Factors influencing organic honey production level and marketing: evidence from southwest Ethiopia

Abstract

The primary aim of this study was to identify factors influencing natural honey production and marketing constraints in Kafa, Sheka, Bench- Sheko, and West Omo zones which covered 23 woredas and 69 kebeles. Primary and secondary sources of data were utilized for this study. Both qualitative and quantitative data types were employed. Data were collected from 94, 134, 118, and 39 respondents that were selected randomly from Bench- Sheko, Kaffa, Sheka, and West-Omo Zones respectively, based on probability proportional to the sample size. The collected data were analyzed by using descriptive statistics and a multiple linear regression model. The dominant honey production practice in the study area was the use of traditional beehives. The productivity of traditional, transitional, and modern beehives was 9, 16, and 22 kg per hive. Major constraints that affect honey production include lack of modern technology (92.5%), absconding (69.5%), pests and predators (46.8%), lack of credit access (28.3%), poor extension service (57.4%), lack of beekeeping equipment's (45.2%) and death of colony (38.05). Similarly, poor market linkage (84.1%), lack of market information (66.2%), poor infrastructure (61.5%), low price of product (60.7%), weak bargaining power of farmers (37.5%), long-distance to market (88.4%), shortage of packing and storage materials (57.6%), presence of illegal traders (53.5%) and absence of branding (60.3%) are factors that influence honey marketing in the study area. The econometric result showed that variable cost, age of the respondent, marital status, experience, and hive number owned influence the level of honey production. The policy should focus on creating access to modern honey bee technologies, providing capacity building for producers, organizing cooperatives, providing credit services, promoting the involvement of private sectors, establishing linkages among honey producers, researchers, and private sectors.

Organic Honey; Beehives; Marketing, Production, Constraints, Assessment, Multiple Linear Regression

1. Introduction

In Ethiopia, honey production deeds are one of the ancient agricultural activities (Bekena and Greiling, 2017) that expedited with favorable natural resource endowment, distinctive agro-ecological conditions, and over seven thousand flowering species (Admassu et al., 2014; MoA and ILRI, 2013) and have a comparative advantage in honey and wax production (MoA and ILRI, 2013). The country is among the foremost honey producers in Africa and the world. There are over ten million bee colonies and one point eight million beekeepers exist in Ethiopia (Bekena and Greiling, 2017). It is the fourth largest honey producer next to the Republic of India, China, and Turkey in the world by having 6,958,004 beehives (CSA, 2020) which produced 53782 and 5790 tons of honey and beeswax respectively (FAO-STAT, 2021).

In Ethiopia, honey production is much less from grasping its potential for earning foreign currency generally and generating financial gain for honey producers specifically. The country has the potential of manufacturing up to five thousand tons of honey and fifty thousand tons of beeswax every year (Assefa, 2017). But the recent production level of honey and beeswax is a smaller amount than ten percent of its potential, exploitation of alternative high-value honeybee products is non-existent (MoA and ILRI, 2013) and also the gain from the subsector is not adequate (Beyene and David, 2010). Identifying the issues that hinder honey production, productivity and marketing is pivotal for further intervention and improving the subsector.

The dominant honey-producing regions in Ethiopia are Oromia, Amhara, and SNNPR (Southern Nations, Nationalities, Peoples Regional Government) that accounts for 54.3%, 19.5%, and 16.6% of the total production (CSA, 2020). The Southwestern part of Ethiopia which is endowed with dense natural forest, appropriate environmental conditions, and different species of flora and fauna has a prominent potential for honey production (Ito, 2014; Tarekegn et al., 2018). Different researchers like Tarekegn et al. (2017a,b); Tarekegn and Ayele (2020); Tarekegn et al. (2017a,b); Erkalo (2017); Belete (2015); Shenkute et al. (2012); Albore et al. (2019) conducted a study on the honey value chain, honey production systems, technology adoption and market outlet selections of honey in southwest Ethiopia. But none of these studies addressed factors affecting organic honey production level and marketing. Therefore, this study aimed to identify factors poignant honey production in the Bench-Sheko, Kafa, Sheka, and West-Omo zones of southwest Ethiopia.

2. Research methodology

2.1. Description of the study area

This study was conducted in Bench- Sheko, Kaffa, Sheka, and West Omo zones of SNNPR. The study was conducted in 23 woredas and 69 kebeles (Table 1). The detailed description was explained as follows:

Table 1.

Study area description.

Description	Former Bench- Maji (current Bench-Sheko and West-Omo zones)	Kaffa Zone	Sheka Zone
GPS location range	50 30’ -70 00′ N to 34000′-360 00′ E	60 24′-70 70′ N to 350 69’ - 360 78′ E	70 24’ - 70 52′ N to 350 13’ - 350 35′ E
Zonal Towns	Mizan Aman and Jemu	Bonga	Masha
Annual Rainfall (mm)	1500–1800mm	1600–2200	1800–2200
Temperature (C0)	15 °C–29 °C	18–21	15 °C–27 °C
Altitude (m.a.s.l)	1200 to 1959	500 to 3350	900 to 2750
Total population (2017)	847,168	1,102,278	269,243
Bordered	on the south by the Ilemi Triangle, on the west by South Sudan, on the northwest by the Gambela Region, on the north by Sheka, on the northeast by Kaffa, and the east by Debub Omo	on the south by Debub Omo, on the southwest by Bench Sheko, on the west by Sheka, on the north by the Oromia Region, and on the east by Konta	on the south by Bench Sheko, on the west by the Gambela Region, on the north by the Oromia Region, and on the east by Kaffa

