Economic analysis of beekeeping enterprises producing chestnut honey Black Sea region in Türkiye

Abstract

In the present study, we economically analyzed the enterprises producing chestnut honey in the TR82 region provinces of the Black Sea region of Turkey (Kastamonu, Çankırı, and Sinop provinces). Face-to-face interviews were conducted to collect data from 74 small, medium, and largescale enterprises, and data were analyzed by simple random sampling. Furthermore, a backward regression model was developed to assess the input and output relationships in the enterprises. The average honey yield per hive was 4.06 kg. The statistically significant difference was found between scales and provinces in unit cost. The unit cost average of small-scale enterprises was statistically different from that of large-scale enterprises. A statistical difference was found between provinces in sales price and profit per hive (p < 0.05). The adjusted R2 value was found to be 0.865 for the regression model created with the variables affecting the unit cost. The lowest honey production cost (10.52 US$) was found in small-scale enterprises and the Çankırı province (9.17 US$/kg), whereas the highest production cost (12.34 US$/kg) was found in large-scale enterprises and the Sinop province (12.38 US$/kg). The net profit per hive was calculated to be US$52.83 for small-scale enterprises and US$46.47 for medium and large scales. Thus, chestnut honey can be marketed as a geographically indicated product at a high price. However, the increased number of enterprises producing chestnut honey, along with a lack of professionalism and insufficient technical knowledge and interest, has raised production costs. These findings suggest that producers should be encouraged to reduce their unit costs to increase their profit.

Keywords:

• beekeeping
• enterprise
• Türkiye
• chestnut honey
• Black Sea region

1. Introduction

Humans have been practicing beekeeping right from the beginning and evolution of mankind (Genç, 2003; Özkök & Sorkun, 2001; Tutkun, 2000). Beekeeping is a husbandry sub-sector that provides a foreign exchange source to a country and generates qualified products such as honey, wax, pollen, propolis, royal jelly, and bee venom that can be exported to earn huge profits (Kekeçoğlu & Rasgele, 2013).

Pollination by bees is widely practiced around the world. This pollination activity is becoming more and more important today by performing an important ecosystem service on a global scale. Intensive land use, climate change, the increase of invasive species and the spread of diseases are recognized as the main causes of the decline of bee presence (Matsuzawa & Kohsaka, 2021; Uchiyama et al., 2017).

The profitability of the beekeeping enterprise depends on seasonal colony management and flower supply. Beekeeping is an activity that makes two important contributions to sustainable rural area development globally. First, in terms of pollination and second, in terms of income, as a main or secondary source of income, it offers opportunities especially in disadvantaged rural areas. In such areas, bee products have the potential to rapidly generate and diversify income in enterprises with limited land and/or limited capital (Kumsa et al., 2020; Novelli et al., 2021).

Because of its geographical location, climate, and flora diversity, beekeeping activities have been regularly practiced in Türkiye. Due to these advantages, Türkiye has emerged as one of the leading countries in the beekeeping sector worldwide. Türkiye ranks among the top three countries worldwide with respect to the total beehive presence and total honey production (M. B. Çevrimli & Sakarya, 2018, 2019a; Güngör & Ayhan, 2016). The geographical location, climatic conditions, and vegetation diversity of forests in Türkiye are highly suitable for beekeeping activities such that different tree species in forests can produce honey of different nectars (Güngör & Ayhan, 2016). Effective beekeeping activities in forested lands maintain biological diversity, as well as increase the economic levels of the people living in rural areas and minimize the development differences between the regions (Altunel & Olmez, 2019; Güngör & Ayhan, 2016). In Türkiye, the Black Sea region is one of the primary locations with chestnut forests, where beekeeping activities are performed to produce chestnut honey. Chestnut forests contribute to the biodiversity in Türkiye, and they are spread from the Aegean region to the east of the Black Sea region. They are present in very large groups from the western Black Sea to the eastern Black Sea. The scheme for the chestnut forest expansion in Türkiye is presented in Figure 1 (OGM, 2021).

