Agriculture Insurance as a Risk Management Strategy in Climate Change Scenario: A study in Islamic Republic of Iran

International Journal of Agriculture and Crop Sciences. Available online at www.ijagcs.com IJACS/2012/4-13/831-838 ISSN 2227-670X 2012 IJACS Journal Agriculture Insurance as a Risk Management Strategy in Climate Change Scenario: A study in Islamic Republic of Iran Kiyanoush Ghalavand 1, Karim.MH (Karim Koshteh) 2 * and Abolhassan Hashemi 3 1. Ph.D. Research scholar in Economics, Department of Gandhian Studies Panjab University. Chandigarh, India. 7 2.* Associate Professor, University of Economics Sciences, Tehran. 3. Faculty Member, College of Economics, Sistan and Baluchistan University, Iran. Corresponding author email: karimsistani482@yahoo.cam ABSTRACT: Agriculture is central to the livelihoods of the rural poor and in the attainment of the Millennium Development Goals (MDGs). Agriculture has always been a risky business. In recent years, natural disasters, particularly climate-related ones, have increased both in frequency and magnitude. Scientists the world over have agreed (IPCC AR4, 2007) that human-induced climate change is exacerbating this impact. Agriculture sector is likely to be affected most due to extreme weather events like cyclone, flood or drought. So, the farmers are hit hardest. For Drought plain countries like Iran, structural measures for management of disaster risk and its consequences often were found less effective. So non-structural measures like micro-insurance or crop insurance are being suggested as a risk management strategy. UN Climate Convention and the Kyoto Protocol have included the provision of insurance as a mechanism to address the risks from climate change. The main objective of the study was to develop a realistic framework and concrete roadmap for introducing crop insurance as a risk management strategy for the farmers in Iran. The study is based on both secondary and primary data and information. Survey was the research method, and data was collected by questionnaire and different instruments, such as survey questionnaire, FGDs, interview schedule, inception workshop and roundtable discussions with stakeholders at different levels. The three survey districts were: Golestan Province (as a flashflood area, north of Iran), Khuzestan Province (as a drought area, south of Iran) and Khuzestan Province (as a cyclone and flood-prone area, south of the country). The results revealed that four independent variables explain adoption of Drought insurance. Consult with other farmers is the main independent variable. Keywords: Agriculture Insurance, Risk management, Strategy, Climate Change, Iran INTRODUCTION Agriculture is central to the livelihoods of the rural poor and in the attainment of the Millennium Development Goals (MDGs). For present purposes, climate change is defined as a process of global warming, in part attributable to the greenhouse gases generated by human activity. Accompanying changes are likely to be both global, as with rising sea levels attributable to ice-melt, and local, such as changes in rainfall patterns. Responses to climate change can either seek to reduce the level or rate of change (mitigation) or manage its consequences (adaptation). We are concerned here with both types of response. Natural disasters hit hard. They may cause heavy losses to farmers and forest owners. Insurance can assist in managing these losses, and disaster insurance is that branch of this financial mechanism that is especially geared to covering losses from

adverse weather and similar events beyond the control of growers. Agriculture has always been a risky business. In recent years, natural disasters, particularly climate-related ones, have increased both in frequency and magnitude. Scientists the world over have agreed that human-induced climate change is exacerbating this impact. Agriculture sector is likely to be affected most due to extreme weather events like cyclone, flood or drought. So, the farmers are hit hardest. For Drought plain countries like Iran, structural measures for management of disaster risk and its consequences often were found less effective. So non-structural measures like micro-insurance or crop insurance are being suggested as a risk management strategy. The rationale is that poverty and vulnerability to climate change feed each other, and this nexus warrants that climate change policies work in concert with poverty reduction policies. However, traditional micro-credits and savings are inadequate when poor farmers with no safety or security nets are exposed to risks beyond their means to cope with. Agriculture production and farm incomes are frequently affected by natural disasters such as droughts, floods, cyclones, storms, landslides and earthquakes. Susceptibility of agriculture to these disasters is compounded by the outbreak of epidemics and man-made disasters such as fire, sale of spurious seeds, fertilizers and pesticides, price crashes etc. All these events severely affect farmers through loss in production and farm income, and they are beyond the control of the farmers. With the