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

اقتصاد و توسعه کشاورزی

Article 3, Volume 33, Issue 4 - Serial Number 46, December 2020, Pages 351-361   

Document Type: Research Article

DOI: 10.22067/jead2.v33i4.79473

Authors

M.H. Karim^{* 1}; A. Dadras Moghadam²; S.M. Hosseini³; S.M. Seyedan⁴

¹Kharazmi university

²Assistant Professor univesity of Sistan and Baluchestan, Zahedan, Iran

³univesity of Sistan and Baluchestan, Zahedan, Iran

⁴Economic, Social and Extension Research Department, Hamedan Agricultural and Natural Resources Research and Education Center, Iran

Abstract

Introduction and Aim: 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. .
Materials and Methods: 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.
Results and Discussion: 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.
Conclusion: 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.

Keywords

Barberry; Genetic function approximation algorithm; Jujube; Risk Management; South Khorasan Province

References

1- Agricultural Statistics (Gardening Products) .2016. Ministry of Agriculture.

2- Akbari A., Shahikitash M., and Yazdani M. 2015. Identification of factors affecting the risk of pistachio production in Sirjan County (using Tobit regression). Agricultural Economics Research 23: 175-190. (In Persian with English abstract)

3- Amani A.R., and Nikandish A. 2010. Identification of socioeconomic factors affecting the application of agricultural risk production strategies by Dezfol wholesalers. 91-101.

4- Amiri Z., Asgharipour M.R., Ramurodi M., and Kakolvand I. 2017. The effectiveness of the technical-promotional project on managing the risks of the agricultural sector in sustainable production and reduction of damage (case study of Lorestan province). The first international conference on natural hazards and environmental crises in Iran. Solutions and challenges. (In Persian with English abstract)

5- Annepu G.K., Subbaiah V., and Kandukuri N.R. 2011. Land allocation strategies through genetic algorithm approach- A case study. Global Journal of Research in Engineering 4: 54-69.

6- Altieri M.A. 2018. Agroecology: the science of sustainable agriculture. CRC Press.‏

7- Bahrami A., and Hossein A. 2006. Risk management in agriculture. Insurance and Agriculture magazine. (In Persian with English abstract)

8- Bala J., Huary J., Vafaie H., De Jong K., and Wechslev H. 1995. Hybrid learning using genetic algorithms and decision trees for pattern classification, IJCAI Conference, Montreal, August. 19-25.

9- Bellarby J., Siciliano G., Smith L.E.D., Xin, L., Zhou J., Liu K., and Surridge B. 2017. Strategies for sustainable nutrient management: insights from a mixed natural and social science analysis of Chinese crop production systems. Environmental Development 21: 52-65.‏

10- Blumenthal M. 1998. The complete German Commission E Monographs: Therapeutic Guide to Herbal Medicines. Austin: American Botanical Council.

11- Cole S., Gine X., and Vickery J. 2017. How does risk management influence production decisions? Evidence from a field experiment. The Review of Financial Studies 6: 1935-1970.‏

12- Dourandish A., Daneshvar K.M., and Rahnama A. 2011. Investigating effective quality factors on barberry prices (Case study: South Khorasan province). Journal of Agricultural Economics and Development 3: 385-391. (In Persian with English abstract)

13- Duke J.A. 2002. Handbook of Medicinal Herbs, Second Edition Boca Raton, FL: CRC press, 899.

14- Eshghi K., and Karimi M. 2011. Combined optimization and Meta Herostorica algorithms. Tehran, Azarin Mehr publications. 100-250.

15- Ghorbani M., and Jafari F. 2009. Investigating the factors affecting the risk of crop risks in North Khorasan Province. Journal of Agricultural Economics and Development (Science and Technology of Agriculture) 1: 41-48 .(In Persian with English abstract)

16- Golmohammadi F., Razavi H., and Masoumeh P. 2013. The economic importance of agriculture and medicinal properties of barberry plant in South Khorasan province is an appropriate option for agricultural development in arid and semi-arid regions of Iran (considering its marketing management as the main export and income-producing commodity in this area). Fourth regional conference on challenges and development strategies in deprived Areas. (In Persian with English abstract)

17- Gravandi S., and Alibeigi A. H. 2011. Identification of agricultural risk management determinants: A study of farmers in Kermanshah. Iranian Journal of Agricultural Economics and Development 2: 255-264 .(In Persian with English abstract)

18- Gravandi S., and Alibeigi A.H. 2011. Determination of effective factors on using risk management strategies for Kermanshah corn farmers. Rural Research Quarterly 2:118-135. (In Persian with English abstract)

19- Gravandi S., and Alibeigi A.H. 2011. Identification of effective factors on behavioral response of Kermanshah corn farmers against production risk. Iranian Journal of Agricultural Research & Development 2: 293-300. (In Persian with English abstract)

20- Heidari S., Marashi S.H., Farsi M., and Mirshamsi K.A. 2009. Investigation of the diversity of wild and agricultural masses and agriculture of barberry in Khorasan province by using morphological markers and evaluating its effectiveness in systematic studies. Iranian Journal of Agricultural Research 2: 401-410. (In Persian with English abstract)

21- Hasanpour M.R., and Seddiqi H. 2011. Risk Management in Agriculture.

22- Holland J.H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.

23- Jang J.R., and Sun C. 1995. Neuro-fuzzy modeling and control, Proc. IEEE Conf. on Fuzzy Syst. San Francisco. 378-405.