2.2. Research design

Both qualitative and quantitative data types were used in this study. Both primary and secondary data sources were used. Primary data were collected through a household survey by using questionnaires, focus group discussions, key informant interviews, field observations, and market assessment. In each woreda three focus group discussions were conducted. A total of 69 focus group discussions in which each composed of 8 honey producers were performed. In each woreda, 5 key informants were interviewed concerning honey production and marketing problems. Secondary data were collected from official reports and published documents.

In this study, a multi-stage sampling technique was followed. In the first stage, the four zones (Kafa, Sheka, Bench Sheko, and West Omo) and all 23 (twenty-three) woredas of the four zones were selected based on the recommendation of the SNNPR special support office. In the second stage, three kebeles1 from each woreda2 were selected purposively based on the production potential of honey. The sample size was determined by using the following formula of Morgan (1970) (equation 1).

$$n=\frac{Z^{2}P(1-P)}{D^2}=385$$ (1)

where, n = sample size, Z² = 95% = 1.96 & e = level of precision (5%).

P = the population proportion (assumed to be 0.5 for it provides the maximum sample size) and q=(1-p). Based on the above formula, 94, 134, 118, and 39 producers and 14 traders, 10 cooperatives and unions, and 20 processors were selected randomly from Bench- Sheko, Kaffa, Sheka, and West-Omo Zones respectively, based on probability proportional to the sample size.

The collected data were examined by using descriptive and econometric methods. Descriptively, mean, percentage, and frequency were used and an econometrically multiple linear regression model was used to identify factors influencing the level of honey production. A highly skewed variable was converted into log form because logarithmic conversion is a suitable way to convert highly skewed into a more normalized dataset (Table 2).

Table 2.

Summary of variables and expected signs.

Variable	Description	Type	Expected signs
Lnyield	Production level by the kilogram
lnAge	Age of the respondents	Continuous	+/-
Education level	The education level of the respondents	Continuous	+
Marital status	Marital status of the respondents	Dummy	-
Family size	Family size of the respondents	Continuous	+/-
Lnworkage	Member of the family that exists in Working Age	Continuous	+/-
Sex	Sex of the respondent	Dummy	+
Lnvariable price	The variable cost of honey production equipment's	Continuous	+/-
Hive number	Number of honey possessed by respondents	Continuous	+/-
Cooperative membership	Membership status of the respondents in cooperatives	Dummy	+
Lnincome	Income level of the respondents	Continuous	+
Lndistance	Distance from the nearest market center of the respondents	Continuous	-
Lnexperience	Experience of the respondents in honey production	Continuous	+
Training	Access to the training of the respondents	Dummy	+
Lnprice	The selling price of honey	Continuous	+/-

Specification of multiple linear regression model (equation 2)

Log Yi = β₀ + β ₁ Log x_1i + β ₂x_2i + β ₃x_3i + β ₄x_4i + β ₅ Logx_5i + β ₆x_6i + β ₇ Logx_7i + β ₈x_8i + β ₉x_9i + β ₁₀ Logx_10i + β ₁Logx_11i + β ₁₂ Logx_12i + β ₁₃x_13i + β ₁₄x_14i + μ (2)

whereas, LnYi = production level of honey in kilogram, x₁ = Age of the respondent, x₂ = The education level of the respondents, x₃ = marital status_, x₄ = Family size of the respondents_, x₅ = Member of the family that exists in Working Age_, x₆ = Sex of the respondents_, x₇ = The variable cost of honey production equipment's_, x₈ = Number of honey possessed by respondents_, x₉ = Membership status of the respondents in cooperatives_, x₁₀ = Income level of the respondents_, x₁₁ = Distance from the nearest market center of the respondents_, x₁₂ = Experience of the respondents in honey production_, x₁₃ = Access to the training of the respondents_, x₁₄ = The selling price of honey.

3. Results

3.1. Socio-demographic characteristics of respondents

In the study woredas, 95.7% of respondents were males and the rest of the respondents were females. It indicates that honey production is dominated by males and women's participation was low. Regarding marital status, 94.65% of respondents were married while 5.35% of respondents were single (Table 3). The major occupation of the interviewed respondents was agricultural farmer which accounts for 95.7% of respondents. This result implies that since the majority of respondents depend on agriculture for their income, it creates a suitable condition for beekeeping as it can go side by side with other agricultural activities.

Table 3.

Sociodemographic characteristics (Dummy variable).

S/N	Characters		Zones (Frequency/Percent)				Average (%)
			Bench-Sheko	Kaffa	West-Omo	Shaka
1	Sex	male	119 (96.7)	116 (94.3)	121 (99.2)	114 (92.6)	95.7
		Female	4 (3.3)	7 (5.7)	1 (0.8)	9 (7.4)	4.3
2	Marital status	Single	5 (4.1)	9 (7.4)	2 (1.7)	10 (8.2)	5.35
		Married	118 (95.9)	114 (92.6)	120 (98.3)	113 (91.8)	94.65

Source: Authors computation, 2020.