growing commercialization of agriculture, the magnitude of loss due to unfavorable eventualities is increasing. (Raju&Chand, 2008).Drought is one of the main natural disasters in the Islamic republic of Iran, and has seriously affected people's daily lives, socioeconomic activities and environment, particularly in the poorer areas. Drought occurs annually in many parts of the Islamic republic of Iran. In 2008, the country suffered its worst drought in 50 years, causing water shortages to at least 20 million people and an economic loss of about $1.24.The worst-hit area was the southwestern region including Khuzestan and 15 provinces, where about 35 million people (50% of the region's population)experienced serious water shortages. Currently in the Islamic republic of Iran, policies and regulations to mitigate droughts have major shortcomings. These include (i) weak risk management strategies; (ii) inadequate long-term strategic plans; (iii) inadequate attention to private sector and NGOs' participation; (iv) ineffective interagency coordination due to unclear roles and responsibilities of different agencies; (v) ineffective information gathering and sharing mechanism; (vi) inadequate governments' support for drought management; (vii) lack of commercial insurance to mitigate drought risk; and (viii) inadequate attention to policies for the protection of ecosystems and environment. In addition, as drought impacts have become more severe, traditional approach for the construction of water conservation structures and irrigation system is no longer practical and economically feasible. Thus, structural measures need to be combined with nonstructural measures such as drought monitoring, forecasting and warning, drought insurance, and water savings technologies. However, nonstructural drought management measures have yet to be fully developed. Furthermore, the Islamic republic of Iran has yet to initiate systematic and comprehensive studies on drought management. The international lessons and experience will be very useful for strengthening structural and nonstructural drought mitigation measures for a long-term national drought management strategy in the Islamic republic of Iran, One of the key findings of systematic and comprehensive drought management studies conducted in the United States clearly revealed that the impacts of droughts can be alleviated through good management. As a result, the United States has shifted from crisis management to risk management to minimize drought impacts by establishing drought preparedness plans, developing policies and regulations for mitigation, and advance forecasting. (EM-DAT, 2008).Agricultural activities are carried with different risks such as natural disasters. Crop insurance is one of the most mechanisms to reduce financial damage in agriculture. It is a new idea and innovation for rural areas especially in third world. Then, different factors influence adoption of crop insurance. Drought is a particularly troublesome hazard that has a documented adverse impact on agricultural Development. A long history of decision support tools have been developed to try help farmers of policy makers manage risk. The steps, ideas and philosophy of adoption process have been explained in different studies (Rogers, 1995). These studies view the innovation as a key issue related to technology changes and these studies have been focused on adoption rate and its level (Rogers, 1995). Various studies have explained the adoption process in the form of systematic models. The diffusion model was the most widely used pattern for adoption of innovations (Rogers, 1983). Based on this model, innovative farmers adopt the new ideas and these ideas transform to another farmers in time. The focus of this model is in relationship between awareness and adoption. Awareness is perceived as an essential condition for adoption of innovations in the diffusion model (Hooks et al., 1983). Another necessary condition for adoption is a favorable attitude toward innovation. The model says that knowledge gained through access to different information sources is posited to be an important determinant of adoption behavior (Rogers, 1995). Also, the diffusion model asserts that adopters' characteristics are important determinants of adoption process. It is hypothesized that farmers' education, age and farming experiences is related to adoption. Bhende (2005) found that income of the farm households from semi-arid tropics engaged predominantly in rain-fed farming was positively associated with the level of risk. Hence, the availability of formal instrument for