Figure 1. Distribution map of chestnut trees in Türkiye (OGM, 2021).

The literature reports numerous studies on beekeeping activities conducted in Türkiye, focusing on economic, econometric, marketing, production problems, and risk factors (M. B. Çevrimli & Sakarya, 2019b; M. B. Çevrimli et al., 2020; Sarıözkan et al., 2009; Varalan & B, 2023).

Many different types of honey are produced in other regions of Türkiye from various flower and plant nectars. There are limited economic studies on the production of chestnut honey (Diktas-Bulut et al., 2022). Unlike the studies in the literature, the unit production cost of chestnut honey was calculated in this study. In addition, the study was planned in the western black sea region, considering the chestnut forests and chestnut honey production potential in the research region. In this context, the objectives of the study are; calculating the production cost of 1 kg chestnut honey and, determining the sales price, calculating the profitability level per hive in enterprises according to scales and provinces. On the other hand, it is statistically evaluated whether there is a difference between production costs and sales prices, scales, or provinces.

In this study, we conducted economic analyses of the enterprises largely producing chestnut honey in Kastamonu, Cankiri, and Sinop provinces, known as the TR82 region, where chestnut honey production is conducted in the forested land in the Black Sea region.

2. Materials and methods

A data collection form was used to collect the primary data from producers engaged in beekeeping activities in Kastamonu, Cankiri, and Sinop provinces. The form consisted of questions regarding the type of enterprises (fixed or mobile), production pattern (honey, propolis, pollen, royal jelly, etc.), whether producers were operating in another field other than beekeeping, and enterprise cost elements.

To determine the sample size of the study, the “Stratified Random Sampling” Neyman method was used within the limits of a 10% margin of error and a 90% confidence interval. The sample was first calculated on a provincial basis and distributed to the strata.

n = [Ʃ(NhSh)²]/[N²D²+Ʃ(Nh(Sh))²],

where n = sample size, Nh = number of units in the stratum h (frequency), Sh = standard deviation of the stratum h, N = the total number of units, D = d/z, d = deviation from the average at a certain rate (such as 5–10%). Z refers to the degree of freedom in the t-distribution table (N-1) and t-value (such as 90–95–99%) of a certain confidence limit (Z value in the t-distribution table if the number of units is above 30) (Sümbüloğlu & Sümbüloğlu, 2005; Yamane, 1967).

The total sample size was calculated as minimum 65 enterprises (Table 1). The sample size distributions in Kastamonu, Cankiri, and Sinop provinces were 25, 16, and 24, respectively. While at least 65 enterprises had to be visited, this number was increased to 80 enterprises to evaluate the data statistically and to compare the scales accurately. The data of 80 beekeeping enterprises for 2020 were examined, and 74 enterprise data, where the data were reliable, were included in the analysis.

Table 1. Number of enterprises included in the sampling by provinces and strata

Province	Sample size	Scale	Sample size
Kastamonu	25	Small (30–74)	22
Cankiri	16	Medium (75–149)	16
Sinop	24	Large (150 and above)	27
Total			65

Cost elements that constituted the cost, secondary income, and income items were determined. The following formula was used to calculate the unit cost.

Cost of honey (TL/kg) = general total expenses (TL)—total secondary income (TL)/amount of honey produced (kg)

The data obtained in the study were transferred to Microsoft Excel 16.00. SPSS 24 (IBM, 2016.) program was used to analyze the data. Descriptive statistics consisting of mean ±standard deviation, median (maximum—minimum), percentage, and frequency values were used.

The normality of the data was evaluated using the “Shapiro—Wilk” test. The homogeneity of the variances was assessed using the Levene test. To evaluate the differences between the groups, the Kruskal—Wallis test and Bonferroni—Dunn test for multiple comparisons were used. Kruskal—Wallis analysis was performed to assess a statistically significant difference between unit production costs, unit honey sales prices, and profit amounts per hive in beekeeping enterprises by scale size and provinces. Regression analysis of independent variables given for the regression analysis of the unit cost function for 1 kg of honey was estimated using the Backward regression analysis procedure. In the multiple regression model, controls were made in terms of necessary assumptions.

