An analysis on Problems in implementing NAIS (National Agriculture Insurance Scheme) with special reference to Erode Block

 

Ms. Mohanapriya T.¹, Dr.V.M.Senthilkumar²

¹Research Scholar, Bharathiar University, Coimbatore

²Professor in Humanities, Sri Shanmugha College of Engineering and Technology, Sankari, Salem,

 

ABSTRACT:

Agriculture is considered to be the backbone of the India Economy. In developing countries like India, agriculture has been crucial sector due to its perceived ability to contribute significantly to achieve the developmental objectives. Agriculture in India is characterized by low productivity, lack of technology, under employment, etc., Normally agriculture production implies an unexpected outcome or yield.   Variability in outcomes from expected those which are expected poses risks (Harwood et al 1999). Generally the farmers produce the agriculture products under the condition of risky environment. The decisions taken by the farmers are not know with certainty and the results may be better or worse than the expectation. Crop insurance is the best solution to compensate the loss of farmers arising from the field. The ongoing National Agriculture Insurance Scheme (NAIS) serves as a good step to insure the risks of millions of farmers.  However, crop insurance inherent major problems as only about 10% of sown area and suffers from adverse claims to premium. There are problems with both the design and delivery of crop insurance schemes. So the present paper focus on identifying the problems that exists in implementing NAIS in three firka from Erode blocks viz., Erode East, Erode North and Erode West. The study is conducted by interviewing 300 farmers in selected blocks using a prepared questionnaire. The aim is to know the awareness of NAIS among farmers and the issues in executing the agriculture insurance scheme.  The data collected through the primary method is analyzed with the help of tools like percentage analysis, ANOVA, Chi-Square, correlation and Garrets Ranking Technique. The main problems that the farmer is facing in execution of the NAIS are delay in payment of claim amount, crop coverage and premium rate. It is also revealed that there is significant difference between the variables like educational levels of farmers with respect to availing of NAIS, borrowing amount and availing NAIS, borrowing amount and experience of farmer’s in farming and borrowing amount and farmer’s category.

 

 

INTRODUCTION:

In geographical level, India is the 7^th largest country, in population it is the 2^nd largest country and 12^th largest country in economic wise. The major occupation in the country was agriculture. Nearly 70% of the population are having their livehood in agriculture and engaged in this occupation directly or indirectly.

 

The father of India nation Mahatma Gandhi had quoted that "Indian economy lives in rural villages", and many of the industries get their raw material from agriculture sector. India economy mostly depends on agriculture so there are enormous opportunities in agricultural/rural insurance in India.

 

STATEMENT OF THE PROBLEM:

The agricultural sector in India highly depends on monsoon. The farmers are exposed to heavy due to uneven distribution of rainfall. The erratic and uneven distribution of monsoon rains perpetuate yield/price volatility and hence farmers exposure to risk and uncertainty. In this scenario of high risk and uncertainty of rain fed agriculture, allocating risk is an important aspect of decision making to farmers. This indicates a need for contingent plans that will help to improve the handling of risky outcomes across individuals. The design and implementation of contingent contracts is thus an integral part of development process in Indian agricultural sector.

 

OBJECTIVES OF THE STUDY:

·         To study the socio-economic background of the farmers’ who had insured their crop.

·         To identify the awareness level of crop insurance among the farmers’.

·         To point out the reasons for not availing crop insurance by the farmers’.

·         To identify the problems faced by the farmers’ while availing the crop insurance.

·         To provide suggestions based on the findings.

 

REVIEW OF LITERATURE:

Agricultural production implies an expected outcome or yield. Variability in outcomes from expected those which are expected poses risks (Harwood et al 1999).

The topic of crop insurance has been widely studied in the domain of agricultural insurance by the academicians. Studies in US for crop insurance are widespread. These studies have focused on several issues particularly the failure of crop insurance programs to perform as expected. Several authors have suggested that this failure is primarily due to problems of moral hazard, adverse selection, and systemic risks (Weaver and Kim, 2002; Chen, 2005; Quiggin et al, 1986, Roberts et al, 2006).

 

An agricultural insurance scheme is difficult and complex to execute. Even the private agricultural insurance has not been successful due to failures on the parts of market and government because of the several reasons (Mark Wenner and Diego Arias, 2003).

