Rural revitalization through improvements of technical efficiency in honey production: Evidence from Horo Guduru Wollega Zone, Oromia, Ethiopia

Abstract

Making apiaries more effective is only one aspect of reviving the beekeeping industry. Additionally, the beekeeping industry is generating employment both in rural and urban locations. This study’s goal was to identify the technical levels of honey production in Ethiopia’s Horo Guduru Wollega zone and their contributing factors. To accomplish the aforementioned objective, structured questionnaire data collected from 396 households were used. Stochastic production frontier estimator shows that the number of hives, the amount of work put into producing honey, and the area of the land all significantly influenced the amount of honey produced. In the mean technical of both traditional and modern hives, there were 56.68% and 73.93%, respectively. This demonstrates how technically more efficient farmers who use contemporary hives to make honey are. According to mean technical efficiency, both beekeepers were, however, operating below the production frontier. Household sex, credit utilization, extension services, training, beekeeping experience, and family size were significant technical efficiency variables for honey producers. The study suggests policies to address technical inefficiencies by increasing the number of hives, extending the best performers’ experience by increasing the frequency of extension contacts on honey production, facilitating and expanding credit service in the study area, making bee forage access simple, and increasing forest coverage on land area in line with current policy of Ethiopia. Additionally, since farmers in the study area spend their time guarding the honey from damage by ants, labor that utilizes technology must be made available.

Keywords:

• Honey production
• stochastic model
• one stage
• Technical inefficiency

1. Introduction

In addition to having a large number of bee colonies and a very diverse honeybee flora, Ethiopia is famous throughout Africa for its beekeeping potential (Global Business Network, 2021). Beekeeping has a long tradition among Ethiopian farmers, nation potential for producing it. Ethiopia has produced far less honey than was predicted, which has a negative effect on overall output and productivity (Shiferaw and Berhanu, 2015; Ozturk, 2017; Tsegabirhan et al., 2015). Ethiopia’s reputation as a country with abundant apicultural resources is due in part to the high number of native farmers and other people who have been involved in the growth and administration of apiculture for many generations (Wakjira et al., 2021). However, beekeepers in Ethiopia have limited access to contemporary hive equipment and beekeeping management techniques. The apicultural sector is well below its potential due to these obstacles (SAMS (Smart Apiculture Management Services., 2017). The ability to grasp production given inputs and close the yield gap is aided by spatial variation of technical efficiency in farmers’ fields (Zhou et al., 2021). Ethiopia’s honey industry will be a sustainable source of income, helping families escape poverty and promoting ecosystem balance and land restoration for the benefit of present and future generations hence with increased production capacity (Alropy et al., 2019). Agro-chemical poisoning, a shortage of bee food, a lack of rainfall, pests and predators, absconding, and a lack of honey storage facilities were other factors that restricted the latent potential of beekeeping (Khan et al., 2019). Nowadays, relative poverty in rural regions can be reduced through the contribution of agricultural factor endowment (Song et al., 2022). To boost the productivity and efficiency of honey production, farmers’ socioeconomic, agricultural and institutional qualities must all be improved (Alropy et al., 2019). As a result, there is low productivity, which lowers the country’s contribution to its agricultural Gross National Product (GNP). In order to increase productivity; honey producers’ economic efficiency must be improved. Most empirical studies (Adzawla & Alhassan, 2021; Dessale et al., 2017; Pangapanga-Phiri & Mungatana, 2021; Tesfaw et al., 2021) concentrated on the efficiency of crops. There was little information available on technical efficiency in the honey production. Majority of empirical investigations show that the efficiency levels of smallholder farmers vary (Adom & Adams, 2020; Bati & Gemechu, 2020; Elijah et al., 2016; Hailemariam et al., 2020; Obianefo et al., 2020; Bahlol & ELkhayat, 2021; Soh et al., 2021; Su et al., 2021). Their failure to compare the technical efficacy of honey produced in traditional and modern hives is the research’s principal shortcoming. In order to increase the smallholder farmer’s performance in Ethiopia, the government has placed a lot of emphasis on the modern hive. Hence, the following question was addressed by using this study. What are the levels of technical efficiency in tradition and modern hive? And what are the determinants of technical efficiency in honey study area? As a result, policymakers in that country have to be aware of how efficient these hives are as well as the variables that affect how well they perform. Therefore, this work closes this gap in the literature. This study fills the information and knowledge vacuum in Ethiopia’s Horo Guduru Wollega Zone by comparing performance of traditional and modern hives.

