Modeling the Effective Factors on the Risk Management of Barberry and Jujube Producers of South Khorasan Province Using the GFA Approach

Introduction and

Agriculture is a risky occupation. The different natural, social, and economic hazards have created handicaps and problems for agricultural producers, which result in the instability of income for producers. In general, the nature and environment of agricultural activities are at high risk. The risks of agricultural activities are often associated with low performance, increased cost, and time lag. In order to from the perspective of risk management, studies can be categorized into three types including product hazards, price risks, and natural hazards. Major economies in the rural areas of south Khorasan province is based on the planting and selling three products of barberry, saffron and jujube, which are resistant to water deficit and drought, and at the same time all have the high economic value. This province in Iran accounts for about 96 to 98% of the barberry and jujube production. Therefore, this study aims to examine the factors affecting the barberry and jujube production in the South Khorasan province, Iran. .

Genetic function approximation (GFA) algorithm describes the basic problem of approximating the function. Many factors affect the response variable and primary input correlated with best response. The GFA algorithm works with a range of strings called population, developed for the purpose of searching.  The selection, crossover, and mutation operators also run appropriately. New members were given scores according to the estimator’s criteria. In the GFA, the criteria scoring is obtained for fitted regression models. The selection probabilities should add each new member to the population again. This method continued for a specified number of generations until the point of convergence. GFA algorithm can be used to produce a generation with respect to the evolution charts according to the achieved time. This graph shows the number of events for each variable about the population, which has evolved for each generation. GFA algorithm converges as no progress in population occurred. At this time, the model is significant which means the best choice for all models of population. Using GFA and MS modeling software, modeling is used to identify the factors affecting the knowledge of barberry and jujube producers in order to determine which one of 15 independent variables were effective on the risk management information of barberry and juvenile producers in South Khorasan province.

The results showed that frostbite risk had the lowest risk, affecting the risk index of barberry producers. Awareness of hail hazards exerted the greatest effect on the risk index of jujube producers in south Khorasan region. The least effective factor was the awareness of fire hazards in the jujube gardens.

The results suggest that most jujube and barberry producers use traditional knowledge and experience and do not acquire the necessary training that are needed for dealing with natural hazards due to poor knowledge and non-compliance with the principles of agricultural education in dealing with hazards. Therefore, it is recommended  that the system of agricultural extension and education and agricultural meteorology must have been improved the knowledge of the farmers' risk management and skills in the southern Khorasan region by presenting appropriate training programs to address these risks (drought, , storm, sudden precipitation, etc.). Accordingly, the information and promotion system, accompanied with appropriate training in relation to new innovations, will increase the awareness among the male and female producers and government support should be taken in this area.  So producers can use modern methods for better managing natural hazards in the Jujube and Barberry gardens of South Khorasan province.