 

The farmer is likely to allocate resources in profit maximizing way if he is sure that he will be compensated when his income is catastrophically low for reasons beyond his control. A farmer may grow more profitable crops even though they are risky. Similarly, farmer may adopt improved but uncertain technology when he is assured of compensation in case of failure (Hazell 1992).

 

 

 

 

DATA ANALYSIS:

Socio- Economic Background of the farmer’s:

Table 1: Socio economic Background of the Farmers’

Socio economic Background	Particulars	No of farmers’		Total
		Insured farmers(NAIS)	Non insured farmers(NAIS)
Age	Below 25 Years	29(19.86)	26(16.88)	55
	25-30 Years	54(36.99)	61(39.61)	115
	31-35 Years	47(32.19)	43(27.92)	90
	Above 35 Years	16(10.96)	24(15.58)	40
Education	Below School Level	22(15.07)	39(25.320	61
	School Level	49(33.56)	66(42.86)	115
	UG	48(32.88)	38(24.68)	86
	PG	27(18.49)	11(7.14)	38
Nature of family	Joint Family	59(40.41)	54(35.06)	113
	Nuclear Family	87(59.59)	100(64.94)	187
Farmer Category	Small Farmer(< 2.5 Acres of land)	37(25.34)	45(29.22)	82
	Medium Farmer (< 5 Hectares of land)	74(50.68)	75(48.70)	149
	Large Farmer (> 5 Hectares of land)	35(23.97)	34(22.08)	69
Nature of land holding	Own Land	73(50)	69(44.81)	142
	Leased Land	73(50)	85(55.19)	158
Years of Experience in Farming	0-5 Years	32(21.92)	41(26.62)	73
	6-10 Years	56(38.36)	59(38.31)	115
	11-15 Years	39(26.71)	4(27.27)	81
	Above 15 Years	19(13.01)	12(7.79)	31

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

 

The Table 1 depicts the socio-economic factors of the farmers’. It shows that out of  300 farmers interviewed, 115 farmers belongs to age category of  25-30 years of age, 90 farmers’ from 31-35 years of age, 55 farmers’ from age category of below 25 years and 40 farmers are from above 35 years.

 

The Table 1 points out that, 115 farmers had completed their school level, 86 farmers completed UG degree, 61 farmers belongs to category of below school level and 38 farmers had completed PG.

 

From the Table 1 it is obvious that 187 farmers are living in nuclear family and 113 farmers is living in joint family. It is stated from the Table 1, 149 farmers belongs to medium sized farmers, 82 farmers to small level farmers and 69 farmers are large sized farmers. 158 farmers are cultivating the crops on leased land and 142 farmers are cultivating on their own land.

 

It is found from the Table 1 that 115 farmers are having 6-10 years of farming experience, 81 farmers are having 11-15 years of experience, 73 farmers are having 0-5 years of experience and only 31 farmers are having above 15 years of experience in farming.

 

Farmer’s Borrowing Details

Table 2: Borrowing Details

Particulars	No of farmers’
	Insured farmers (NAIS)	Non insured farmers (NAIS)
Yes	82(56.17)	54 (35.06)
No	64(43.83)	100 (64.94)
Total	146	154

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

It is shown from the Table 2 that 154 farmers are aware about the agriculture insurance  and 146 farmers are not aware about the insurance.

 

Table 3: Amount Borrowed

Amount (Rs)	No of farmers’
	Insured farmers (NAIS)	Non insured farmers (NAIS)
Below Rs. 50000	23(28.05)	12(22.22)
Rs.50000-100000	25(30.49)	19(35.19)
Rs.100001-150000	21(25.61)	12(22.22)
Above Rs.150000	13(15.85)	11(20.37)
Total	82	54

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

From the above Table 3, it is depicted that 82 farmers have insured their crop and 54 have not yet insured their crop under National Agriculture Insurance Scheme.

 

Table 4: Source of loan

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

It is found from the above Table 4 that most of the farmers had got loan from bank for agriculture purpose.

 

 

Table 5: Source of fund for repayment of loan

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

From the Table 5, it is clear that mainly the farmers are repaying their loan by getting another loan.

 

Crop insurance:

Table 6: Motivation to avail the Crop Insurance

Particulars	Frequency	Percentage
Banks / Financial Institutions	57	39.04
Heard of good experience from other farmers	30	20.55
Financial Security	59	40.41
Total	146	100

(Source: Primary Data). Values in the parenthesis represent the percentage.