The mean age of the respondents was 42.5, education level (4 years of schooling), family size (7.15), working-age (3.14), and experience (18.8 years) (Table 4). The higher mean educational level (6.2) was obtained in Sheka zone whereas, the average education level in the West-Omo zone were of (0.56).

Table 4.

Sociodemographic characteristics of respondents (Continuous Variables).

S/N	Characters	Zones (Frequency/Percent)				Average (%)
		Bench-Sheko	Kaffa	West-Omo	Shaka
4	Age (Mean)	46.9	40.5	39.37	43.4	42.5
5	Educational level (Mean)	4.67	4.56	0.56	6.2	4
6	Family size (Mean)	7.4	7.3	6.9	7	7.15
7	Working age size (Mean)	3.28	4.31	2.8	3.34	3.4
8	Experience of beekeeping (in years)	11.6	18.64	23.5	21.6	18.8

Source: Authors survey, 2020

From the total respondents, 27.7% reported that they had access to extension service and the rest 72.3% no access to extension service particularly in the honey sector, and 20.75% respondents reported that they had access to credit while 79.25% respondents had no access to credit service in the study area (Table 5).

Table 5.

Access to services (Dummy variables).

S/N	Characters		Zones (Frequency/Percent)				Average (%)
			Bench-Sheko	Kaffa	West-Omo	Shaka
3	Income source:	Agriculture	115 (93.5)	117 (95.1)	120 (98.3)	118 (95.9)	95.7
		Trade	5 (4.1)	2 (1.6)	2 (1.7)	3 (2.4)	2.45
		Gov't worker	3 (2.4)	4 (3.3)	0 (0)	3 (2.5)	2.03
4	Extension service access	Yes	40 (32.6)	38 (30.8)	24 (19.7)	34 (27.7)	27.7
		No	83 (67.4)	85 (69.1)	98 (80.3)	89 (72.3)	72.3
5	Credit access	Yes	25 (20.4)	34 (27.7)	13 (9.7)	31 (25.2)	20.75
		No	98 (79.6)	89 (72.3)	109 (89.3)	92 (74.8)	79.25
6	Cooperative membership	Yes	22 (17.9)	58 (47.1)	2 (1.7)	47 (38.2)	69.85
		No	101 (82.1)	65 (52.9)	120 (98.3)	76 (67.8)	30.15

Source: Authors survey, 2020

Regarding cooperative membership, 69.85% of respondents said that they were a member of a cooperative whereas 30.15% were not a member of any cooperatives (Table 5).

3.2. Types of hives owned

The three hive types which are used by honey producers in the study area are traditional, transitional/intermediate, and modern/movable frame hives. Traditional hives are hives that are constructed from locally available materials by implementing indigenous knowledge and the experience of honey production. Transitional/intermediate hive types are also made from a wooden box and locally available material. Modern hives are hives that indicate the use of frame-hives and accessories. It contains bottom boards, boxes encompassing frames for brood and honey, and an inner cover and top cap providing shelter from the weather.

Many respondents own both traditional and transitional beehives. In the study area, 65 and 7.5% of respondents owned traditional and transitional beehives respectively whereas the rest of the respondents owned both traditional and transitional beehives (Table 6). From the respondents, 58.3% of respondents revealed that honey bees stay for a long time in modern beehives than traditional ones (Table 6). This might be due to the presence of a queen excluder and easiness for internal inspection of modern hives compared to traditional ones. The only mechanism in the traditional hive to harvest honey is by the destruction of combs and this, in turn, affects the stability of bees in the hives. Similarly, the occurrence of disease was highest in traditional hives. It implied that the use of modern and transitional hives is by far better than traditional hives.

Table 6.

Types of hive owned, honeybee stay period, and disease occurrence in hives.

S/N	Characters	Zones (Frequency/Percent)
		Bench-Sheko	Kaffa	West-Omo	Shaka	Average %
1	Hive types owned by farmers
	Traditional	80 (65.0)	67 (54.4)	98 (80.3)	76 (61.8)	65
	Transitional	5 (4.1)	18 (14.6)	3 (2.4)	11 (8.9)	7.5
	Modern	0 (0.0)	0 (0.0)	0 (0.0)	0.0 (0.0)	0
	Traditional & modern	12 (9.7)	14 (11.3)	10 (8.2)	14 (11.4)	10.1
	Traditional & transitional	18 (14.6)	15 (12.6)	8 (6.5)	9 (7.3)	10.2
	All hive types	8 (6.5)	8 (6.5)	3 (2.5)	16 (13.0)	7.2
2 Long stay
	Traditional	26 (21.2)	33 (26.8)	30 (24.6)	42 (34.1)	26.6
	Transitional	19 (15.4)	21 (17.1)	21 (17.2)	13 (10.6)	15.1
	Modern	78 (63.4)	69 (56.1)	71 (58.1)	68 (55.3)	58.3
3 Disease occurrence
	Traditional	61 (49.6)	43 (34.9)	67 (54.5)	68 (55.3)	48.6
	Transitional	14 (11.4)	41 (33.3)	18 (14.8)	17 (13.8)	18.4
	Modern	48 (39.0)	39 (31.7)	37 (30.3)	38 (30.9)	33

Source: authors survey, 2020

3.3. Price of hives

The cost of hives determines which types of hives could be used by the farmers in the study area. The average cost of a modern hive was 1581.20 ETB3 which is higher than the average cost of the transitional beehive (1140.00 ETB) (Table 7). The average cost of a traditional hive was 93.77 ETB and is obtained from the local market. According to the focus group discussion, traditional hives were prepared from locally available materials in a social group called ‘Debo’ by some farmers.