diffusion of risk like crop insurance will facilitate farmers to adopt risky but remunerative technology and farm activities, resulting in increased income. Some of the studies confirm the conventional view that moral hazard incentive lead insured farmers to use fewer chemical inputs (Smith and Goodwin 1996). Babcock and Hennessy (1996), find that at reasonable levels of risk aversion, nitrogen fertilizer and insurance are substitutes, suggesting that those who purchase insurance are likely to decrease nitrogen fertilizer applications. A study by Horowitz and Lichtenberg (1993) find that in the US Midwest, crop insurance exerts considerable influence on maize farmers' chemical use decisions. Those purchasing insurance applies significantly more nitrogen per acre (19 %), spend more on pesticides (21 %), and treats more acreage with both herbicides and insecticides (7 % and 63 %) than those not purchasing insurance. These results suggest that both fertilizer and pesticides may be risk-increasing inputs. An analysis of data from US agriculture indicates that the producer's first response to risk is to restrict the use of debt. Price support programmes and crop insurance are substitutes in reducing producer risk. The availability of crop insurance in a setting with price supports allows producers to service higher levels of debt with no increase in risk (Atwood et al., 1996). Mishra (1994) analyzed the impact of a credit-linked Comprehensive Crop Insurance Scheme (CCIS) on crop loans, especially to small farmers in Gujarat. It is observed that CCIS had a collateral effect as reflected through the increased loan amount per borrower and reduction in the proportion of non-borrowers among small farmers. The implications of credit expansion are that increased availability of credit can enhance input use and output and employment that increased share of small farmers in the total loan can have desirable effects on equity and efficiency considerations. Ghalavand, (2005) said that individual characteristics of farmers, their education and income from the farm are related to adoption of crop insurance. The diffusion model was criticized (Rogers, 1983), so that the farm structure model (economic constraint model) was offered. This model emphasizes access to resources as predictive factors of adoption (Napier et al., 1984). Also, the farm structure model emphasizes profitability and economic motives for adoption of innovations. Based on this model, the socio-economic status of farmers is related to adoption behavior. Another studies provided an alternative model to identify adoption process. The multiplicity model combines the diffusion and farm structure models to explain adoption process (Nowak, 1987). There are different studies regarding to adoption of crop insurance and its determinants. Mishra (1999) revealed that increasing the value of insurance, identifying the target farmers, to provide financial resources and suitable communication with farmers are the major determinants of adoption of crop insurance. Vandenveer (2001) said that individual characteristics of farmers, their education and income from the farm are related to adoption of crop Drought insurance. A number of studies showed that there is positive and meaningful correlation between amount of land and the value of farm with demand for insurance (Goodwein, 1993). Smith and Baquet (1996) concluded that the adoption of crop insurance is influenced by variables such as farmers' education, their risk, and the variation in productivity and the value of insurance. Baker (1990) emphasized the importance of the farmers' knowledge. He argued that the more knowledge of farmers regarding crop insurance will result in increasing demand to crop drought insurance in rural areas. Darijani and Ghorbani (1998) revealed that the adoption of insurance among farmers related to amount of loans, amount of farm, kind of agricultural activity and level of risk. The study of Rayatpanah (1995) showed that access to communication channels, extension and education methods, and the rate of employment in family influence on adoption of crop insurance. Naimi-Nezamabadi (1998) indicated that attitude and knowledge regarding the insurance process, and its advantages were the major factors to adopt crop insurance. Investigation factors affecting farmers' adoption of crop insurance was the objective of this study. METHODOLOGY Wheat producer farmers who produce irrigated wheat crop in Khuzestan and Gorgan provinces were the statistical population of this study. Khuzestan is one of the big provinces which is in south-west of Iran. Most of the rivers in Iran are flow in Khuzestan province and it has warm and humid weather. Gorgan Province is one of the beautiful provinces which is in North of Iran. Statistical population (wheat producer farmers) of northern Khuzestan were 1830 and statistical population of Gorgan were 3172 (Totally 5002 wheat producers). Proportional stratified random sampling was used to determine sample population. Each geographical division (Shahrestan) is assumed