3. Results and discussion

We found that 2.7% of the enterprises that provided data were engaged in inter-provincial mobile beekeeping, 33.8% were engaged in intra-provincial mobile beekeeping, and 63.5% were engaged in fixed beekeeping (Figure 2). Furthermore, 34 enterprises sold pollen along with honey, and 18 enterprises sold both pollen and propolis other than honey. However, 43.24% of the enterprises produced flower honey, and 56.76% produced chestnut honey. In addition, all enterprises, except for one, had an additional income source.

Figure 2. Chestnut honey apiary visited during the fieldwork.

Data in Table 2 demonstrated that with an increase in the enterprise scale, the honey yield decreases. Furthermore, the average honey yield per hive across all enterprises was 4.06 kg. An evaluation of the provinces in terms of yield revealed that the Çankiri province has the highest average.

Table 2. Honey yield per hive in beekeeping enterprises as per the enterprise scales and provinces

Scales/provinces	Small-scale honey yield (kg)	Medium-scale honey yield (kg)	Large-scale honey yield (kg)
Lowest yield	1.39	2.11	2.14
Highest yield	8.33	12.35	6.67
Average yield	4.56	4.25	3.50
Kastamonu	4.68	3.69	3.59
Cankiri	4.06	6.45	3.41
Sinop	5.02	3.40	3.44

Data in Table 3 revealed that bee feeding costs ranked first, with a share of 28.35% among the cost elements that constituted the cost throughout the scales. Subsequently, labor costs had a share of 20.05%, and auxiliary material costs had a share of 16.63%. At the level of scales, data revealed that labor costs ranked first in small-scale enterprises. In addition, unlike the general average, labor costs ranked second in medium and large enterprises. Average honey production costs, sales prices, and net profit per hive in beekeeping enterprises by scale size and provinces are listed in Table 4.

Table 3. Distributions of cost elements constituting the cost by scales in beekeeping enterprises

Cost elements	Small scale (%)	Medium scale (%)	Large scale (%)	General (%)
Bee feeding	23.38	25.61	30.30	28.35
Auxiliary Material	14.33	19.05	16.23	16.63
Labor	35.24	19.46	17.25	20.05
Energy	4.08	4.56	3.10	3.56
Transportation-accommodation	1.57	5.63	6.27	5.52
Maintenance repair	5.03	4.69	5.20	5.06
Veterinary medicine	3.57	4.34	5.08	4.72
Marketing	3.89	4.06	3.47	3.65
General administrative expenses	1.58	0.97	0.89	0.99
Other expenses	7.33	11.64	12.22	11.46
Total	100.00	100.00	100.00	100.00

Table 4. Honey production costs, sales prices, and net profit per hive by scales and provinces in beekeeping enterprises

Scales/provinces	Enterprise number (n)	Cost of 1 kg centrifugal honey (US$)	Sale price of 1 kg centrifugal honey (US$)	Net profit per hive (US$)
Small scale	24	10.52	2.51	52.83
Medium scale	21	10.67	22.49	46.47
Large scale	29	12.34	22.49	46.47
Kastamonu	28	11.64	24.32	52.39
Cankiri	19	9.17	14.01	24.40
Sinop	27	12.38	23.98	49.43
General	74	11.27	21.55	44.12

Data in Table 4 demonstrated small-scale enterprises as the most advantageous group in terms of net profit per hive. In addition, the highest sale price among the provinces belonged to Kastamonu. The data obtained in beekeeping enterprises were evaluated by variance analysis by unit cost, sale price, and net profit per hive to understand whether a difference existed between the groups at the scale and province levels.

Data presented in Table 5 showed that the unit cost average of small-scale enterprises was statistically different from that of large-scale enterprises. The average unit cost of beekeeping enterprises in the Cankiri province was statistically different from that of enterprises in both the Kastamonu and Sinop provinces. The average honey sale price of beekeeping enterprises in the Cankiri province was statistically different from that of enterprises in both the Kastamonu and Sinop provinces.

