Operational Policy Making for the Development of Pistachio Drought Insurance in Sabzevar County

مجله اقتصاد و توسعه کشاورزی

Article 5, Volume 38, Issue 1 - Serial Number 62, April 2024, Pages 85-100   

Document Type: Research Article

DOI: 10.22067/jead.2024.85860.1234

Authors

M. Mirchooli; M. Ghorbani^* ; M. Sabouhi

Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction
The dependence of agriculture on environmental conditions has caused the activity in this sector to face natural and unnatural risks. After several years of agricultural insurance activity in Razavi Khorasan province, most of the pistachio farmers are not insured. Drought insurance is one of the methods that has become important to cover the risks of drought and lack of water resources in order to compensate part of the gardeners' losses. The main issue from a managerial perspective is risk management. The use of agricultural insurance, which is one of the risk management tools, will ensure financial security and stability for farmers. Given that insurance is a tool for risk management, and given the uncertainty and risks of climate change in agriculture, insurance can be a very adaptable tool to water scarcity. Agricultural insurance is considered as a useful and appropriate solution to deal with natural hazards. . Drought insurance is an important factor in off-farm drought risk management that can mitigate the effects of this inevitable phenomenon. Insurance as one of the risk management tools can increase the risk-taking of farmers and, consequently, increase the sense of security in farmers, the necessary ground for proper and efficient use of factors of production and investment in the use of new technology and thus increase productivity in agriculture provide. The effects of water scarcity can be summarized as follows; Loss of production and income, abandonment of busy crops (with high water demand) and decline in agricultural employment, on the other hand, intensifies the over-exploitation of groundwater aquifers, which has tempted many farmers to do so meet your water needs. Access to water in the study area is one of the important variables affecting pistachio yield and quality as well as the survival of pistachio trees. This variable directly affects the profitability of producers and gardeners may suffer losses from this vital input. For this reason, gardeners' behavior in relation to regular pistachio insurance can affect access to water and make more farmers inclined to drought insurance.
 
Materials and Methods
This research seeks to answer the question that with 5% reduction in available water, pistachio growers in Sabzevar city, whether these people are willing to accept pistachio drought insurance or not, and if so, what is the extent of this desire. The Probit pattern is one of the most suitable econometric patterns for censored observations. This model was first proposed by Tobin (1958) to estimate the demand for durable goods. Subsequently, Arab Mazar and Schmidt (1979), Brown and Mufit (1982), Madela and Nelson (1982), and Hard (1975) worked on and developed the model, validating its high capability. This pattern was named by Goldberger (1964) as the Tobit or Probin Tobin model. Assume that y is the level of activity or action desired and xi are factors that generally affect the level of activity or action in question, namely:
Also assume that one group of the observed observations performs the desired activity and the other group (the rest) does not perform the desired activity. As mentioned earlier, the values of xi and yi are visible for the first group. While for the second group only xi values are available and yi values are zero.
In Hackmann's proposed two-step method for estimating the Tobit model, it is assumed that one set of variables may influence the decision to participate in the activity and another set of variables may affect the amount of activity performed after the decision is made. Therefore, two different sets of variables can be included in the Probit model, which are not necessarily barriers to aggregation. Therefore, two different sets of variables can be included in the Tobit model, which are not necessarily barriers to aggregation. Because it does not have a one-step model of this flexibility, it assumes that the variables influencing a person's decision to engage in an activity are the same as the variables that determine the amount of activity, if this is not necessarily the case. Hackman's two suggested steps are:
Step 1: In the first step, the variables that affect the decision of gardeners in accepting pistachio drought insurance are identified and placed in a model with a binary dependent variable (zeros and ones); This means that the positive values of the dependent variable that indicate the tendency to accept pistachio drought insurance become the number one, and the dependent variable that does not tend to accept the drought insurance is set to zero. The number one means the decision to perform the activity and zero means the non-performance of the activity. At this stage, in order to identify the factors influencing the individual's decision, the Probit Model is used and estimated by the maximum likelihood method. The first step is to create a new variable inverse of the Mills ratio to enter the second step. In other words, this variable is the first and second stage communication bridge.
Step 2: In the second stage, the measures affecting the willingness to participate in drought insurance after the decision is made along with the inverse Mills ratio variable are placed in a classical regression model. The dependent variable in the second stage is the amount of garden area likely to be allocated to drought insurance.
Reasons to use the Tobit model: Many econometric models face two types of errors, either due to the use of specific observation data or due to the structural features of the models: first, the error due to incorrect sample selection, which usually occurs in using classical regression models, and second, the same error Assuming effective variables in the decision stage and the amount of activity performed after the decision is made (decision and action or intention and action), which usually occurs in regression models with binary and multiple responses. The Tobit model has been developed to prevent the occurrence of these two types of errors in studies.
The first error is the error of incorrect sample selection; in the sense that in many econometric models, information is obtained only from observations that have acted on the activity and omits observations that have refused to do that activity. Therefore, these models are not able to assess the reaction of observations that did not act on the independent variable changes. Tobit model (type one) solves this problem in terms of observations that have performed the desired activity as well as other observations. Under these conditions, the effect of changes in independent variables on both the total observations and on the observations of the activity can be calculated separately.
The second error means that the factors that influence a person's decision to perform an activity are not necessarily the same as the factors that determine the amount and level of activity desired, and can be two different sets of variables. The Tobit model (type two, Hackett or Hackman two-stage) solves this problem by separating the factors influencing the decision and the amount of activity.
 