 

It is obvious from the Table 6 that 40.41% of the farmers are motivated by the Financial Security for availing the crop insurance, 39.04% from Banks/ Financial Institutions and 20.55% by hearing from other farmers.

 

Table 7: Source of Information about the Crop Insurance

Particulars	Frequency	Percentage
Radio and  T.V	14	9.59
Newspapers	18	12.33
Agriculture Department	37	25.34
Bank	29	19.86
Friends and Relatives	48	32.88
Total	146	100

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

The above Table 7 depicts that 32.88% are getting the information about the crop insurance from friends and relatives, 25.34% from agriculture department, 19.86% from bank, 12.33% from newspaper and 9.59% from Radio and T.V. 

 

One way ANOVA:

The researcher will be able to find out whether there is a difference in availing NAIS among different education level groups. A one-way ANOVA will evaluate the variance among the group means as a function of overall variance. It is appropriate to test differences among 2 or more groups.

 

 

A significant result can be interpreted to mean that all the group means are not equal (or close to equal). The one way ANOVA was used to test whether significant difference between educational level of farmers and availing of NAIS.

 

 

H0 (Null Hypothesis):

There is no significant difference between educational level of farmers with respect to availing of NAIS.

 

H1 (Alternate Hypothesis):

There is significant difference between educational level of farmers with respect to availing of NAIS.

 

Table 8: Relationship between educational level and availing of NAIS of the farmers Descriptive Availing NAIS

Education Level	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Minimum	Maximum
					Lower Bound	Upper Bound
Below School Level	61	1.64	.484	.062	1.52	1.76	1	2
School Level	115	1.57	.497	.046	1.48	1.67	1	2
UG	86	1.44	.500	.054	1.33	1.55	1	2
PG	38	1.29	.460	.075	1.14	1.44	1	2
Total	300	1.51	.501	.029	1.46	1.57	1	2

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

Table 9: ANOVA Availing NAIS

(Source: Primary Data). Values in the parenthesis represent the percentage.

 

The Table 8 presents descriptive statistics. The Table 9 presents the results of the ANOVA. It can be seen that the overall F is significant with 3 and 296 degrees of freedom, F (3, 296) = 5.174, p < .001. It is concluded that at least two group means are significantly different.

 

 

From the Table 9 it is obvious that p value (0.002) is less than 0.010 Null Hypothesis is rejected. Hence it is concluded that there is significant difference between educational levels of farmers with respect to availing of NAIS.

 

Table 10: ANOVA

Variables	F-Value	Sig
Age and availing NAIS	0.714	0.544*
Borrowing amount and availing NAIS	14.002	0.000**
Borrowing and nature of family	8.102	0.005*
Borrowing and nature of land holding	10.296	0.001*
Borrowing amount and experience of farmer’s in farming	2.568	0.057**
Borrowing amount and farmer’s category	1.395	0.252**
Education level and borrowing amount	6.736	0.000*

(Source: Primary Data). Values in the parenthesis represent the percentage.  ** Significant relationship. * No significant relationship.

 

 

The Table 10 represents the ANOVA tests for various combinations of variables. The table shows that the borrowing of the farmer’s has a significant relationship with nature of family either joint family or nuclear family and nature of land holding i.e. own land or leased land. Similarly the borrowing amount has significant relationship between the educational level and no significant relationship between the farmer’s category and experience of the farmer’s in farming.

 

Chi-square Analysis:

In chi-square test, an attempt is made by applying cross tabulation test to check that is there any relation between farmer’s category and borrowings of the farmer’s. Following hypothesis was taken:

 

H0: There is no association between farmer’s category and borrowings of the farmer’s.

 

H1: There is association between farmer’s category and borrowings of the farmer’s.

 

Table 11: Cross Tabulation

Farmer’s category                                     Borrowings	Yes	No	Total
Small Farmer(< 2.5 Acres of land)	41	41	82
Medium Farmer (< 5 Hectares of land)	67	82	149
Large Farmer (> 5 Hectares of land)	28	41	69
Total	136	164	300

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

Table 12: Chi square Analysis

	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	1.358a	2	.507
Likelihood Ratio	1.359	2	.507
Linear-by-Linear Association	1.350	1	.245
N of Valid Cases	300

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

From the above Table 11 and 12, results of Chi-Square test, it is obvious that value of Pearson Chi-Square is .507 which is greater than 0.05, so the null hypothesis is accepted i.e. there is no association between farmer’s category and borrowings of the farmer’s.