Table 5. Comparison of intergroup unit cost, sale price, and profit per hive

	Groups	Test statistics	Std. error	p-value
	Small–Medium	−5.214	6.426	1.000
Unit cost	Small–Large*	−14.937	5.953	.036
	Medium–Large	−9.722	6.162	.344
	Cankiri–Kastamonu*	19.041	6.392	.009
Unit cost	Cankiri–Sinop*	24.610	6.440	.000
	Kastamonu–Sinop	−5.569	5.801	1.000
Sale price	Interscale	1.895		.388
	Cankiri–Kastamonu*	36.789	6.351	.000
Sale price	Cankiri–Sinop*	36.787	6.398	.000
	Kastamonu–Sinop	0.002	5.763	1.000
Profit per hive	Interscale	4.452		.108
	Cankiri–Kastamonu*	20.729	6.392	.004
Profit per hive	Cankiri–Sinop*	15.936	6.440	.040
	Kastamonu–Sinop	4.794	5.801	1.000

*p < 0.05

Correlation coefficients were calculated to study the direction and power of the relationship between independent variables and dependent variables. Regression analysis was conducted to evaluate the relationship between the unit cost of beekeeping enterprises and the cost elements constituting the cost in beekeeping enterprises, as shown in Table 6.

Table 6. Pearson’s correlation coefficients of dependent and independent variables included in regression analysis

Variable	Unit cost	Bee Feeding	Aux. material	Labor	Veterinary medication	Generaladministration exp.	Other exp.	Energy	Marketing
Unit cost	1.000	0.230	0.219	0.499	0.580	.441	.631	0.548	−0.047
Bee feeding	0.230	1.000	0.686	−0.489	0.344	−.080	.407	−0.032	0.790
Aux. material	0.219	0.686	1.000	−0.474	0.105	−.250	.289	−0.035	0.594
Labor	0.499	−0.489	−0.474	1.000	0.092	.399	.044	0.365	−0.612
Veterinary medication	0.580	0.344	0.105	0.092	1.000	.314	.474	0.256	0.156
General administration exp.	0.441	−0.080	−0.250	0.399	0.314	1.000	.153	0.211	−0.048
Other exp.	0.631	0.407	0.289	0.044	0.474	.153	1.000	0.136	0.221
Energy	0.548	−0.032	−0.035	0.365	0.256	.211	.136	1.000	−0.190
Marketing	−0.047	0.790	0.594	−0.612	0.156	−.048	.221	−0.190	1.000

Correlation analysis revealed no correlation of 0.80 and above between the variables.

We preferred the backward elimination method for multiple regression analysis. The model consisted of six independent variables with the highest explanatory value remained. The R² value for the final model established with these independent variables was 0.876, the adjusted R² was 0.865, and the Durbin—Watson coefficient was 2.106. In the last model created, 86.5% of the changes in the dependent variable were explained by independent variables included in the model, and the remaining 13.5% of changes were explained by variables not included in the model by the error term.

Table 7 showed that auxiliary material, labor, veterinary medication, general administrative expenses, other expenses, and energy-independent variables were statistically significant (p < 0.001). The following equation was obtained as dependent variable Y = unit cost for the final model

Table 7. Estimated unit cost regression analysis results for 1 kg of honey

Parameters		N	β	Std. error	t	Sig.	Adj. R^2	F	Sig. F	Tolerance	VIF
Fixed		74	0.556	0.108		<0.001	0.865	78.633	<0.001
Aux. mat.	X2	74	0.156	0.022	5.169	<0.001				0.653	1.532
Labor	X3	74	0.394	0.045	7.155	<0.001				0.593	1.687
Veterinary medication	X4	74	0.120	0.028	8.718	<0.001				0.687	1.456
General administration exp.	X5	74	0.112	0.033	4.226	<0.001				0.747	1.338
Other exp.	X6	74	0.160	0.025	3.401	<0.001				0.697	1.434
Energy	X7	74	0.059	0.012	6.507	<0.001				0.802	1.247