2. Methodology

2.1. Description of study area

The Horo Guduru Wollega zone of Ethiopia far 310 km to the west of Addis Ababa in which Shambu is its administrative zone’s capital. There are nine districts for administration. There are 576,737 people living in the zone, 50.1% of whom are men and 49.9% of whom are women (CSA (Central Statistical Authority), 2007). The zone’s rural parts are home to about 89% of its residents. 712,766.22 hectares make up the entire zone. In terms of agro ecology, highlands make up 37.89%, mid lands make up 54.75%, and lowlands make up 7.86%. Before collecting the data, permission from the Wollega University research and innovation director were obtained. The office of agricultural and rural development gave its verbal approval for data collection.

2.2. Sampling Methods and Sample Size

Using a multi-stage random choice mechanism, sample respondent households were chosen for this investigation. Guduru, Abe Dongoro, and Amuru were specifically picked for the first phase out of the Zone’s 11 districts because among their potential for production. Two Kebeles (peasant associations) were chosen for the second phase from each district. In the third step, 396 sample households were selected randomly from each of the chosen Kebeles with a probability proportional to size, and data was gathered employing questionnaire survey. The sample size was determined based on Yamane (1967).

(1) $n=\frac{N}{1+\left(e^2\right)N}$(1)

Where, n is sample size, N is number of households in the zone which is 37,161 and e is the desired level of precision at 5%.

2.3. Data collecting techniques

To acquire data at the household level, a survey was conducted utilizing structured questions. The data included inputs and outputs for honey (number of hives, land size in hectares, land held by households, labor, and socioeconomic status). Enumerators who are proficient in both the native tongue and English were assigned. In order to prevent bias researcher and pertinent enumerators sufficiently explained the study’s goals during the interview.

2.4. Specification of an econometric model for estimating technical efficiency levels

In order to measure the level of efficiency and the significance of the numerous variables in determining the inefficiency of honey producers, the production frontier was first specified in this work using a Cobb-Douglas function. Stochastic frontier is the method that works best for efficiency studies that may be impacted to overcome measurement and observation errors occur while data is being collected and also unpredictable meteorological conditions that have an impact on the efficiency of honey in the Horo Guduru zone but are beyond the control of the farmers. The Cobb-Douglas functional form’s most pleasing superiority, rendering is its simplicity (Mkhabela, 2005; T.J. Coelli et al., 2005). Although the Cobb-Douglas model assumes unitary elasticity of substitution, constant production elasticity, and constant factor demand, it will adequately represent technology and have little effect on efficiency measurement if the concentration is on efficiency measurement rather than the overall structure of the production function (T. J. Coelli et al., 2006). When farmers operate in small farms, the technology is unlikely to be substantially affected by variable returns to scale. Besides, according to small holders farming, the technology is unlikely to be substantially affected by variable returns to scale and therefore it is better to use Cobb-Douglas production function than Translog function (Battese & Coelli, 1988; Castiglione, 2012; Madau, 2007). The alternatives such as Trans log production functions also have their own limitations such as being susceptible to multicollinearity and degrees of freedom problems.

