extension workers	75	75	93.3	80	80.8
Books	15	45	40	20	30
Radio	30	15	40	20	26.5
News papers	0	10	0	0	2.5
^§Values are not mutually exclusive, as respondents gave more than one source of information