$\mathrm{Y}=0.556+0.156{\mathrm{X}}_2+0.394{\mathrm{X}}_3+0.120{\mathrm{X}}_4+0.112{\mathrm{X}}_5+0.160{\mathrm{X}}_6+0.059{\mathrm{X}}_7$

In the present study, the honey yield was found to be lower than the average recorded in Türkiye and studies conducted at the level of beekeeping enterprises in Türkiye (Aydın et al., 2019; M. B. Çevrimli & Sakarya, 2018; Doğan et al., 2020; Güler & Demir, 2005). The limited production areas of chestnut honey and the chestnut honeybee pests have been reported to cause a decrease in fruit yield in chestnut trees, leading to an average yield loss of 50 to 70%. Extensive contamination results in a major loss of yield and the death of chestnut trees (Coşkuncu, 2010). This consequently results in a reduced yield of honey. Since one of the most critical factors affecting chestnut honey yield in the Western Black Sea Region is seasonal weather conditions, chestnut honey yield in Türkiye is lower than flower honey yield. Especially rainfall and fog during and after the flowering period of the chestnut tree reduce honey yield. 55% of beekeeping enterprises stated that climate is the most critical variable affecting honey yield (Ceyhan et al., 2016). Similarly, Marinković and Nedić (2010) reported differences in production per hive due to climate and site suitability.

A study conducted in Iran determined 3.89 kg of honey yield in traditional hives and 8.64 kg in modern hives (Vaziritabar & Esmaeilzade, 2016). A study conducted with enterprises largely producing chestnut honey in Duzce reported honey yield even below the Türkiye average of 5.6 kg (Kekeçoğlu & Rasgele, 2013). A study on chestnut honey conducted in the Eastern Black Sea region obtained an average honey yield of 9.94 kg per hive (Diktas-Bulut et al., 2022). Unlike our study, honey yield figures were found to be high in both studies. This difference is because itinerant beekeeping is practiced at a higher rate in other studies.

A comparison of study provinces revealed that the highest yield was observed in the Cankiri province. This could be attributed to the fact that while chestnut honey is largely produced in Kastamonu and Sinop provinces, except for chestnut honey, some flower honey is also produced in beekeeping enterprises in the Cankiri province. Similarly, in a study conducted with chestnut honey, the yield per hive/total honey production in the Ordu province was higher than in other provinces. This situation is because, in addition to chestnut honey, flower honey is also produced in the Ordu province (Diktas-Bulut et al., 2022).

Cost percentages are presented in Table 2. In a similar study on chestnut honey in the eastern black sea region, bee feeding costs were found to be in the first place at 15.84% (Diktas-Bulut et al., 2022). A study conducted in the Aegean region reported bee feeding costs in the first place, with a rate of 22.27% (M. Çevrimli, 2017). In a study conducted in Van province, feeding costs were in the first place, with a rate of 19.74% (Tosun, 2019). Similarly, a study conducted in the Canakkale province reported fuel-carrying costs in the first place and feeding costs in the second place (Aydın et al., 2019). A study conducted in the Adana province reported a share of bee feeding costs as 19.34% (Ören et al., 2010), whereas it was reported to be 18.9% in a study conducted throughout Türkiye (Emir, 2015). A study conducted in Greece reported that 30% of variable costs consisted of bee feeding costs (Makri et al., 2015).

Labor costs were found to be in second place with a rate of 20%. Previous reports have stated 10.81% of labor cost in Van province, 14.72% in the Aegean region, 51% in Canakkale province, and 68% in Greece (Aydın et al., 2019, Çevrimli & Sakarya, 2019b; Makri et al., 2015; Tosun, 2019). In a beekeeping study conducted in Serbia, labor costs were the primary cost element, with a rate of 24.7% (Nedić et al., 2019). Different labor percentages in these studies are attributed to the methodology difference, family labor, and the inclusion of foreign labor in different cost items. Compared to studies using similar methodologies in Türkiye, labor costs were found to be slightly higher in our study. This could be because of the involvement of more family members in beekeeping activities than required (disguised unemployment). For example, while daily hive controls are performed by women and children, activities such as hive transportation, feeding, and spraying are conducted by men, and all of them are included separately while calculating the family labor cost.