Following Aigner et al. (1977) the stochastic frontier model is defined as:

(2) $\mathrm{l}\mathrm{n}\mathrm{y}\mathrm{i}=\beta \mathrm{o}+\sum _{\mathrm{j}=1}^3\mathrm{b}\mathrm{j}\mathrm{l}\mathrm{n}\mathrm{x}\mathrm{i}\mathrm{j}+\mathrm{e}\mathrm{i}\mathrm{j}$(2)

$\mathrm{e}\mathrm{i}\mathrm{j}=\mathrm{V}\mathrm{i}-\mathrm{U}\mathrm{i}$

(3) $\mathrm{l}\mathrm{n}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{h}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{y}=\beta \mathrm{o}+\mathrm{l}\mathrm{a}\mathrm{n}\left(\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{h}\mathrm{i}\mathrm{v}\mathrm{e}\right)+\mathrm{l}\mathrm{a}\mathrm{n}\left(\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}\right)+\mathrm{l}\mathrm{a}\mathrm{n}\left(landsize\right)+\mathrm{e}\mathrm{i}\mathrm{j}$(3)

(4) $\mathrm{e}\mathrm{i}\mathrm{j}=\mathrm{V}\mathrm{i}-\mathrm{U}\mathrm{i}$(4)

According to Aigner et al. (1977), the symmetric component (vi) is assumed to be independently and identically distributed as N (0, σ²v). On the other hand, ui is non-negative truncated half normal random variable with zero mean and constant variance, σ²u (Narcisse et al., 2019).

The technical inefficiency effects are expressed as:

(5) $\mu \mathrm{i}=\mathrm{z}\mathrm{i}\delta +\mathrm{w}\mathrm{i}$(5)

$\mu \mathrm{i}$= is inefficiency effects of the i^th firm

$\mathrm{z}\mathrm{i}$ = is a (1xm) vector of variables explaining technical inefficiency effects such as

x₁ = Types of hive (1 if modern and transitional hive, 0 if traditional hive)

x₂ = Educational levels in year of schooling of household heads.

x₃ = family size of household measured in number.

x₄ = Extension contact (the number of contacts received for honey producers in year)

x₅ = Training on honey production activities (1 if received training and 0 if not)

x₆ = Experience in honey production (year of experience of household in beekeeping)

x₇ = Market distance (far of honey producers from the market in minute)

x₈ = Credit service of household (1 if honey producers receive credit service and 0 if not)

δ = is an (mx1) vector of unknown parameter and

wi = is error term that are assumed to be independently distributed, as an alternative. Thus the means E (μi) = E (ziδ +wi) = ziδ (may be different for different farmers). In this work, the production frontier function and the inefficiency effects model’s one-stage estimate approach are used. Unreliable parameter estimates are allegedly produced by the two stage techniques which have advantage more than one stage (T. Coelli et al., 1998; Yang & Chen, 2009).

3. Result and discussion

3.1. Cobb-Douglas stochastic frontier’s

Before beginning the process of model estimation, all stochastic frontier technique assumptions were examined. The labor input required, the total number of hives, and the size of the land all had positive coefficients, indicating a positive relationship with honey output at 1% levels of significance. Additionally, the fact that these variable inputs have positive coefficients implies that output is increased by increasing their quantities. The amount of honey produced will increase by 0.8575, 0.0667 and 0.3071%, respectively, if the number of hive, labor and land size used for honey production are all increased by 1% each (Table 1). In Table 1, it is shown that the total partial elasticity of all inputs is equal to 1.23 which were increasing return to scale. The production may rise by 1.23% for every 1% increase in these inputs, according to this calculation. As a result, it was important to assess the technical efficiency of honey production and identify the causes of inefficiency. The results were then compared to the observed or estimated average honey yield, and the productive efficiency of honey production was computed. It should be noted that all of the parameter coefficients for the production variables were calculated using the physical quantity value. Farmers in the research region said that it was inefficient despite the fact that the total number of hives, labor, and land size all contribute to a high level of honey output.

Table 1. Input factor and determinants of honey production according to the stochastic frontier model

Variables		Coefficient		Standard error		Z-vale		p-value
Lan of number of hive		0.8575***		0.02922		29.34		0.001
Lan of labor		0.0667***		0.0208		3.20		0.001
Lan of land size		0.3071***		0.03891		7.89		0.001
constant		1.574***		0.13628		11.55		0.001
Inefficiency variables
Types of hive		−0.5249**		0.2111		−2.49		0.013
Education		0.018605		0.03121		0.60		0.551
Family size		−0.3667***		0.05203		7.05		0.001
Extension service		0.00746*		0.0039		−1.91		0.071
Training		0.67874		0.3969		0.234		0.202
Experience in bee keeping		−0.04699***		0.01138		−4.13		0.001
Distance from the market		−0.0003		0.00531		−0.06		0.951
Credit service of household		−0.50894**		0.22534		−2.26		0.024

Note:*,** and *** represent the level of significance at 10%,5% and 1% respectively.