as stratify. Then, based on proportion of wheat producers' population in each Shahrestan, the sample size was determined according to Kucran sampling formula. Table 1 shows details of sampling process. Table 1. Statistical population and sample size according to proportional stratified random sampling. Khuzestan Golestan Shahrestan Statistical Statistical Sample size Shahrestan population population Sample size Iezeh 182 14 Gonbade Kavoos 549 42 Shush 600 45 Gorgan 523 40 Shushtar 210 16 Bandar e Torkaman 626 48 Dezful 320 24 Kordkooy 780 59 Andimeshke 312 24 Azad Shahr 282 21 Masjed soleyman 206 16 Agh Ghala 412 31 Total 1830 139 Total 3172 241 Survey was used as research method. A questionnaire was prepared for data collection. Its validity was examined by face validity. A pilot study was operated. Chronbach Alpha test was executed in order to examine the questionnaire internal consistency and its reliability. The alpha parameter was equated 0.92. The data were collected by facial interview with the farmers. Findings In relation to sample group demographic characteristics, the education level of sample has been illustrated in Table 2. The distribution of education condition of wheat producers is approximately normal and seems appropriate. About 24, 24.5, and 20 percent of respondents have benefited from education in primary, secondary, and high school levels, respectively. Therefore, almost all of wheat producers have been benefited of literacy in desirable levels. Table 2. Education level of respondents Education level Frequency Percent Literacy 68 18.5 Primary school 88 23.9 Secondary school 90 24.5 High school 72 19.6 Diploma and higher education 50 13.5 Without any response 12 - Total 380 100 Table 3. Correlation coefficients between independent variables and farmers' acceptance of crop insurance Variables r Age -0.265 ** Level of education 0.508 ** farmer's wheat crop area (Hectare) 0.213 ** Background of wheat cultivation (Year) -0.106 Income 0.219 ** Farmers awareness of goals and advantages of crop insurance 0.598 ** Consultation with other farmers 0.373 ** Participation in training classes and sessions 0.888 ** Amount of contact with insurance agents 0.626 ** Participation in extension lectures 0.857 ** Watching films and video clips related to crop insurance -0.012 Study of extension bulletins and journals related to crop insurance 0.079 Visiting of crop insurance company's activities 0.855 ** Participation in crop insurance workshops -0.011 Contact with agricultural extension agents 0.678 ** * Significant at 0.05 level of probability ** Significant at 0.01 level of probability Spearman correlation coefficient was executed to determining correlation rate between independent variables and farmers' acceptance of crop insurance (Table 3). Age had a significant negative correlation with insurance acceptance. It means insurance acceptance rate is higher between younger farmers. If we assume crop insurance as an innovation, it is natural that older farmers to be lateness. Whereas, farmers with higher education

better understand the crop insurance advantages, their adoption increases with increasing education level, significantly. Background of wheat cultivation variable had no correlation with Drought insurance adoption. But the adoption rate increased with increasing wheat crop area, and farmers income enhancement, significantly. These findings are justified with the reason that threat of unexpected factors is increased by increasing crop area and farmers expect to alleviate these threats by crop insurance. Besides, farmers with higher income have less difficulty to pay crop insurance charge. Farmers' awareness of goals and advantages of drought insurance; consult with other farmers; participation in training classes and sessions toward necessity of insurance; amount of contact with insurance agents; participation in crop insurance workshops; and amount of contact with agricultural extension agent were significant correlation with crop insurance adoption variable. Multiple regression analysis according to stepwise method was executed to determine independent variables' effect on Drought insurance acceptance changes, as dependent variable, simultaneously (Table 4). The variable of consult with other farmers could explain 81 percent of dependent variable changes (R 2 = 0.81). It shows the importance of farmers' negotiation with each other and their communication on decision making toward Drought crop insurance. Whereas, most of farmers in research area had similar condition, they believed that the experience of each farmer can be generalized to the others. Considering that the triabilitys of innovation is one of the stages of the adoption of innovations, when one farmer examines crop insurance, his experience is a criterion for other farmers to decision making. Amount of contact with insurance agents explained 15 percent of dependent variable changes (Table 4). It reveals the important role of insurance agents towards farmers' Adoption. Then, insurance agents are the second information source of farmers after the other farmers in relation to decision towards accept or reject crop insurance. These two independent variables explained 0.96 percent of dependent variable changes. Farmers' awareness rate of goals and advantages of Drought insurance could explain 2.3 percent of dependent variable changes (Table 4). Awareness especially about the advantages of an innovation is the first stage in adoption of innovations. Table 4. Results of multiple regression analysis according to stepwise method to determining independent variables' influence on Drought crop insurance Adoption Independent variables B S.E.B Beta adjust change T Sig. Consult