Results and Discussion
The data show that the response of pistachio growers to the reduction of available water in the next 2 and 5 years is that all gardeners will insure their pistachio orchards with a 5% reduction in available water, but in terms of area under cultivation, only 39% Gardeners will increase their arable land in the next 2 years and 33% of gardeners in the next 5 years. The reaction of gardeners who did not have a history of pistachio insurance to accept pistachio insurance and increase or decrease the area under pistachio orchard in exchange for a 5%  reduction in available water in the next 2 and 5 years shows that about 51% of gardeners face a 5%  reduction in water in 2 And in the next 5 years, they will insure their pistachio orchards, and about 60%  of gardeners will increase their cultivation in the next 2 or 5 years in the face of a 5%  reduction in available water. The results of the evaluation of gardeners' reaction to the continuation of the horticultural profession in the face of a 5%  reduction in available water in the next 2 years will cause 34%  of gardeners not to continue this profession and 51%  of gardeners will not continue this profession in the next 5 years. In the long run, water shortages can reduce the incentive for gardeners to grow pistachios. The reaction of gardeners to pistachio insurance against the reduction of available water quality shows that only 1.38 percent of the total population in the face of reduced quality of available water reduce the level of their insured garden and about 30% of them faced with declining available water quality, they will increase the level of their insured garden; And the rest of the gardeners (about 68.6%) do not change their insured level in the face of declining water quality.
Conclusion
According to the obtained information, the variables as gardener's age, ownership, relationship between gardener's field of study and agriculture, location, variety of cultivation, existence of insured pistachio garden in the neighborhood, frequency of risk, total water available to each gardener and garden life of each gardener in the first stage (Probit Model) have positive coefficients; which indicates the positive effect of these variables on the probability of willingness to accept pistachio drought insurance. In the second stage (linear regression), the variables of pistachio horticulture history, frequency of risk, garden life and total number of water hours available to gardeners have positive coefficients, which indicate the positive effect of these variables on the dependent variable of the second stage, is the tendency to accept pistachio drought insurance.