The negative and statistically significant types of hive coefficient on the technical inefficiency of honey are present at 5% levels of significance. This shows that a household headed by a contemporary or transitional hive is more technically efficient than others. This is a result of the fact that contemporary hives are more prolific and resistant to ants and summertime rain. Honey products are lost during harvesting more often than not in the study area’s traditional hives, which are gathered from the trees. These obstacles hinder conventional hives, which decreases the effectiveness of agriculture. This conclusion is backed by that of Danso et al. (Danso-Abbeam et al., 2020).

Sign of the family size was negative with technical inefficiency as expected at 1% level of significance, showing that farmers with smaller families are less technically inefficiency than those with larger families. This might be the case because a large family can manage difficulties with honey production and productivity. A restriction was that the study area and had to consume the bees from the hive at night. So that the family can stop the ant from hurting the bees. This conclusion was in line with the vast majority of the research that were looked at, including Andaregie and Astatkie (2020).

At the 10% of significance, the association between the technical inefficiency of honey production and extension service was statistically significant and positive. It was demonstrated the quality of the extension service was low due to worries about dread of political situations of extension worker for advising farmers. Another reason could be that farmers did not properly implement the service they received, or that the service was not related to honey production and was instead used for other products in the research region, such as grains and vegetables. This study’s outcome is similar with the findings of Abate et al. (2019) and Wassihun et al. (2019); they argued that unexpected result of extension contact was due to biasedness in extension program.

The coefficient of bee beeping experience was significance and positively related to technical inefficiency at 1%. This is because the person will be able to boost bee production and management as their experience level rises. In a similar vein, it is asserted by Khanal et al. (2018) and Long et al. (2020) that a farmer’s sensitivity to new production techniques improves with his level of involvement in farming, which in turn raises productivity.

At 5% of significance, credit service has a substantial effect on honey and has negative relationship with technically inefficiency. Producers of honey who had access to financing were more likely to be technologically proficient than those who did not. They might be able to escape their financial confines with the aid of a credit agency, enabling them to use more effective technological strategies. Financial services may also make it possible for beekeepers to buy supplies or obtain physical capital, both of which are necessary for embracing new technology and forcing farmers to buy contemporary hives when they are short on money. This indicated that having financial resources could help beeping production be more productive. Creating government financial support would enhance farmers’ ability to live sustainably and eliminate absolute (AlFraj & Hamo, 2022; Zeng et al., 2021).

3.2. Technical efficiency scores for various types of hives in the sample household

An effective beekeeper enhances the likelihood that each type of hive will produce the most honey. The mean technical of conventional and contemporary hives, as indicated in Table 2, was 56.68% and 73.93%, respectively. This demonstrates the importance of mean technical efficiency disparities between each type of hive (traditional and modern hives). A summary in Table 2 demonstrates that, at 1% levels of significance, there is a considerable mean technical efficiency difference between conventional and modern hives. Comparatively speaking, farmers who produce honey using old hives are more technically proficient than farmers who produce honey using modern hives. As a result, the average overall technical efficiency was 63.96%, means if size adjustments were made, farmers could reduce their inputs by 36.04% while keeping the same level of output. This was explained by prioritizing raising crops and livestock, while beekeeping is viewed as a supplemental activity. Farmers give priority to cereal production and grain crops over beekeeping because the majority of the world’s least developed nations depend on agriculture for existence.