24- Kani F., Asgari F.M., Savari M., and Daneshvar A.J. 2014. Analysis of factors influencing production risk management in Jiroft oranges. Iranian Economic Research and Development, 1:57-67. (In Persian with English abstract)

25- Khajeh A., and Modarress H. 2010. QSPR prediction of flash point of esters by means of GFA and ANFIS. Journal of Hazardous Materials. 179:715–720.

26- Khaled K.F., and El-Sherik A.M. 2013. Using molecular dynamics simulations and genetic function approximation to model corrosion Inhibition of iron in chloride solutions. International Journal of Electrochemical Science 8: 10022-10043.

27- Metchell M. 2004. An introduction to genetic algorithms. A brad ford book. Combridge, Massachusetts. London. England.

28- Mohammadi Y., Nakhaee M., Khodadad M., and Pirmoradi A.H. 2016. Drought risk management and its role in sustainable development in rural areas of Bonab. Sixth National Conference on Water Resources Management, the University of Kordestan. (In Persian with English abstract)

29- Mortazavi S.A., Ghorbani M., Alaali B.P., and Alipour A. 2013. Factors affecting the risk of pomegranate production with poverty approach (Case study: villages in the central district of Shahrreza City). Agricultural Economics Research 3: 27-31. (In Persian with English abstract)

30- Mousavi S.N., Mohammadi H., and Taheri F. 2009. Investigating the effects of production factors on product risk in pistachio gardeners in Fars province. Journal of Extension and Agriculture Economics. 1-16. (In Persian with English abstract)

31- Najafi K.A.A., and Haji Hosseini A. 2014. Investigating the factors of risk management in the agricultural sector: A case study of wheat traders and sectaries in the Kangar village. Kalale County The first national conference on tourism and natural resources and sustainable development. (In Persian with English abstract)

32- Rosta K. 2009. Investigating factors affecting farmers' ability in risk management (A case study of wheat farmers of Khorasan Razavi). Journal of Extension and Agriculture Economics 1: 1-16. (In Persian with English abstract)

33- Rogers D., and Hopfinger A.J. 1994. Application of genetic function approximation to quantitative structure–activity relationships and quantitative structure–property relationships. Journal Chemical Infornation Computre Science 34: 854–866.

34- Samuel H., Uzairu A., Mamza P., and Oluwole Joshua O. 2015. Quantitative structure-toxicity relationship study of some polychlorinated aromatic compounds using molecular descriptors. Journal of Computational Methods in Molecular Design 3:106-119.

35- Salami H., and Thamipour M. 2014. Determination of factors affecting the risk of corn prices in Iran. Agricultural Economics and Development 89: 95-114. (In Persian with English abstract)

36- Sataroloyobi F. 2017. Risk assessment of selected agricultural projects in Kerman province, using fuzzy method Under Uncertainty. Master's Thesis, University of Sistan and Baluchestan. (In Persian with English abstract)

37- Shahabpour S.M. 2009. Knowledge and Importance of Barberry, Gardener 33: 35-37. (In Persian with English abstract)

38- Suganthi L., and Samuel A.A. 2012. Energy models for demand forecasting a review. Renewable and Sustainable Energy Reviews 2: 1223-1240.

39- Sun X., Zhang X., and Hu H. 2009. Berberine inhibits hepatic stellate cell proliferation and prevents experimental liver fibrosis. Biological & Pharmaceutical Bulletin 9: 1533–1537.

40- Thamipour M. 2007. Investigating factors affecting the risk of Pistachio production in Zarand, Agricultural Economics and Development 63. 1-19 .(In Persian with English abstract)

41- Torkamani J. 1997. Risk intervention in agricultural economics planning: applying risk phase planning. Quarterly Journal of Agricultural Economics and Development 15: 130-113. (In Persian with English abstract)

42- Torkamani J. 2009. Effect of agricultural insurance on risk reduction and income inequalities (Case study of Fars province). Journal of Agricultural Economics Research 1: 17-34. (In Persian with English abstract)

43- Torkamani J., and Mousavi N. 2010. Effect of agricultural insurance on productivity and risk management in agriculture (Case study in Fars province). Journal of Agricultural Economics Research 1: 1-26. (In Persian with English abstract)

44- Tseng L.Y., and Yang S. 1997. Genetic algorithms for clustering feature selection and classification, IEEE Int. Conference on Neural Networks 1612-1616.

45- Vafaie H., and De Jong K. 1993. Robust feature selection algorithms, Proc. of the fifth conference on tools for artificial intelligence, Boston, MA: IEEE Computer Society Press 356-363.

46- Vafaie H., and De Jong K. 1992. Genetic algorithms as a tool for feature selection in machine learning. Proc. of the 4th Int. conference on tools with artificial intelligence, 200-204.

47- Vafaie H., and Imam I. 1994. Feature selection methods: genetic algorithms vs. greedy-like search. Proc. of the Int. Conference on fuzzy and intelligent control systems.

48- Vazifeshenas M. 2002. Root beetroot cuttings using growth regulator treatments. Peak and fog, Shiraz University. (In Persian with English abstract)

49- Yaghoubi A., Chizari M., Felli S., and Pezeshki Rad G. 1988. Factors affecting risk management among Wheat farmers in Tafresh County, Iranian Journal of Science Promotion and Education 1: 91-101.

50- Yin J., Xing H., and Ye J. 2008. Efficacy of bebeerine in patients with type 2 diabetes mellitus, Metabolism: Clinical and Experimental 5: 712–7.