Table 7.

Price of hives in the study area.

No	Types of hive	No of Hives	Minimum (ETB)	Maximum (ETB)	Mean (ETB)	Std. Dev.
1	Modern hive	125	1200.00	3400.00	1581.200	219.11332
2	Transitional hive	110	250.00	1500.00	1140.00	209.49918
3	Traditional hive	380	30.00	450.00	93.77	105.10110

Source: Own survey data, 2020

3.4. Productivity of honey

The productivity of traditional, transitional, and modern beehives was 9, 16, and 22 kg per hive (Table 8). The average selling price of a kilogram of honey was 92.2 Birr.

Table 8.

Productivity and production level of Beehives.

No	Types of hive	Productivity	Total Production	Average selling price birr/Kg
1	Modern hive	22	2750	92.2
2	Transitional hive	16	1760
3	Traditional hive	9	3420
	Total		7930

Source: Own survey data, 2020

3.5. Challenges of honey production

Factors that affect the production and productivity of honey were discussed as follows:-

Lack of modern technology: - Of the total respondents, 92.5% of respondents reported that lack of modern technology affects honey productivity. The traditional method of honey production is dominant due to the lack of modern technology. It needs to be replaced by improved and modern scientific methods for better management.

Lack of Apiculture specialists: - In the whole study area, there was no specialized apiculture expert, therefore, the sector face the technical challenge to improve production and productivity. About 74.9% of respondents reported that poor extension service affects beekeeping practice in the study area (Table 9).

Table 9.

Factors affecting honey production in the study area.

S/N	Production Challenges	Zones (Frequency/Percent)				Average freq (%)
		Bench-Sheko	Kaffa	West-Omo	Shaka
1	Lack of modern technology	85 (89.4)	126 (95.1)	36 (94.2)	117 (91.1)	91 (92.5)
2	Absconding	62 (65.8)	73 (55.3)	27 (70.5)	1061 (86.2)	67 (69.5)
3	Lack of beekeeping knowledge	87 (91.8)	119 (88.6)	28 (72.9)	95 (80.5)	82 (83.5)
4	Poor pre and post harvesting management	68 (72.3)	125 (92.6)	25 (64.8)	82 (69.9)	75 (74.9)
5	Poor extension service	78 (82.9)	65 (49.6)	19 (47.5)	59 (49.6)	55 (57.4)
6	Lack of input supply (beekeeping equipment)	58 (61.8)	51 (39.0)	15 (37.7)	50 (42.3)	43 (45.2)
7	High cost of modern bee hives	63 (66.6)	92 (69.9)	18 (47.2)	104 (88.6)	69 (68.1)
8	Lack of credit service	30 (30.9)	48 (36.5)	10 (25.4)	23 (20.3)	28 (28.3)
9	Deforestation	21 (22.7)	38 (29.2)	8 (19.6)	36 (30.8)	26 (25.6)
10	Shortage bee forage	55 (57.7)	98 (73.9)	17 (45.1)	43 (36.6)	53 (53.3)
11	Pests and predators	53 (56.1)	82 (61.7)	17 (43.4)	30 (26.0)	46 (46.8)
12	Pesticide and herbicide threat	50 (52.8)	85 (64.2)	15 (40.2)	39 (33.3)	47 (47.6)
13	Death of honey bee in colony	34 (36.5)	62 (47.1)	16 (41.8)	31 (26.8)	35 (38.1)

Source: Own survey data, 2020

Absconding: - Absconding is a term used when all bees leave the hive. It can occur due to shortage of feed, disturbance, bud management, unfavorable weather condition, colony collapse disorder (CCD), and related factors. Absconding results in total loss of colony and by hive productivity reduced. From interviewed respondents, 69.5% of respondents faced absconding challenges (Table 9).

Absence of Input Suppliers: - Of the total respondents, 45.2% responded that lack input supply challenges beekeeping activities (Table 9). Lack of beekeeping equipment like bee suits can directly affect the management and inspection of the hives. In the study area, this equipment was not available for honey producers even to buy from the market. DAs (Development Agents) deeply regrettably mentioned that there was a lack of overall wear even for themselves, lack of queen excluder and the limited number of NGOs (Non-Governmental Organizations) that support cooperatives farmers was a critical challenge for beekeeping. NGOs simply distribute hives to farmers without support with technical training; therefore the use of modern hives is not as effective both at cooperatives and individual levels as the expected potential.

Lack of credit access: - Of the respondent, 28.3% responded indicated that lack of credit service was one of a challenge for beekeeping practice (Table 9). Discussion with government bodies and experts supporting in this area from kebele to Zonal level of whole study area raised that lack of credit service and lack of equipment and material supporting organization contributed for less production of honey as compared to the potential of the study area.