Table 2. Efficiency score for each types of hive

Variable	Observation	Mean	Std. error.	T-value
Traditional hive	229	0.5668	0.008623	15.15***
Modern hive	167	0.7393	0.006
Pooled	396	0.6396	0.007

***: p <10% significance level

3.3. Distribution of Traditional and modern hive technical efficiency

Results show that for traditional hives 44.97% of the honey producers were in the range of 60–70% technical efficiency ranges (Figure 1). Hence, by acting in a way that minimizes costs, households in these ranges have to save at least 55.03% of input costs. Secondly, the 26.2% of household had efficiency range of 50% to 60% lastly only 8.73% of all sampled households had a technical score between 70% and 80%.

Figure 1. Distribution of traditional and modern hives according to technical efficiency.

In a sense, between 70% and 80% have technical efficiency of 59.28% of the technically proficient farmers using modern hives to produce honey, and experienced failure. Only 3.59% of the sample’s total households fall into the technical efficiency class of less than 50%. Inferentially, this means that 96.41% of households can raise output by at least 50%.

4. Conclusions and implications

Ethiopian honey production is regarded as a financially viable undertaking because investments in this produce significant returns quickly. To revitalize rural greater care and attention must be given to this significant culinary and economic product, as well as the need to qualify personnel and stimulate investment in the sector by offering suitable loans and financing. Technical efficiency has continued to be a crucial area of empirical research. In the Horo Guduru Wollega region of Oromia National Regional State, the technical effectiveness of beekeepers was examined, as well as the factors that contribute to variations in technical effectiveness. Detailed information about the production of honey was requested from 396 farmers. The level of technical efficiency of honey producers was examined, as well as the causes of differences in technical inefficiency among them, using a one-stage approach and a stochastic production frontier model with inefficiency effects. The findings show that the number of honey hives, the size of the land, and the size of the land are all significant determinants in the production of honey. These elements all have positive coefficients, therefore it stands to reason that as these inputs are employed more frequently, honey output will probably rise more quickly. The research area’s deteriorated and destroyed forests, which result in low honey sector output, forced the Ethiopian government to launch the green legacy initiative of planting trees every year. In the short term, beekeepers might cut their inputs by 34% based on the mean technical efficiency that was provided. Regarding elements that affect technical efficiency, the following guidance was given to revitalize rural area thought improvements of honey production. First, the government must supply contemporary hives for in the research area at no cost or with subsidies, as the types of hives have a favorable impact on the technical efficiency of honey producers. Second, the farmers who have larger families are generally more technically efficient than those who have smaller families. This is so that large families can manage bee waste management and other honey production tasks including safeguarding bees from ants. Since the household used their own hand to waste out the honey output from hive in the study area. Thus, governments have to provide capacity building for the household on new technology of pulling out honey from honey hives and health facility for those household. The technical efficiency of beekeepers greatly increased by extension contact. However, the manner in which it was presented was acceptable for the study area given that the extension agents were more focused on livestock and crops than the honey industry. Therefore, it is vitally crucial to increase extension workers’ expertise of honey production. Because of this, the regional government should assist farmers in educating extension agents who have a focus on the beekeeping industry. The extension of the farmers’ training center or the increase both official and informal education in the region could be used to carry out this. Furthermore, as experience is the primary determinant of the technical efficiency of honey production, governments must expand their understanding of contemporary beekeeping. The technical efficacy of honey production is mostly determined by credit. However, credit is to purchase cereal crop inputs, including maize, wheat, barley, and potatoes. Therefore, it is necessary to construct specialized microfinance in the study area and throughout Ethiopia that focuses on lending to beekeepers and flows up how they use their loans for beekeeping. The profitability of honey production and the effects of efficiency on honey production must be the main topics of future research.

Ethical standards

Authors followed ethical principles of data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Wollega University [25000].

Notes on contributors

Tolesa Tesema

Tolesa Tesema were employed in government institution under ministry of education from Ethiopia and serving as lecturer and engaged in research and community service activities under the departments of agricultural economics at Wollega University.Current Authors published five research papers in Scopus index journals.

Megersa Adugna

Megersa Adugna was employed in government institution under ministry of education from Ethiopia and serving as lecturer under the departments of economics in Wollega University.

Seid Hassen

Seid Hassen Were employed in government institution under ministry of education from Ethiopia and serving as lecturer under the departments of agricultural economics in Wollega University.