Keywords

Access to water; Agricultural insurance; Drought; Hekman 2-stage; Tobit Model

References

1. Bahrami, W., Agahi, H., & Rangin, H. (2009). A two-parameter Balakrishnan skew-normal distribution. Journal of Statistical Reseach of Iran, 2, 231-242. )In Persian with English absrtact).
2. Barghi, H., Nouri, R., Baratizadeh, F., & Mohammadi, R (2017). Factors influencing the insurance of agricultural and livestock products in rural areas (case study: Khomein city). Research and Rural Planning Journal, 6(3), 19-33. )In Persian with English absrtact).
3. Barnett, J.B., Skees, R.J., & Hourigan, D.J. (1990). Explaining participation in Federal Crop Insurance, Annual meeting, August 5-8, Vancouver, Canada from American Agricultural Economics Association. https://doi.org/22004/ag.econ.270875
4. Beniston, M. (1992). Climatic change in mountain regions: A review of possible impact. Climate variability and change in high elevation regions: Past, present and future. Spronger, Dordrecht, 2003: 5-31. https://doi.org/10.1007/978-94-015-1252-7_2
5. Binswanger, P.H., Khandker, R.S., & Rozenzweig, R.M. (1993). How infrastructure and financial institution affect agricultural output and investment in India. Journal of Development Economics, 41, 337-366. https://doi.org/10.1016/0304-3878(93)90062-r
6. Carter, M., Janvry, A., Sadoulet, E., & Sarris, A. (2014). Index-based weather insurance for developing countries: A review of evidence and a set of propositions for up-scaling. Development policies working paper, 11.
7. Chalise, L., Coble, K.H., Barnett, B.J., & Miller, J.C. (2017). Developing area-triggered whole-farm revenue insurance. Journal of Agricultural & Resource Economics, 42, 27-44.
8. Chambers, G.R., & Quiggin, J. (2003). Price stabilization and the risk-averse firm. American Journal Agricultural Economics, 85(2), 336–347. https://doi.org/10.1111/1467-8276.00123
9. Chavas, J., & Mullarkey, D. (2002). On the valuation of uncertainty in welfare analysis. American Journal Agricultural Economics, 84(1), 23–38. https://doi.org/10.1111/1467-8276.00240
10. Dashti, Gh., Khaksar Khiabani, F., & Ghahremanzadeh, M. (2013). Determining the factors affecting the production and risk of onion production, Tabriz Plain. Economic Research and Agricultural Development of Iran, 44(3), 397-389. (In Persian).
11. Enjolras, G., & Sentis, P. (2008). The main determinants of insurance purchase: an empirical study on crop insurance in France. Documents de Research parus.
12. Ghalavand, K., Chizari, M., Feeli, S., & Baghaie, M. (2004). Investigating factors affecting the acceptance of agricultural products insurance among wheat farmers in Tehran and Mazandaran provinces. Agricultural Insurance Quarterly, 3(11), 49-68. (In Persian)
13. Garcia-Ruiz, J.M., Lopez-Moreno, J.I., Vicente-Serrano, S.M., Lasanta-Martinez, T., & Begueria, S. (2011). Mediterranean water resources in a global change scenario. Earth-Science Reviews, 105, 121-139. https://doi.org/10.1016/j.earscirev.2011.01.006
14. Ghorbani, B., Karbasi, A., & Farhamand, Z. (2000). Investigating the factors influencing the adoption of agricultural products insurance. Proceedings of the 3rd Iran Agricultural Economics Conference, Ferdowsi University of Mashhad.
15. Ghorbani, M., & Jafari, F. (2009). Can production inputs play the role of insurance in the wheat production process? Quarterly Journal of Agricultural Economics and Development, 17(68), 1-16.
16. Ghorbani, M., Kochaki, A., Kohansal, M., & Jafari, F. (2009). Application of risk profile in risk management of crops in North Khorasan province (case study of sugar beet). Agricultural Economics Quarterly, 31-48. (In Persian)
17. Ghorbani, M., & Radmehr, R. (2019). Applied microeconometrics (restricted dependent variables) using Stata. Mashhad. Publications of Ferdowsi University of Mashhad. (In Persian)
18. Goodwin, K.B. (1993). An empirical analysis of the demand for multiple peril crop insuranc American Journal Agricultural Economics, 75, 425-434. https://doi.org/10.2307/1242927
19. Hajibabaee, M., Azizi, F., & Zargari, K. (2012). Effect of drought stress on some morphological, physiological and agronomic traits in various foliage corn hybrids. American Eurasian Journal Agriculture Environment Science, 12(7), 890-896. (In Persian)‏
20. Hart, E.C., Hayes, J.D., & Babcock, A.B. (2006). Insuring eggs in baskets: Should the government Insure Individual risks? Canadian Journal of Agricultural Economics, 54, 121-137. https://doi.org/10.1111/j.1744-7976.2006.00041.x
21. Hennessy, A.D., Babcock, A.B., & Hayes, J.D. (2014). Budgetary and producer welfare effects of revenue insurance. American Journal Agricultural Economics, 79, 1024-1034. https://doi.org/10.2307/1244441
22. Huffman, E.W. (2014). Farm and off-farm work decisions: The role of human capital. The Review of Economics and Statistics, 62, 14-23. https://doi.org/10.2307/1924268
23. Innes, R. (2015). Crop insurance in a political economy: An alternative perspective on agricultural policy. American Journal Agricultural Economics, 85(2), 318-335. https://doi.org/10.1111/1467-8276.00122
24. Just, E.R. (2002). Risk research in agricultural economics: Opportunities and challenges for the next twenty-five years. Agricultural Systems, 75, 123-159. https://doi.org/10.1016/s0308-521x(02)00063-x