A study that calculated the unit cost of producing 1 kg of honey as 2.67 TRY (2.00 US$) in Izmir, 2.19 TRY (1.63 US$) in Mugla, and 2.29 TRY (1.70 US$) on average (Saner et al., 2004). Another study conducted throughout Türkiye (Emir, 2015) reported that the cost of producing 1 kg of honey was 6.96 TRY (2.56 US$), which was close to 2.49 US$/kg calculated in this study. In a study conducted in Albania, the cost of producing 1 kg of honey was reported to be 0.6–1.5 US$ (Dedej et al., 2015). Similarly, a study conducted in the Aegean region reported the average cost of 1 kg of honey as $2.5, the sale price was approximately $5, and the net profit per hive was $35 (M. B. Çevrimli & Sakarya, 2019b). We found that with an increase in the scale, the unit cost decreased. A study conducted in the Mediterranean region reported that the cost of 1 kg of honey was $5.30, and the sale price was $8.00 (Subaşı et al., 2019).

Data in Table 4 demonstrated that the small-scale enterprises had the lowest production cost in Cankiri province. The cost of 1 kg honey was $11, the sale price was $21, and the net profit per hive was $44 on average. The reason for the high profitability in the study conducted in the eastern black sea is due to the high yield per hive due to the high expected rainfall in 2017 compared to 2020, flowering, and nectar release period. However, due to some banning decisions taken by the Republic of Türkiye due to the COVID-19 pandemic in 2020 affected the level of interest of breeders in bees (Diktas-Bulut et al., 2022). This directly affected the yield. Considering all these factors, with the decreasing yield per hive, the fixed and production period costs per unit increased, directly affecting profitability (Altunel & Olmez, 2019).

The Cankiri province was found to be statistically different in terms of unit cost, sale price, and profitability figures per hive. This was attributed, in addition to chestnut honey, to the fact that a certain amount of flower honey is produced in the Cankiri province, thereby increasing the productivity per hive and reducing the unit cost. Since 1970 because flower honey is also sold at the sale price, prices are lower compared to chestnut honey (Göksu & Saner, 2021; Örük et al., 2022). Although the amount of flower honey sales was high, it reduced the profitability per hive. Because chestnut honey is largely sold in Kastamonu and Sinop provinces, the profitability figures were considerably higher.

Thus, enterprises should implement a strategy to increase the amount of chestnut honey production to meet marketing opportunities. This also explains why enterprise scales are growing despite the increase in production costs.

In the regression model, the dependent variable was unit cost. The labor (X3), with the largest coefficient in the regression model, was considered the independent variable. The increase in labor costs is a highly important variable that increases the unit cost. More effective and efficient use of labor input is required to reduce the unit costs of enterprises producing chestnut honey. Considering the inclusion of family labor, it is recommended to involve a smaller number of people in a more planned and programmed method instead of all family members working to reduce costs. A study showed that although beekeeping is a profitable activity, labor costs should be reduced, and along with honey, other more profitable bee products, such as pollen, should be produced (Kadirhanoğulları et al., 2016).

The secondary important coefficient is an independent variable of other expenses (X6). Transportation expenditures to control hives were included in other expenses. A study conducted in the Aegean region determined that other expenses were independent variables that markedly decreased the profitability in the regression equation, which was found to be compatible with this study (M. B. Çevrimli & Sakarya, 2019b).

Thus, it is recommended that controls created during the year should be planned and scheduled, and that hive locations should be close to producers’ residences by considering the chestnut nectar flow. Implementing a project similar to digitizing bee location points in Mugla province (M. B. Çevrimli & Tuncel, 2015) and determining chestnut honeybee location points and directing them to the closest points of transportation will help reduce production costs.