 

Table 13: Chi square for Demographic factors and Borrowings

Demographic factors	Asymp. Sig (2 sided)	Significance
Age	0.026	Significant @ 5% level
Education	0.480	No significant
Nature of the family	0.005	Significant @ 5% level
Farmer’s category	0.002	Significant @ 5% level
Nature of land holding	0.002	Significant @ 5% level
Experience of farmer in farming	0.239	No significant
Income from the farming	0.634	No significant

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

The Table 13 depicts the summarized value of chi-square. This shows that there is significant relationship between borrowing and the demographic factors such as age, nature of the family (i.e) joint or nuclear, farmer’s category namely small farmers or medium or large farmers,  nature of land holding such as own land or leased land, etc.

 

Correlation Analysis:

The bivariate Pearson Correlation produces a sample correlation coefficient, r, which measures the strength and direction of linear relationships between pairs of continuous variables. By extension, the Pearson Correlation evaluates whether there is statistical evidence for a linear relationship among the same pairs of variables in the population, represented by a population correlation coefficient, ρ (“rho”).

 

Table 14: Correlation for demographic variables and satisfaction level

Demographic factor                                  Satisfaction Level		SL of  Crops covered	SL of Premium	SL of Sum assured	SL of Claim procedure	SL of  Facilities available at financial institution	SL of  Documentation	SL of  Loss assessment unit
Age	Pearson Correlation	-.098	.058	.155	-.035	-.791**	.121	-.089
	Sig. (2-tailed)	.242	.484	.062	.675	.000	.146	.286
	Significance	NS	NS	NS	NS	S	NS	NS
Education Qualification	Pearson Correlation	.073	-.012	-.075	-.870**	-.035	-.886**	-.042
	Sig. (2-tailed)	.384	.887	.371	.000	.674	.000	.612
	Significance	NS	NS	NS	S	NS	S	NS
Nature of the Family	Pearson Correlation	-.018	.063	.103	.046	-.157	.050	-.068
	Sig. (2-tailed)	.828	.452	.217	.585	.050	.553	.414
	Significance	NS	NS	NS	NS	S	NS	NS
Farmer’s Category	Pearson Correlation	.134	-.322**	-.095	-.002	-.110	-.008	-.824**
	Sig. (2-tailed)	.107	.000	.252	.985	.186	.923	.000
	Significance	NS	S	NS	NS	NS	NS	S
Nature of Land holding	Pearson Correlation	.065	-.117	-.073	-.167*	.019	-.143	-.106
	Sig. (2-tailed)	.434	.158	.380	.043	.818	.086	.204
	Significance	NS	NS	NS	S	NS	NS	NS
Framer’s Experience in farming	Pearson Correlation	-.819**	.115	.020	.031	-.110	.019	.098
	Sig. (2-tailed)	.000	.168	.809	.714	.184	.825	.241
	Significance	S	NS	NS	NS	NS	Ns	NS
Income from farming	Pearson Correlation	.078	-.672**	-.739**	-.049	.116	-.171*	.114
	Sig. (2-tailed)	.350	.000	.000	.556	.165	.039	.172
	Significance	NS	S	S	NS	NS	S	NS
Borrowing	Pearson Correlation	.009	-.010	-.054	-.397**	-.049	-.313**	-.008
	Sig. (2-tailed)	.914	.903	.516	.000	.556	.000	.926
	Significance	NS	NS	NS	S	NS	S	NS

(Source: Primary Data). Values in the parenthesis represent the percentage. 

SL- Satisfaction Level, NS-No Significant and S-Significant

*. Correlation is significant at the 0.05 level (2-tailed). 

**. Correlation is significant at the 0.01 level (2-tailed).

 

The above Table 14 shows the correlation for demographic factors of the farmers’ and satisfaction level regarding various crop covered, Premium, Sum assured, Claim procedure, Facilities available at financial institution, Documentation and Loss assessment unit. ** represents that the factors are significant at 1% level and * depicts that the factors are significant at 5 % level. Negative symbol indicates negative relationship between the compared factors or otherwise there is a positive relationship between those factors.  It is interpreted that most of the demographic factor has no significant relation with the satisfaction level.

Garrets ranking Technique:

Garrets Ranking Technique has been used to analyze the factors that need improvement to increase in availing the NAIS scheme by the farmer’s Under the Garrett’s Ranking Technique the percentage position is calculated by using the following formula:

Percentage Position =           100(Rij-0.5)

                                                       Nj

Where Rij= Rank given for i th variable by the j th respondent.