25. Karbasi, A., & Kambozia, N. (2003). Investigating factors affecting the demand for insurance of agricultural products in Sistan and Baluchistan province. Agricultural Economics and Development, 41(42). (In Persian)
26. Kalantari, Kh., & Chobchian, Sh. (2015). Choosing the most appropriate method to compensate for natural damage caused to the agricultural sector in Iran by AHP method. Quarterly Journal of Agricultural Economics and Development, 23(92), 169-191. (In Persian)
27. Karami, A.A., Zamani, G.H., & Keshavarz, M. (2008). Determinants of insurance of agricultural products. Quarterly Journal of Agricultural Economics and Development, 16(62), 53-81. (In Persian)
28. Laieghi, A., Ghasemi, P., & Babaei, (2012). Investigating the relative advantage of production and employment in the agricultural sector of the country's provinces. Review of Economic Issues and Policies, 11(12), 83-110. (In Persian)
29. Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge University press.
30. Marenya, P.P., & Barret, B.C. (2006). Household-level determinants of adoption of improved natural resources management practices among smallholder farmers in western Kenya. Food Policy, 32, 515-536. https://doi.org/10.1016/j.foodpol.2006.10.002
31. Mojavarian, S.M., & Amirnezhad, H. (2008). Investigating factors affecting insurance demand by rice farmers (case study: Sari city). Journal of Agriculture, 10(1), 162-151. (In Persian with English abstract)
32. Mohammadzadeh, H., Karbasi, A., & Kashefi, M. (2016). Applied comparison of logit, probit and Tobit in the investigation of factors affecting the acceptance of saffron insurance, case study: Qain city. 254-239. (In Persian with English abstract).
33. Osaki, M., & Batalha, M.O. (2014). Optimization model of agricultural production system in grain farms under risk, in Sorriso, Brazil. Agricultural Systems, 127, 178-188. https://doi.org/10.1016/j.agsy.2014.02.002
34. Rahmati, A., Kohansal, M., & Ghorbani, M. (2012). Estimating the amount of insurance premium and compensation for two new methods of functional and income insurance and comparing it with the current system (case study of Mashhad city). The 8^th Biennial Conference on Agricultural Economics of Iran, Shiraz University. (In Persian with English absrtact)
35. Rahmati, A., Kohansal, M., & Ghorbani, M.(2015). Investigating the willingness of wheat farmers of Mashhad city to participate in new plans for insurance of agricultural products. Agricultural Economics and Development, 23(91). (In Persian)
36. Rostami, F., Shabanalifami, H., Movahed Mohammadi, H., & Irvani, H. (2007). Factors affecting the acceptance of insurance, a case study of wheat farmers in Harsin, Kermanshah. Agricultural Economics and Development, 15(60), 1-22. (In Persian with English abstract).
37. Salem, B. (2011). Investigating the production and commercial trend of agriculture in Iran and selected countries according to the trade liberalization of the last decade. Review of Foreign Policy, 4(5), 39-60. )In Persian)
38. Sharfi, L., & Zarafshan, K. (2010). Assessing the economic and social vulnerability of farmers against drought (a case study of wheat farmers in Kermanshah). Rural Researches, 4, 129-154. )In Persian)
39. Sharma, S., & Schoengold, K. (2016). A Comparison of Stated and Revealed Risk Preferences using Safety-First, 333-2016-14586. https://doi.org/22004/ag.econ.236126
40. Sai, T., Yulian, W., & Xiaofeng, H. (2010). An empirical study of agricultural insurance evidence from china. Agriculture and Agricultural Science Procedia, 1, 62-66. https://doi.org/10.1016/j.aaspro.2010.09.008
41. .Smith, H.V., & Baquet, E.A. (1996). The demand for multiple peril crop insurance: Evidence from Montana wheat farms. American Journal Agricultural Economics, 78, 189-201. https://doi.org/10.2307/1243790
42. Summer, A.D. (1982). The Off-farm Labor supply of farmers. At New York University. American Journal of Agricultural Economics, 64(3), 499-509. https://doi.org/10.2307/1240642
43. TorKamani, J., & Ghorbani, M. (1999). Factors affecting the demand for insurance of agricultural products: a case study of farmers in Sari city. Iranian Journal of Agricultural Sciences, 30(2). (In Persian with English absrtact)
44. Varela-Ortega, C., Blanco-Gutierrez, I., Esteve, P., Bharwani, S., Fronzek, S., & Downing, T.E. (2016). How can irrigated agriculture adapt to climate change? Insights from the Guadiana Basin in Spain. Regional Environmental Change, 6(1), 59-70. https://doi.org/10.1007/s10113-014-0720-y
45. William, J.A.D., & Weijing, W. (2010). Government interventions in agricultural insurance. Agriculture and Agricultural Science Procedia, 1, 4-12. https://doi.org/10.1016/j.aaspro.2010.09.002
46. Yaghoubi, A., Chizari, M., & Feeli, S. (2007). Insurance of agricultural products: a suitable solution in risk management. 6^th Iranian Agricultural Economics Conference, Mashhad, Iranian Agricultural Economics Association, Ferdowsi University of Mashhad. (In Persian)
47. Yazdani, S., & Sasoli, M.R. (2008). Investigating the effect of input consumption on the risk of rice production in Shaft city, Gilan province. Agricultural Economics Quarterly, 2(1), 35-46. (In Persian)
48. Zhang, Y., Ju, G., & Zhan, J. (2019). Farmers using insurance and cooperatives to manage agricultural risks: A case study of the swine industry in china. Journal of Integrative Agriculture, 18(12), 2910-2918. https://doi.org/10.1016/S2095-3119(19)62823-6