4. Conclusion

A decrease in honey yield with an increase in enterprise scales was observed. This is because, despite the growth of the scale, additional measures are not implemented by beekeeping enterprises. Therefore, despite the growth of the scale, the attention to beekeeping enterprises does not increase. In addition, the lack of sufficient increase in labor and technical infrastructure investments ultimately reduces productivity.

Consultancy services, particularly in the fields of maintenance and feeding, should be provided to the producers engaged in beekeeping as the primary business line to ensure high honey yields. In addition, the labor and other inputs must be provided in accordance with the principles of technical management with an increase in the enterprise scale.

Bee feeding is the primary cost element of beekeeping enterprises. Because of the change of seasons and precipitation regime and with global warming negatively affecting the nectar flow in nature, bee feeding costs have been estimated to increase in the future and maintain their place in the ranking.

Although studies on unit cost in beekeeping have implemented the same basic methodology, differences exist in the calculation methods used. Therefore, although cost elements with similar names were calculated in similar percentages, percentages of different cost elements could not be found at similar rates. It is important to perform financial comparisons and conduct unit cost studies in beekeeping under similar classifications and use them as an example over time.

As the scale grows, production costs are expected to decrease. In this study, the opposite was found. As the scale grows, the decrease in interest in beekeeping enterprises and the lack of knowledge increase production costs. The marketing problem of chestnut honey is less than other honey varieties. For this reason, beekeepers focus on producing more products instead of reducing their unit costs.

In conclusion, different kinds of honey are produced in different regions in Türkiye with a wide variety of nectar flows. The findings of studies conducted in different regions vary, and these variations are significant in econometric analyses. Therefore, different studies should be conducted on each production pattern in each region. In addition, producers in different regions in the same sector require different support systems. Each region should be offered solutions according to their specific problems, informing both enterprise owners and technical personnel. These studies should be regularly conducted in different production areas in different regions, ensuring correct situation analysis and contributing to the sustainability of enterprises.

Ethics Committee Approval

Ethics committee approval was received for this study from the ethics committee of Kastamonu University (Approval number: 2020/12).

Disclosure statement

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

Additional information

Funding

This work was supported by the [Kastamonu University, Scientific Research Projects Coordination Unit] under Grant [KÜ-BAP01/2020-42].

Notes on contributors

Murat Polat

Murat POLAT is working as a Assistant Professor Kastamonu University, Faculty of Veterinary Medicine, Department of Animal Health Economics and Management. He completed his doctorate in Ankara University Veterinary Health Sciences Institute, Department of Health Economics and Management in 2018. Since 2018, he has been working on the economics of animal products and production in the field of Animal Health Economics and Management. He carried out economic and econometric analysis studies in the poultry industry, honey production, sericulture, dairy cattle, especially in the red meat industry, which is his area of expertise. He also gives undergraduate courses on Animal Health Economics and Management at Kastamonu University, Faculty of Veterinary Medicine. He also takes part in many boards and commissions related to the field of Livestock Economics.

Mustafa Bahadır Çevrİmlİ

Mustafa Bahadır Çevrimli (Ph.D.)-Associate Professor, Department of Animal Health Economics and Management, Faculty of Veterinary Medicine, Selçuk University, Konya, Turkey.

Burak Mat

Burak Mat (Ph.D.)-Assistant Professor, Department of Animal Health Economics and Management, Faculty of Veterinary Medicine, Selçuk University, Konya, Turkey.

Ahmet Cumhur Akin

Ahmet Cumhur Akın (Ph.D.)-Associate Professor, Department of Animal Health Economics and Management, Faculty of Veterinary Medicine, Mehmet Akif Ersoy University, Burdur, Turkey.

Mehmet Saltuk Arikan

Mehmet Saltuk Arıkan (Ph.D.)-Associate Professor, Department of Animal Health Economics and Management, Faculty of Veterinary Medicine, Fırat University, Elazıg, Turkey.

Mustafa Agah Tekİndal

Mustafa Agah Tekindal (Ph.D.)-Associate Professor, Department of Biostatistics, Faculty of Medicine, Izmir Katip Çelebi University, Izmir, Turkey.