with other farmers 2.41 0.126 0.579 0.81 0.81 19.15 0.000 Amount of contact with insurance agents 1.01 0.102 0.363 0.96 0.15 9.95 0.000 Farmers awareness of goals and advantages of Drought crop insurance 1.30 0.207 0.189 0.983 0.023 6.30 0.000 Each farmer's wheat crop area -0.133 0.044-0.71 0.987 0.004-3.017 0.006 Dependent variable: Drought Crop insurance adoption F= 533.36 Sig: 0.000 Constant= 5.50 Finally, amount of land which each farmer allocated to wheat cultivation was the fourth and last independent variable which has been interred in regression equation according to stepwise method (R 2 =0.004). These four variables explained 98.7 percent of dependent variable changes. The multiple regression equation has been written in below: Y=2.41 X 1 +1.01 X 2 +1.3 X 3-0.133 X 4 +5.5 Factor analysis to understand factors network related to adoption of wheat insurance According to factor analysis, Kaiser-Meger-Olkin (KMO) parameter and its Bartlet equated 0.74 and 9724.39, respectively. These were significant at 0.99 level. It shows the correction of the factors entered for factor analysis. Kaiser method and percent of variance have been executed to determine number of factors. Only those factors have been selected that their Eigenvalues based on Kaiser Method have been higher than 1. Finally, nine factors extracted and they could explain 75.03 percent of total variance. These factors were illustrated in table 5. Variables' situation after factors rotation according to Verimax method and factors nominating have been illustrated in table 6. It should be pointed that 24 variables after Verimax rotation because of low factor loading (less than 1) and non significance of their correlation with other factors, were eliminated of analysis process. The reason for this elimination is that the common level of the variables was overlap with more important variables, before. Therefore, these variables could be integrated with the other variables. R 2 R 2

Table 5. Extracted factors with their specification, based on factor analysis. Factors Special Amount (Eigen value) Variance Percent of S.A. Cumulative Frequency of Variance Percent First 7.48 22.67 22.67 Second 4.18 12.67 33.35 Third 2.74 8.31 43.67 Fourth 2.45 7.45 51.12 Fifth 2.27 6.90 58.02 Sixth 1.69 5.12 63.15 Seventh 1.53 4.65 67.8 Eighth 1.22 3.71 71.52 Ninth 1.15 3.51 75.03 Table 6. Variables of each factors and the coefficients which have been extracted of rotated matrix. Factors Variables Coefficient Execute of training classes towards crop insurance advantages 0.857 Distribution of training bulletins and leaflets 0.707 Execute of workshops 0.875 Contact with agricultural extension agents 0.779 Distribution of newspaper towards crop insurance affairs 0.699 Extension-education factor Use of radio for farmers' enlightenment towards crop insurance 0.843 advantage Use of TV for farmers' enlightenment toward crop insurance advantage 0.834 Use of propagator films towards crop insurance 0.573 Area of wheat cultivation 0.902 Land revenue system 0.768 Economic Factor Farmers' income 0.803 Primary insurance contract payment 0.735 Discount toward agricultural crops insurance 0.859 Awareness towards crop insurance advantages 0.767 Communication Channels Contact of farmers with crop insurance agents 0.897 Factor Information deliver towards crop insurance to farmers 0.819 On time indemnity payment to indemnity farmers 0.522 Motivate Factor Discount considering for farmers who were without crop damage 0.671 Give present to farmers by insurance companies 0.603 Arrange group discussion toward crop insurance advantages 0.742 Beneficiary of local leaders role towards crop insurance Opinion Leadership Factor encouragement 0.877 Beneficiary of local council role towards crop insurance encouragement 0.515 Facility toward indemnity payment 0.877 Facility Factor Discount consideration for farmers who were without crop damage 0.532 Insurance companies performance towards their commitments 0.712 To facilitate official process for contracting of crop insurance 0.591 Confidential Factor Satisfaction of insurer farmers of their crop insurance 0.940 Persuasion of farmers to insure their crops by insurance agents 0.943 Supervision Factor On time indemnity payment to indemnity farmers 0.613 Continuous control of insurance process correctness by inspectors 0.569 Diversity Factor Diversification of Drought insurance options 0.825 Regarding to the results of factor analysis in table 6, the factors affecting on crop Drought insurance acceptance have been classified in nine factors. They are: 1)Extension-education; 2)Economic; 3)Communication channels; 4)Motivate; 5)Opinion leadership; 6)Facility; 7)Confidential; 8)Supervision; and 9)Diversity factors. They could explain 75.03 percent of total variance, as mentioned before. Extension-education factor with special amount (Eigenvalue) which is equated 7.48 could explain 22.67 percent of total variance. This factor is the most important factor in compare to the others. It includes the variables such as execute of training classes, bulletins, leaflets, workshops, newspapers, radio and TV programs, and so on in order to persuade farmers to taking action towards crop insurance. Economic factor has been the second factor that could explain 12.67 percent of total variance with special amount that equaled 4.18. It includes variables such as area of wheat cultivation by each farmer, land revenue system, income, insurance contract payment, and discount towards agricultural crops insurance (Table 6). Third factor was communication channels. This factor with special amount equal 2.74 could explain 8.31 percent of total variance. It contains variables awareness of crop insurance advantages, contact with insurance agents, and deliver information towards insurance to farmers. Motivate factor was considered as fourth factor. This factor with 2.45

