|
|
|
|
|
|
|
|
|
|
|
RSS |
|
|
|
 |
|
|
|
|
|
|
|
|
|
|
Analysis of economic indicators of trout farm fisheries (Case study: Hamedan Province)
|
Mahsa Motaghed1   , Amir Dadras Moghadam , Seyed mohsen Seyedan |
|
|
|
Abstract: (366 Views) |
Introduction
In Southwest Asia and Iran, the fisheries industry is an important sub-sector of agriculture because it provides a portion of the valuable protein for society, employment, and national income. The growth of the aquaculture industry in the world is 4.5% and it is 7% (Agricultural Research, Education and Extension Organization, 2023) in Iran. In addition to providing food for the growing population and creating jobs and income for farmers, this can also play an important role in earning foreign exchange for the country. Aquaculture in the country includes warm-water fish farms (carp), cold-water fish farms (trout), and shrimp farms. According to statistics published by the Iranian Veterinary Organization (2023), Iran ranks first in the world in the production of cold-water fish in freshwater with a production of 237 thousand tons (Iranian Veterinary Organization, 2024). In the west of the country, and especially in Hamedan Province, due to its geographical location, the presence of natural canals, seasonal and permanent rivers, and springs, it is possible to grow and develop trout farms and ponds, which are nutritionally the best type of protein for human consumption. The trout farmers in Hamedan Province produced more than 5,237,000 tons of trout in the year (2021-2022) and sold them fresh in markets inside and outside the province (Census of Jihad Agriculture, 2022-2021). Given the special situation of Hamedan Province in the field of fish production, a comprehensive study has not yet been conducted on the analysis of the economic indicators of trout farms in this province. Therefore, the above study aimed to investigate cold-water fish producing farms that used researchers' recommendations in their management and production methods based on the results of research projects as the treatment group and units that were managed traditionally and without using research recommendations in their management and production as the control group.
Methodology
This research is a descriptive-analytical study that is applied in terms of purpose and is considered a survey in terms of data collection. The statistical population of the research includes cold-water fish producers (trout) in Hamedan province. The sample size of 49 operators was calculated using the Cochran formula and was systematically selected from the list of farms in the province using simple random sampling. The required data was collected and completed through a researcher-made questionnaire in 2023, including 5 sections: fish farm manager's characteristics (11 questions); fish farm characteristics (30 questions); technical aspects of production (35 questions); fixed investment costs (12 questions) and current costs (11 questions), and by visiting the Fisheries Department and the Agricultural Jihad Organization of the province in person. The opinions of agricultural and fisheries economics experts were used to examine the validity of the questionnaire, and the Cronbach's alpha test was used to examine the reliability of the questions. Using the matching method, the statistical population was first divided into two groups: the treatment group (units in which the research results were used) and the control group (units that did not use the research results or used them less), and then the two groups were compared using efficiency indicators, internal rate of return, benefit-to-cost ratio, payback period, and production function.