Poor pre and Post-harvest management: - Poor pre and post-harvesting management of products affect the quality and amount of honey produced from the hive. The volume of production is very small and needs routine collection from fragmented small-scale production. In the current study, 57.4% of respondents faced challenges in pre and post-harvest management (Table 9).

Deforestation: - Southwestern Ethiopia is naturally endowed with different bee forage species that make beekeeping pleasant activity in the study area. Of the total respondents, 53.5% and 25.6% mentioned deforestation and the shortage of bee forage influencing beekeeping (Table 9). According to FGD (Focus Group Discussions) discussion with farmers and stakholders, there was no attempt done for plantation of bee forage by the government or any other body.

Pesticide and herbicide threats: - From the total respondents, 47.6% of respondents replied the effect of pesticide and herbicide on honey was crucial (Table 9). This might be due to a lack of knowledge in content, dose, and application time of chemicals in the study area.

Death of Honey bee:- Death of honey bees occur in the study area, from respondents 38.1% revealed the death of honey bee colony reduces hive productivity (Table 9). The death of the colony might be due to lack of inspection for disease, might be an inappropriate application of chemical in the surrounding farm, might be pest and predator or some poisonous plants and majorly due to ants in all study area.

3.6. Honey marketing challenges

Marketing here is defined as the movement of beehive products from the production area to consumers. Its major concern is the pricing and supply of honey bee products.

Poor market linkage: - According to information obtained from FGD, there was no market linkage of producers with traders; therefore, farmers sell their products to local traders and consumers at low prices. From the total interviewed farmers, 84.1% of respondents indicated that poor linkage of the market is the major factor that affects the benefit of honey production.

Lack of market information and access: - Averagely, 66.2% of respondents in the study area confirmed that the lack of market information is a problem while 84.2% of respondents confirmed that the lack of market access is one of the major challenges for selling the product (Table 9). This result is in agreement with the finding of Tadesse (2001) and Gebrehiwot (2015).

Poor Infrastructure:- Infrastructure (road) problems were another factor, farmers carry their product and walk a long distance to market access (Table 10). From the total respondents, 61.5% of respondents revealed that poor infrastructure is a problem in honey marketing.

Table 10.

Marketing challenges at farmers, cooperatives, traders, and union level.

S/N	Marketing Constraints	Zones (Frequency/Percent)				Average Freq. (%)
		Bench-Sheko	Kaffa	West-Omo	Sheka
	Farmers level
1	Poor market linkage	75 (79.7)	109 (82.1)	31 (81.1)	105 (93.5)	81 (84.1)
2	Lack of market access	80 (85.4)	102 (77.2)	35 (91.8)	96 (82.1)	78 (84.2)
3	Lack of market information	66 (69.9)	88 (66.6)	25 (64.7)	74 (63.4)	63 (66.2)
4	Poor infrastructure	60 (63.4)	72 (54.4)	28 (72.1)	66 (56.1)	57 (61.5)
5	Low price of product	57 (60.9)	115 (62.6)	25 (64.7)	64 (54.4)	77 (60.7)
6	Long distance to market	32 (91.1)	115 (86.9)	34 (88.5)	102 (86.9)	70 (88.4)
7	Weak bargaining power of farmers	32 (34.1)	51 (39.0)	14 (35.2)	49 (41.5)	36 (37.5)
8	Lack of packing and storage facility	43 (46.3)	96 (72.4)	19 (47.5)	75 (64.2)	58 (57.6)
9	Lack of cooperatives and institutional linkage	52 (55.3)	85 (64.2)	22 (56.5)	82 (69.9)	60 (61.5)
	Traders, cooperative and Union level
10	Presence of illegal traders	39 (41.4)	67 (54.5)	20 (52.4)	77 (65.8)	50 (53.5)
11	Adulteration	42 (34.1)	38 (30.9)	10 (25.4)	43 (36.5)	33 (31.7)
12	Absence of branding	50 (52.8)	83 (67.5)	18 (46.7)	87 (73.9)	59 (60.3)
13	Storage period (month)	1.8	3.85	1.2	2.5

Source: Own survey data, 2020

Presence of illegal traders: - Presence of illegal trade directly and directly affects legal traders’ competence in the market. According to the discussion held with traders, illegal traders buy honey at a high price and adulterate with other substances then sell at a lower price. This affects the quality as well as the price of the honey market; therefore, consumers and other traders raise serious problems of adulteration of the produce. As traders' information, farmers store honey for a better price but due to extraction and storage problems, honey quality deteriorates, it also affects the market price of honey but adulteration with external substances is not a factor of farmers rather it is of illegal traders. Regarding illegal traders, 53.5% of respondents responded that illegal trade affects critically the honey market and 60.3% of respondents responded the adulteration is among the other problems which affect the honey market (Table 10).