special amounts could explain 7.45 percent of total variance, consists of three variables on time indemnity payment, discount considering for farmers who were without crop damage, and give present to farmers from insurance companies. Opinion leadership has been the fifth factor. Its special amount was equal 2.27 and explained 6.9 percent of total variance. Arrange group discussion towards crop insurance advantages, beneficiary of local leaders and local council role towards crop insurance encouragement were the variables in this factor. The sixth factor was facility could explain 5.12 percent of total variance with 1.69 of special amount. It consists of four variables i.e. facility toward indemnity payment; discount consideration for farmers who were without crop damage; insurance companies performance towards their commitments; and make facility in official process for contracting of crop insurance. Confidential factor was the seventh factor with special amount equated 1.53 that could explain 4.65 percent of total variance. It has two variables: satisfaction of insurer farmers of their crop insurance; and persuasion of farmers to insure their crops by insurance agents. Supervision factor with special amount equated 1.22 was the eighth factor that explained 3.71 percent of total variance. This factor consisted of two variables: on time indemnity payment to indemnity farmers; and continuous control of insurance process correctness by inspectors. Diversity was the ninth and the last factor with special amount equated 1.15. It explains 3.51 percent of total variance and contains one variable which is diversification of crop insurance options. CONCLUSION AND RECOMMENDATIONS In recent years, the argument that damage caused by natural disasters cannot be prevented but only mitigated has become popular among scientists and engineers engaged in research on natural disasters. This argument implies the following two factors that have been noted from the characteristics of recent natural disasters. The insurance and disaster management industries are closely related -- both deal with the risk of natural disaster and managing the events following disasters. Climate change, although a natural phenomenon is accelerated by human activities. Disaster policy response to climate change is dependent on a number of factors, such as readiness to accept the reality of climate change, institutions and capacity, as well as willingness to embed climate change risk assessment and management in development strategies. These conditions do not yet exist universally. A focus that neglects to enhance capacity-building and resilience as a prerequisite for managing climate change risks will, in all likelihood, do little to reduce vulnerability to those risks. Reducing vulnerability is a key aspect of reducing climate change risk. To do so requires a new approach to climate change risk and a change in institutional structures and relationships. A focus on development that neglects to enhance governance and resilience as a prerequisite for managing climate change risks will, in all likelihood, do little to reduce vulnerability to those risks. The purpose of this paper was exploring the potential role of Index-based Agriculture Insurance as a tool for climate change adaptation and social protection in Iran. The paper will first provide an overview of recently piloted micro policies and macro policies. Agriculture is inherently risky. Production risks include, but are not limited to, climatic hazard which of all the hazards agriculture faces is perhaps the most difficult one for the agriculturalist to manage. Drought is the most globally serious of the natural hazards in terms of loss of life accounting for 44% of reported deaths worldwide in the period between 1974-2003.Thus Agriculture is a risky occupation. Natural disasters are the most threats in agricultural activities. Almost 31 of 40 types of natural disasters which have been distinguished in the world occur in Iran. Therefore, Iran has stood on tenth rank in relation to natural disasters in the world. Insurance is one of the usual strategies to alleviate threats in agricultural production. There are many factors out of farmers control and unpredictable. Accordingly, insurance has an important status in agricultural production. Encouragement of farmers to insure their crops by extension agents could be an appropriate