Nj= Number of variables ranked by the farmers.

 

 

Table 15: Factors needs improvement to increase in availing of NAIS scheme

S. No	Factors	I	II	III	IV	V	VI	VII	Total score	Rank
1.	Cover more crops	182	48	20	56	51	60	47	464	VII
2.	Individual assessment	196	138	95	44	54	90	2	619	IV
3.	Reduce premium	224	150	110	28	54	24	30	620	III
4.	Quick settlement of claims	70	42	280	180	18	42	2	634	I
5.	Making scheme voluntary	182	144	115	16	156	4	15	632	II
6.	Insurance service at your doorstep / at village level	63	330	30	36	42	54	25	580	V
7.	Classes to be conducted in the presence of villagers / insurance company’s representatives	105	24	80	224	63	18	25	539	VI

(Source: Primary Data). Values in the parenthesis represent the percentage. 

 

It is observed from the Table 15 that the factor quick settlement of claims is ranked first by the farmers for improvement and second is ranked for the factor making the scheme voluntary for all including the loanee farmer’s. The factor reduce the premium is ranked by the third, fourth is ranked for the factor individual assessment for assessing the loss from the farm, the factor insurance service at the door step of framer’s is ranked as fifth factor and sixth and seven factor are classes to be conducted and crop coverage respectively.    

 

Table 16: Reason for Not availing NAIS

S. No	Reason	I	II	III	IV	V	Total Score	Rank
1.	Not aware of crop insurance	70	252	153	110	15	600	1
2.	No need of insurance	125	160	87	70	25	467	8
3.	Lack of premium paying capacity	60	152	126	54	36	428	9
5.	Not satisfied with crops covered	85	184	135	46	23	473	6
6.	Not satisfied with area approach	55	132	141	52	37	417	10
7.	Complex documentation	90	176	135	42	26	469	7
8.	Lack of service / co-operation from the bank	135	164	87	72	41	499	5
9.	No faith in scheme / agency	130	260	108	24	15	537	2
10.	Delay in claim payment	135	216	123	22	21	517	3
11.	Not satisfied with indemnity level	120	244	90	32	23	509	4

 (Source: Primary Data). Values in the parenthesis represent the percentage. 

 

The Table 16 represents the reason for not availing the NAIS schemes by the farmers. It is evident from the table that the reason unawareness of that scheme was ranked first by the farmers’ and second is ranked for the reason unfaith in the scheme/agency. Further delay in claim payment is ranked as third, fourth rank is given to the reason indemnity level is not satisfied by the farmer’s, fifth rank is for lack of service/ cooperation from the bank, sixth reason is not satisfied with the crop coverage. Complex documentation is ranked as seventh, eighth and ninth ranks are ranked to the reason such as no need of insurance and lack of premium paying capacity respectively. The reason not satisfied with the area approach is ranked as tenth.

 

FINDINGS:

·         Most of the farmers’ belongs to the age category of 25-30 years of age, 6-10 years of experience in farming and completed only school level. Majority of the farmers’ are living in nuclear family and are coming under the category of medium farmers’ (< 5 Hectares of land) that are cultivated by leasee.

·         Majority of the insured farmers’ have taken loan from the Primary Agricultural Credit Society and repaying the loan amount by selling the assets which reduces the land.

·         The farmers’ are motivated by banks/financial institution to avail NAIS scheme and they getting the information about the scheme through newspaper.  Using Garrets ranking technique it is identified that the factor quick settlement of claim should be improved to increase the availing of NAIS scheme and the factor lack of awareness is the reason for no availing insurance by the farmers’.

·         Using analysis of variance method it is found that there is significant difference between the variables like educational levels of farmers with respect to availing of NAIS, borrowing amount and availing NAIS, borrowing amount and experience of farmer’s in farming and borrowing amount and farmer’s category.

·         There is association between the demographic factors like age, nature of the family, family category, nature of land holding and borrowings using chi-square.  

·         It is obvious from the correlation analysis that there is negative relationship between (i)age and satisfaction level of the facilities available at financial institution, (ii) education qualification and satisfaction level of Claim procedure, satisfaction level Documentation, (iii) nature of the family and satisfaction level of the facilities available at financial institution, (iv) farmers’ category and  satisfaction level of Premium, SL of  Loss assessment unit (v) farmers’ experience in farming and satisfaction level of the Crops covered (vi) income from farming and SL of Premium (vii) borrowing and SL of Claim procedure.