Absence of branding and legal policy: - The discussion with unions and cooperatives showed that there was an absence of branding and legal policy for illegal trade. For example, in Sheka zone there was honey production by Haile Gebresilas and exported by the name of Haile honey. Therefore, the source of honey can't be recognized in the world market. There was no rule and regulation of the marketing of honey in the domestic market, simply one can take honey from one place to another area without any restriction. Hence, the region producing honey does not benefit from honey marketing. There was no licensing system for honey traders in the domestic market, therefore, any person can simply open the shop and start honey trading, and no criteria are set to be a honey trader (Table 10). The other challenge raised by the Bonga forest honey union was that since the export price of honey is lower than the local market price, unions are forced to sell their product to local and illegal traders and, facing a great challenge to get sufficient and reasonable profit which helps them to compete in the international market.

3.7. Econometric result of factors influencing honey production in the study area

Variable cost: - this variable is continuous and measured by the amount of money spent on variable inputs. The result implied that there is a positive relationship between variable cost and quantity of honey produced. This is because that the cost incurred for honey production was small. The increase in one birr for honey production will increase the quantity of honey produced by 9% (Table 11).

Table 11.

Factors influencing natural Honey production level.

Yield	Coef.	Std. Err.	T	P > t	R2	Root MSE	Prob > F
Age	0.33	0.16	2.08	0.038	0.638	0.60051	0.0000
Education level	0.04	0.01	4.3	0.000
Marital status	-0.34	0.06	-5.26	0.000
Family size	-0.02	0.02	-1.29	0.199
Workage	-0.01	0.02	-0.4	0.692
Sex	-0.08	0.3	-0.27	0.789
variable cost	0.09	0.02	4.94	0.000
Hive number	0.04	0.01	18.32	0.000
Cooperative membership	0.04	0.07	0.6	0.55
Income	-0.02	0.04	-0.59	0.553
Distance	-0.04	0.05	-0.85	0.397
Experience	-0.09	0.05	-1.96	0.051
Training	-0.08	0.06	-1.26	0.21
Lagged price	-0.12	0.12	-0.97	0.333
_cons	2.87	0.94	3.06	0.002

Source: Own survey data, 2020

Education level: - it is a continuous variable and accounts for the years of schooling of honey producers. As it was hypothesized, this variable has a positive relationship with the yield of honey produced. This indicates that the more educated producers produce more honey than those of uneducated ones. As one year increase in education level or schooling of respondents, the output or yield will increase by 4 kg.

Age of the respondent: - It is a continuous variable and positively related to honey output as hypothesized. The result indicated that the increase by 1% of age of the respondent will increase honey production by 33%. This indicates that most honey producers are older peoples which possess skills and better management practices.

Marital status: - it is a dummy variable and has a negative relationship with honey production. It indicates that single honey producers are more technically efficient than honey producers who are married. The coefficient indicates that if the number of married households increases by 1 unit, honey production will be reduced by 34 kg.

Experience: - the coefficient of experience indicates that a 1% increase will decrease honey output by 9%. The reason for this is that when producers are experienced agricultural production increase, they turn and focused on the production of cash crops.

Hive number owned: - it is a continuous variable and was measured by the total number of hives owned. As it was hypothesized, the variable has a positive relationship with the quantity of honey harvested and is significant at a 1% significance level. The increase in one number of hives will increase the quantity harvested by 4 kg (Table 11).

3.8. Summary of constraints along with honey value chain actors

The constraints that affected honey production were summarized in Table 12.

Table 12.

Summary of constraints along with value chain actors.

Value Chain Actors and their Challenges
Input supply	Production	Cooperatives	Union	Marketing	Processing
Lack of inputs and input supplier Input price is too high	✓Lack of capacity building and pieces of training for producers & DAs ✓Lack of professionals specialized in Beekeeping ✓Lack of documents ✓Extension service delivery problem ✓Absconding, Ants harvesting problem ✓Pre and post-harvesting problem ✓Lack of credit ✓Infrastructure Problem ✓Reporting problem ✓Quality Problem ✓Packaging material problem ✓Technology adoption problem	✓lack of credit ✓lack of transparency ✓Lack of cooperatives example west Omo zones ✓Lack of processing equipment and machines	✓Absence of rules and regulations for the honey market ✓Export price is lower than local Price ✓The high moisture content of high ✓No brand	✓Price fluctuation ✓Lack of strong value chain ✓Lack of market access ✓Illegal market ✓Adulteration habit ✓No criteria for licensing traders	Lack of processing Industries

4. Discussion

The socio-demographic conditions of beekeepers were seen to play an important role in the adoption of technologies in beekeeping activity (Abuje et al. 2017; Kiros and Tsegay, 2017). Honey production is mainly dominated by males. The result agreed with that of Alemu (2010), Kebede and Adgaba (2011), and Abebe (2011), who reported a low level of women's participation in honey production. The research by Hartmann (2004) confirmed that honey production is mainly men's job in Ethiopia. This is due to the characteristics of beekeeping activities that involve climbing the tallest trees to hung beehives and later transferring them into the backyard. Honey Production is dominated by males and women were not inspired to participate in these activities (Dinka and Kumsa, 2016).

More educated farmers are believed to adopt typically new technologies more than uneducated farmers (Ekwe and Nwachukwu, 2006). Honey producer's education level may have affected their probability of shifting from traditional to the modern honey production system (Dinka and Kumsa, 2016). The descriptive result indicated that the producers were existed in active and productive age group. This result is in line with the finding of Kinati and Tolemariam (2012), Dinka and Kumsa (2016), and Kiros and Tsegay (2017) however it disagrees with Duguma and Janssens (2016) finding who reported that 51.26 ± 10.99 years of mean age recorded in Jimma town.