strategy to alleviate agricultural risks. Investigation factors related to adoption of Drought insurance by wheat farmers was the main objective of this study. Findings revealed that the farmers with higher rate of Drought crop insurance Adoption were younger with higher level of literacy, they had more crop area and more income, they had more awareness towards the goals and advantages of crop insurance, they often consult with other farmers and they have more participation in training classes and sessions. Also, rate of their contact with agricultural extension and insurance agents was higher, they more participated in extension lectures and more visited crop insurance company's activities. Multiple regression analysis revealed that four independent variables could explain about 99 percent of farmers Drought crop insurance Adoption changes. The variables consult with other farmers, amount of contact with insurance agents, farmers awareness of goals and advantages of crop insurance, and each farmer s wheat crop area were interred in regression equation. The variables affecting Drought crop insurance Adoption (31 variables) were classified to nine factors according to factor analysis technique. These nine factors were more general. This classification helps authors to achieve higher theoretical level in relation to the factors which influence on crop insurance acceptance. Consequently, extension-education

factor, economic factor, communication channels factor, opinion leadership factor, facility factor, confidential factor, supervision factor, and diversity factor influence on crop insurance acceptance. They could explain about 75 percent of total variance. Based on the findings, some recommendations are presented in the following. In order to accelerate Drought crop insurance adoption process, identifying the first adopters between farmers is very important. According to this research finding, those farmers are younger with higher level of education, higher wheat crop area, more income and better communication with other farmers, insurance agents and agricultural extension workers. They could affect decision making of the other farmers, because, based on the findings farmers consultant with each other had very important role on their decision making towards crop Drought insurance adoption. Simultaneously, strength of extension educational programs towards crop insurance has a great effect on farmers' acceptance regarding to the research findings. Insurance agents can facilitate the farmers' acceptance process by use of some manners such as present motivate factors, facility factors, confidential factors, supervision factors and diversity factors. REFERENCE Babcock BA, Hennessy DA. 1996. Input Demand under yield and Revenue Insurance. American Journal of Agricultural Economics. 78(1): 212-24. Bhende MJ. 2005. Agricultural Insurance in India: Problems and Prospects. Department of Economic Analysis and Research, National Bank for Agriculture and Rural Development Occasional paper 44. EMD-DAT.The OFDA / CRED, 2008, International Data base UCL-Brussels, Belgium, A report annual. Ghalavand K, Hayati D, Rzaei moghaddam K.2007. Factors effective in crop insurance in Khuzestan province Iran, International Conference APEAEN,Centeral state luzhan state university manila Philippine. Ghalavand K.2005. Investigation factors effective on crop insurance adoption among farmers Tehran and Mazandaran provinces Iran,M.sc thesis Islamic azad university science and research branch,tehran Iran. Hooks GM, Napier TL, Garter MV. 1983. Correlates of adoption behaviors: The case of farm technologies. Rural Sociology, 48, 308-323. Horowitz JK, Lichtenberg E. 1993. Insurance, Moral Hazard, and Chemical Use in Agriculture. American Journal of Agricultural Economics. 75(4): 926-35. Atwood JA,Watts MJ Baquet AE. 1996. An Examination of the Effects of Price Supports and the Federal Crop Insurance upon the Economic Growth, Capital Structure and Financial Survival of Wheat Growers in the Northern High Plains. American Journal of Agricultural Economics. 78(1): 212-24. Mishra PK. 1994. Crop Insurance and Crop Credit: Impact of the Comprehensive Crop Insurance Scheme on Cooperative Credit in Gujarat. Journal of International Development. 6(5): 529-68. Napier TL, Thraen AG, Goe WR. 1984. Factors affecting adoption of conventional and conservation tillage practices in Ohio. Journal of Soil and Water Conservation, 39, 205-209. NCAP Working Paper No. 8, March 2008. National Centre for Agricultural Economics and Policy Research (Indian Council of Agricultural Research). Nowark PJ. 1987. The adoption of agricultural Conservation technologies: Economic and diffusion explanation. Rural Sociology, 52, 208-220. Raju S S, Chand R.2008.Agriculture crop insurance in India: Problems and Prospects. Rogers EM. 1983. Diffusion of Innovations, (third edition). New York: The Free press. Rogers EM. 1995. Diffusion of Innovations, (fourth edition). New York: The Free press. Smith VH, Goodwin BK. 1996. Crop Insurance, Moral Hazard, and Agricultural Chemical Use. American Journal of Agricultural Economics. 28(2): 428-438.