Agricultural Economics
T. Ranjbar; S.M. Mojaverian; Z. Raftani Amiri; S. Shirzadi Laskookalayeh; F. Eshghi
Abstract
There are four interconnected markets, i.e. oilseeds, crude oil, meal and edible oil, in the vegetable oil supply chain. Nowadays, emerging tools in context of information and communication technologies (ICTs) have critical role to develop the supply chain. The purpose of this study is to identify and ...
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There are four interconnected markets, i.e. oilseeds, crude oil, meal and edible oil, in the vegetable oil supply chain. Nowadays, emerging tools in context of information and communication technologies (ICTs) have critical role to develop the supply chain. The purpose of this study is to identify and prioritize actors' preferences for using blockchain technology in the vegetable oil supply chain. For this purpose, we applied the Analytical Hierarchy Process (AHP) method. We interviewed 15 experts, including scientific specialists from adjacent fields and actors in the vegetable oil supply chain, in 2021, to determine the weight of the pairwise comparison matrix. This study evaluated the leading indicators of management improvement, performance improvement, data security, transparency, traceability and visibility, as well as their sub-indicators. The calculation of final weight revealed the most relevance of sub-indices, i.e. increasing inter-organizational trust, compatibility and secure data compatibility, with value of 0.467, 0.043 and 0.043, respectively. The rest of the indicators were also ranked as data immutability, close relationship with suppliers, degree of privacy, forecasting, strategic planning capabilities, reduction of lead time and doing the order on time, respectively. The lack of trust between circles and actors is thus the most crucial obstacle and the largest potential for the new chain in the current supply chain. More training and knowledge of supply chain players on emerging technologies should be put on the agenda to achieve optimal supply chain management. Our results also suggested solutions for advocating for the planning and development of the required infrastructure for the implementation of blockchain technology in Iran.
T. Ranjbar; S.A. Hosseini Yekani; S.M. Mojaverian
Abstract
Introduction: Farming has a significant role in the economy of developing countries. The farming activities face with various dangerous, non-certainties and lots of problems due to natural disasters, price fluctuations in market place and social and behavioral conditions of farmers. Farmers encounter ...
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Introduction: Farming has a significant role in the economy of developing countries. The farming activities face with various dangerous, non-certainties and lots of problems due to natural disasters, price fluctuations in market place and social and behavioral conditions of farmers. Farmers encounter lots of risks in their farming decisions. Generally, there are three kinds of farmers including 1) risk-averse 2) risk-neutral and 3) risk-taker. The majority of previous studies have shown that the most of farmers are risk-averse, but with different rates of risk aversion.
Materials and Methods: Estimating the utility function is one way to quantify the risk. But while there is no certainty and decision making condition is risky, concept of “expected utility” will be considered instead of general concept of utility. In the present study, Direct Elicitation Utility Function (DEU) is used in order to calculating the degree of absolute risk aversion of farmers. In this approach, it is assumed that individual farmers are concerned about the variability of return of production decisions.
The utility function will be shown with U(Y) in which Y is the monetary gross margin of a farmer in specific period of time. The expected utility of the farmer is . The expected monetary margin will be defined with and certainty equivalent (CE) is the monetary margin that comes from the relation of . In DEU method, several mathematical forms of utility functions can be considered as the utility function of producers. Since in the form of negative exponential utility function, the absolute risk aversion coefficient is constant, in this study, the utility function of producers is , where shows the degree of absolute risk aversion. After calculation of farmers’ absolute risk aversion coefficients, the relationship between calculated coefficients and socio-economic characteristics of farmers (such as their age, farm size, family size, education and agricultural experience) were analyzed.
Results and Discussion: In compliance with relation and considering the negative exponential utility function, can be proved:
Where to are four probable levels of farmers’ gross revenues and to are the probabilities of these revenues. Utilizing DEU method, the rates of absolute risk aversion of farmers (high risk aversion, weak risk aversion and medium risk aversion) were calculated for 169 farmers in Sari-Goharbaran. According to the results, 41 farmers (24.2 percent) were weak risk averse, 81 farmers (47.9 percent) were medium risk averse and 47 farmers (27.8 percent) were high risk averse. Findings of the study showed that most of the farmers are medium risk averse. The second part of the findings showed that there is a significant relationship between farmers’ age, farm size, family size and farming experience and the rate of absolute risk aversion. As it was shown in the table 3, the age of farmers has positive relation with the degree of absolute risk aversion of farmers and the family size, farming experiences as well as farm size have negative relation with that degree. Also, according to the t-statistic, estimated coefficients were statistically meaningful at 95% and 99% which means if the farmer’s age increases by one year, then the degree of risk aversion of farmers rises by 95% confidence level, ceteris paribus. In addition, it can be stated that if the farming experiences increase by one year, the absolute risk aversion coefficient declined by 0.34 unit, by 99% certainty. Similarly, by increasing the number of family members and size of farms by one unit, the degree of risk aversion of farmers reduced to 0.37 and 0.98 unit respectively as well.
Conclusion: As the results advocate, the majority of farmers are in the class of average risk averse. Therefore, some measures should be taken to decrease the degree of risk aversion of farmers. This can carried out by the farmers as well as the agricultural sector policy makers. Utilizing the risk reduction techniques, such as crop diversification, insurance, establishing commodity derivatives and futures markets, farmers can reduce their risks. According to one of the results of this study, stating that whenever the farm sizes have risen, the degree of risk aversion has dropped, it is suggested that policy makers try to integrate lands in the agricultural sector. Also, as it is revealed that, by enhancing the experience of the farmers, their degree of risk aversion declines, so, through the educational and prompter classes, the farmers experience can be enhanced, despite the fact that education directly has no significant effect on the degree of risk aversion.