 

SUGGESTION:

Ø  The NAIS scheme can be educated to the farmers’ through mass communication for increasing the awareness among farmers as majority of them are aware of that scheme.

Ø  The government may take action to promote the insurance schemes, strengthen the rural development funds and insurance regulatory mechanism.

Ø  The insurer shall ensure that there is proper network between bank and other financial institutions to maximize the coverage and reach of insurance scheme to the farmers’.

Ø  As most of the farmers are incurring loss in cultivating the crop and may not have the financial strength to withstand a delay in payment of claim amount, become defaulters on loan and sell their asset to repay the loan. So the government may take steps to make sure that the claim amount is settled as at the earliest time or certain percentage of amount can be settled before the assessment of actual yield.

Ø  It is suggested that separate pilot scheme can be designed to all crop which is not included in NAIS scheme as most of the farmers’ are not satisfied with the crop coverage.

Ø  Especially for non-loanee farmers’, enthusiastic network of the implementing insurer agency is required at either District or Taluka level and certain percentage of the premium shall borne by the bank/financial institution.

Ø  Financial institution/ bank official can play a vital role in creating awareness by explaining the terms and condition of the insurance scheme.

 

CONCLUSION:

There are almost 100 million farmers in India who works hard and suffer the most. Expanding the crop coverage under the NAIS scheme would increase the government costs considerably. If not the scheme is redesigned carefully to make it feasible, the prospects of its future expansion to include. Insurance schemes for the rural areas should be structured simply and awareness might be made at large level. From the present study it is understand that unawareness, no faith in the scheme, delay in claim settlement, not satisfied with the indemnity level are the reason for not availing NAIS scheme.  The factors like quick settlement and making the scheme voluntary for loanee farmers’ requires modification to increase the usage of NAIS scheme. The farmers are having opinion that the crop insurance is mainly for large farm size farmers, its risk sharing was very low and small medium farmers are not in position to pay the premium rate.

 

REFERENCE:

1.        Harwood, J., R. Heifner, K. Coble, J. Perry and A.Somwaru. Managing Risk in Farming: Concepts, Research and Analysis. Agicultural Economic Report No. 774. Market and Trade Economics Division and Resource Economics Division, Economic Research Service, U. S. Deparment of Agriculture. March 1999:1- 125.

2.        Weaver, R. D. and Kim, T. Designing Crop Insurance to Manage Moral Hazard Costs, Paper prepared for presentation at the Xth EAAE Congress ‘Exploring Diversity in the European Agri-Food System’, Zaragoza (Spain), 2002. A review available from URL: http://ageconsearch.umn.edu/bitstream/24784/1/cp02we08.pdf.

3.        Quiggin, J., Karagiannis, G. and Stanton, J. Crop Insurance and Crop Production: An Empirical Study of Moral Hazard And Adverse Selection. Australian Journal of Agricultural Economics,37(2).1986, A review available from URL: http://www.uq.edu.au/economics/johnquiggin/JournalArticles93/Cropins93.pdf

4.        Roberts, M., Key, N. and O’Donoghue, E. (2006) Review of Agricultural Economics. 28(3): 381–390. 2006. A review available from URL: http://naldc.nal.usda.gov/download/ 36723/PDF.

5.        Mark, Wenner and Diego, Arias Risk Management: Pricing, Insurance, and Guarantees- Agricultural Insurance in Latin America: Where Are We?” (Inter American Development Bank), 2003. A review available from URL: www.basis.wisc.edu/live /rfc/cs_03b.pdf.

6.        Hazell, P. B. R. The Appropriate Role of Agricultural Insurance in Developing Countries. Journal of International Development. 1992 .4: 567-581.

7.        Bindiya Kunal Soni and Jigna Trivedi , Crop Insurance: An Empirical Study on Awareness and Perceptions, Gian Jyoti E-Journal, April-June 2013. 3 and  2: 81-93.

8.        Chen, Shu-Ling. Acreage Abandonment, Moral Hazard and Crop Insurance. Selected Paper Prepared for Presentation at the American Agricultural Economics Association Annual Meeting, Providence, Rhode Island, July 24-27, 2005. A review available from URL: http://ageconsearch.umn.edu/bitstream/19114/1/sp05 ch06.pdf.

 

 

 

 

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