Poor access to extension services made problems for honey producers that adopt modern technologies (Dinka and Kumsa, 2016). Lack of extension service leads producers to follow traditional management practices instead of modern technologies (Gichora, 2003). Extension service avails information regarding improved technology usage that will increases honey production level (Nigussie, 2011; Samuel 2014; Tarekegn et al., 2017a, Tarekegn et al., 2017b). As the farmer becomes more experienced in agricultural production it will have a higher knowledge to identify challenges and coping mechanisms, therefore, they turn and focused on the production of cash crops rather than beekeeping activity. These results agree with the findings of Gebrehiwot (2015).

A majority of the respondents (69.85 percent) seem to be members of cooperatives. This result is in agreement with the finding of Arage (2018). Gebrehiwot (2015) reported that there was poor participation of honey producers in cooperatives. In Ethiopia, 6,958.004 hives existed. From this, 96.02% were traditional hives while the rest 1.35 ad 2.63% were intermediate and modern beehives, respectively (CSA, 2020). Traditional beehives dominate honey production practice. These hive types are dominant hive types used by beekeepers in developing countries especially in Africa (Schmolke 2009) as cited by (Kasangaki et al., 2014). CSA (2020) indicated that 96% of honey production in the country was from traditional hives, whereas the rest 1.4% and 2.6% were from intermediate and modern hives, respectively. The major advantage of traditional beehives is the local availability of materials for construction of the hives, which needs less capital and fewer skills for making the hives, while its disadvantage is removal and replacement of combs and examination and manipulation of hives are also impossible. And also bee brood will be destroyed and honey quality is low because of the mixing of pollen, brood, and other materials of the combs (Kasangaki et al., 2014). Tulu et al. (2020) revealed that high yield, simplicity of examination, ease of harvesting of honey products, and high-quality honey are the main advantages of improved beekeeping technology while high cost, the need for high skill, the need for accessories, and unavailability as disadvantages of modern beehives. Similar results were reported by Yehuala et al. (2013) and Affognon et al. (2015).

The cost of a beehive is higher than the finding of Sahle et al. (2018), who found the cost of a modern hive between 900-1000 ETB in the Tigray Region. These types of hives are further costly than traditional hives. Its advantage is possiblity to harvest honey without damaging the bee colony, durable, high yielding, and simple to check and perform any management activities (Kasangaki et al., 2014). Therefore, the transitional hive is relatively affordable for farmers than that of the modern hive. Transitional hive types were designed to allow easy manipulation (Kasangaki et al., 2014). The productivity of traditional, transitional, and modern beehives was 9, 16, and 22 kg per hive, respectively. This study agrees with the finding of Kiros and Tsegay (2017) which showed that 16.2 ± 4.12 kg of honey from transitional hives were harvested. The present result was greater than the result implied by Getu and Birhan (2015) and Kinati and Tolemariam (2012) who found that the mean yield per year/colony was 7.20 ± 0.23, 14.70 ± 0.62, and 23.38 ± 0.73 kg for traditional, transitional, and modern hives respectively. However, the mean output found from transitional hives in this study area is higher than the result indicated by Yemane and Taye (2013) which is 14.07 kg per hive/year.

About 92.5% of respondents mentioned lack of technology is one of the challenges for the sector. This result is in agreement with the finding of Tadesse (2001); Kerealem et al. (2009); Yirga and Teferi (2010) and Sahle et al. (2018a,b). The use of old technology outcomes in truncated honey supply and reduced quality of yield (Beyene and David, 2010). According to Gebrehiwot (2015), the use of modern style beehives is still at a very low level in Ethiopia.

In the whole study area, there was no specialized apiculture expert, therefore, the sector faces the technical challenge to increase production and productivity. According to Johannes (2005), the major honey production problems are the lack of knowledge and shortage of trained manpower. Due to poor extension service, farmers lack knowledge on how to obtain better products (quality and quantity). In all study sites, poor extension service and lack of experts specialized in apiculture were raised as a great challenge for beekeeping activities as explained in the focus group discussion and key informant interview. This result is in line with the finding of Kerealem et al. (2009); Gebrehiwot (2015) and Sahle et al. (2018a,b).

The absence of beekeeping equipment (input supply) is another challenge that hinders the productivity of the beekeeping business. According to the focus group discussions and key informant interview, not only the lack of input supply but also the cost of inputs especially of the modern hive is a great challenge in the study areas. This result is in line with the finding of Sahle et al., 2018a, Sahle et al., 2018b. Development Agents (DAs) in the discussion also raised a similar challenge to farmers regarding the lack of beekeeping equipment. This finding is in line with that of Terefe (2018) at Chiro district of East Oromiya Region, who reported that NGOs distribute hives with no demonstration on the management of the hive. Shortage of credit access to buy inputs including modern hive for farmers, cooperatives and unions involved in honey production and marketing affect the production level as well as the quality of honey as indicated by focus group discussion. Kerealem et al. (2009) supported this finding.