Results
The results of examining the balance of auxiliary variables showed that by performing the matching operation to calculate the effects of the research results on the treatment group, the difference between the means of the two groups decreased. To compare the two treatment and control groups and the effect of the factors of fry, labor, capital, food, and pond area on their production rate, two transcendental and Cobb-Douglas production functions were estimated simultaneously and these two specifications were compared using the F test. According to this function, the most important factor in creating differences between units is the variable of pond area. Among the variables used, in addition to the pond area, food, fry and labor also showed a significant effect on production. The results of the Ramsey reset test indicated that there was no specification bias due to the removal of the important variable. In the treatment unit, regarding the pool area variable, considering the logarithmic specification, it can be said that with a 10% increase in the pool area, it is expected that about 13.1% will be added to production. In terms of the contribution to creating a difference in the production level of the units, the pool area variable is not only significant in terms of the absolute value of the coefficient, but also has a very high difference, so that the other four significant variables, including the fry, food, and labor variables, with a 10% increase, can increase production by 11.9, 9 and 12.5%, respectively, which is very different from the food variable. In the control unit, with a 10% increase in the values of the variables under study, the production rate increases by 5.6% less than the values mentioned in the treatment group. Based on the results, the efficiency of the units under study, including technical, allocative, and economic efficiency, has been examined under two assumptions of constant and variable returns to scale in two treatment and control groups. The results showed that the average technical efficiency of trout farms in the treatment group under CRS conditions is 90.5 percent. While the average technical efficiency in the same group is 91.4 percent. The average scale efficiency of trout farms in the treatment group is 99.02 percent. In the control group, the efficiency under CRS and VRS is 82.2 and 83.3 percent, respectively, and the scale efficiency is 98.7 percent. Technical efficiency in the treatment group is much better than the control group. The use of research recommendations has increased technical efficiency by 8.3 percent under CRS and 8.1 percent under VRS. The average allocative efficiency in the treatment group was estimated to be 86.8 percent under CRS and 87.6 percent under VRS. The allocative efficiency in the control group is 75.3 percent under CRS and 70.4 percent under VRS. The use of research recommendations has increased allocative efficiency by 11.5 percent assuming CRS and 17.2 percent assuming VRS. The average economic efficiency of the fish farming treatment group farms under CRS conditions is 78.6 percent and 80.1 percent under VRS conditions. The economic efficiency in the control group under CRS conditions is 61.9 percent and 58.6 percent under VRS conditions. Considering the technology available in fish farming farms, it is possible to increase profits in control units by 16.7 percent assuming CRS and 21.5 percent assuming VRS. Therefore, by improving the economic efficiency of fish farming in the control group, it is possible to increase product production and profitability of the units. Considering a discount rate of 16%, the internal rate of return and the benefit-cost ratio in the treatment group are 33.8% and 1.8%, respectively. These two indicators in the control group are 18.8% and 1.2%, respectively. The payback period in the treatment and control groups is 4.8 and 7.6 years, respectively. Therefore, it can be said that the investment in both groups is economically justified, while in the treatment group, it is in a better position in this respect.
Discussion and Conclusion
Based on the results, the application of research recommendations in the trout farming industry will increase the efficiency and effectiveness of this industry. The results of the studies of Rahman et al. (2019); Samat et al. (2024) and Duy et al. (2023) confirm this. Also, the average productivity of production factors in the treatment group (units that used research results in their activities) was 5.9 times higher than the control group, which is in line with the study by Najafi et al. (2018) and Vormedal (2024) that the use of research findings plays an effective role in increasing productivity. It is also necessary to make the activities of production units more competitive by implementing government economic adjustment policies, reducing production subsidies, further convergence of international markets, and increasing the competitive power of units. Therefore, the area of the pond has a significant impact on profitability. Therefore, the results of the research are consistent with the results of the study by Yarahmadi et al. (2022) and Akter et al. (2024), as they showed in their results that the amount of feed consumed and the area of the fish pond had the greatest impact on trout production. In addition to the above, the results showed that the internal rate of return in the treatment group was 33.8 percent and the benefit-to-cost ratio was 1.8. These two indicators in the control group farms were 18.8 percent and 1.2 percent, respectively. The results of Duy et al. (2023) also confirm this issue and stated that training and promotion of research findings had a positive effect on the efficiency and effectiveness of the firms receiving these findings and reduced production costs in these firms. Therefore, by using research recommendations and promoting applied research findings, this gap can be reduced as much as possible and lead to increased efficiency of these types of activities.
Conflict of Interest
The authors declare no conflict of interest.
Acknowledgment
The authors acknowledge the support provided by the Agricultural Research, Extension and Education Organization. |
|
| Keywords: Trout, Data Envelopment Analysis, sorting, internal rate of return (IRR), benefit-cost ratio. |
|
|
Full-Text [PDF 795 kb]
(104 Downloads)
|
Type of Study: Research |
Subject:
اقتصاد شيلاتي
Received: 2024/10/3 | Accepted: 2025/04/30
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
|
|
|
|
|
|
|
|