The limitations of apiculture know-how especially the method of harvest and lack of proper apiary tools are causing the honey to be with a high moisture content which may be conducive for some pathogens. Similarly, FGD (Focus Group Discussion) report indicated that due to improper handling, honey lacks quality in its stay. Modern honey production that includes the use of modern style beehives is still in Ethiopia at a very low level. Of 4,993,815 beehives available in Ethiopia during 2011, only 139,682 were modern beehives (CSA , 2012). The majority of respondents in the study area own traditional hives and harvesting from traditional hives does not allow total inspection for ripening honey. Therefore, the honey may contain some broods and also there was no extractor for farmers, which affects the quality of honey. According to Beyene and David (2010), the extensive use of traditional beehives results in relatively low honey supply and poor quality of honey harvested when compared to the potential honey yields and quality gains associated with modern beehives. Modern beehive produces more than 20kg of higher quality honey. This situation results in growing domestic prices of table honey and poor perspectives for reaching export markets. The benefit from the sub-sector to the nation as well as to the farmers, processors, and exporters is not satisfactory.

Deforestation of the area for different agricultural activities results in the shortage of bee forage in the study areas. Deforesting the forests as means of survival seriously affects honey production (Gebrehiwot, 2015). In line with agricultural activities, the use of pesticide and herbicide also affects beekeeping activities. This finding is in agreement with the finding of Johannes (2005); Kerealem et al. (2009); Arage (2018) and Sahle et al. (2018).

The death of bee colonies highly influences honey production. In agreement with the present study, Shenkute et al. (2012) reported that flowers of some plants were poisons to honey bees, and flowers of Croton macrostachyus cause the death of honey bees in Kaffa and Sheka zones. Arage (2018) reported that the death of a colony was a key constraint faced by beekeepers at the farm level.

The major factors affecting the income of farmers from honey production were poor market linkage, lack of access to market, lack of market information, poor infrastructure, low price of the product, long-distance to the market, weak bargaining power of farmers, and lack of cooperative and institutional linkage. Tadesse (2001) confirmed the absence of organized market channels as marketing constraints. This finding was in agreement with that of Beyene and Verschuur (2014) and Sahle et al., 2018a, Sahle et al., 2018b. This result agrees with that of Yeserah et al. (2019) who found that licensing procedures, access to credit, market transparency, and experience in honey trading affect honey marketing.

The Econometric result revealed that variable cost, education level, age of the respondent, marital status, experience, and hive number owned. Famuyide et al. (2014) revealed that the higher the educational status, the higher the productivity and better management. This result is in line with the finding of Najafi (2003); Gebrehiwot (2015); Dinka and Kumsa (2016); Abuje et al. (2017). Most honey producers are older peoples which possess experience and better management practices. This study is in line with the finding of Karadas and Kadirhanogullari (2017). Single producers are more efficient than married ones. This result is in line with the finding of Olarinde et al. (2008) implied that married honey producers are more technically inefficient than single honey producers. Gebrehiwot (2015) reported that high experience has no value in increasing honey production. The larger the number of hives owned, the higher the quantity of honey harvested (Dinka and Kumsa 2016). A producer with more beehives can harvest more volume of honey (Tarekegn et al., 2018). The finding is in line with the finding of Nigussie (2011); Kerealem et al. (2009); Abuje et al. (2017) and Kizilaslan and Kizilaslan (2007).

5. Conclusion

This study was designed with the general objective of identification of challenges that affect the production, productivity, and marketing of honey in the Kaffa, Sheka, Bench Sheko, and West Omo zones of SNNPR. A total of 23 woredas and 63 kebeles were involved in the study. Based on the survey, major constraints that affect honey production include lack of modern technology, absconding, disease, and pests as well as predators, lack of credit access, poor extension service, and the like affect honey production in the study area. Lack of technology, high cost of technologies, and lack of practical-based training for farmers as well as DAs made the farmers stick with traditional hives using their indigenous knowledge. Even though there is potential in the study area, due to these challenges the benefits farmers get out of beekeeping are not much as have to be.

Similar to the production challenges, there was poor market linkage and market access, low bargaining power of farmers, long-distance to market, shortage of packing and storage materials, and the like affect marketing in the study area. The presence of illegal traders, lack of brand, the problem of licensing, and lack of trade policy on honey marketing challenges the legal traders and exports and unions in the study area. In general, the beekeeping practices in the study area were facing complex challenges in both production and marketing. Therefore, remedial actions should be taken both by the government and NGOs working around honey production and productivity. To improve and expand beekeeping activities, it is necessary to solve the above-mentioned problems through the involvement of all stakeholders including the farming community, government, non-government organizations, and the private sector. In this regard, it is recommended that increasing accessibility to modern beehives, enhancing the skill and knowledge of the producers through training on the management of beekeeping activities, organizing honey marketing cooperatives, promoting the involvement of the private sector in the provision of apicultural tools, establishing linkages among apiculture farmers, research institutions and the private sector.

Declarations

Author contribution statement

Benyam Tadesse; Yaregal Tilahun; Wondimu Woyamo; Mekuanint Bayu; Zelalem Adimasu: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This work was supported by Southern Nations, Nationalities, and People's regional government special support office and Mizan-Tepi University.

Data availability statement

Data will be made available on request.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.