1. INTRODUCTION:

Agriculture plays a key role in the overall economic and social well-being of our country. More than half of the workforce of the country are engaged in farming or allied activities. It is farmers who supplies the food and nutrition to the entire nation. They also provide livelihood to millions of people in the country. Over the years India has moved from a state of food shortage to self-sufficiency in food, from importer of food grains to exporter of a variety of food items. Since the days of Green Revolution, our farmers have been using varieties of improved seeds and various sources of irrigation for their crops. Agriculture is the backbone of Indian economy. Agriculture encompasses a wide variety of specialties and techniques including ways and means to expand the land suitable for plant raising and other farm activities. For stimulating the tempo of agriculture production an adequate and timely credit is most essential. Modern agriculture is costly affaire and farmers have to rely on outside finance to adopt better techniques of production. There is strong and positive relationship between agriculture growth and availability of credit. The availability of agriculture credit is in the form of short-credit, medium-term credit and long-term credit. Large number of people depend upon rural employment for a living. Current agriculture practices are neither economically nor environmentally sustainable and India’s yield for agriculture commodities are low. The severe problem faced by agriculturists is “lack of adequate credit facilities”. Finance has been recognized as the blood of all economic activities of agriculture. For stimulating the tempo of agriculture production and adequate and timely credit is most essential. Availability of finance will help the famers to produce the crop at the right time and market the same without any delay. Farmers can market necessary equipment’s, seeds, fertilizers etc. to get good productivity of crop.

2. LITERATURE REVIEW:

Access of resources and opportunities can be crucial factor in improving outcomes for disadvantaged groups. Improving access to financial resources in particular is widely acknowledged to facilitate upward economic and social mobility. Conversely lack of access to resources for certain groups based on caste, class, gender and ethno-social identities can perpetuate inequalities. This paper attempts to analyses the access to credit by social groups and decomposes the gross credit differentials using Oaxaca-blinder decomposition method using unit-level data from the All India Debt and Investment Survey, NSSO, 2013 Karthik V and Madheswaran S (2018). Agriculture commodity prices have been quite volatile in India. The proposed study explores the effect of structural change on the flow of information between the spot and futures market of agriculture commodities and select macro-economic factors. The study uses non-linear integration and causality test to understand the direction of causality in volatility of commodities and impact of macroeconomic factors. The study observed the agriculture commodities spot and futures prices to be co-integrated with crude, forex and Sensex for majority of the break periods. We find robust evidence that futures market played a leading role in the price discovery function and information processing. Breaks in agriculture commodity prices are attributed to fundamentals of demand and supply in the market and global financial turmoil of 2007 Shernaz Bodhanwala, Harsh Purohit and Nidhi Choudhary (2018). Farmer suicides has turned out to be one of the major policy concern in India that has resulted in profound implications on the quality of life of farmers. In Karnataka, the Department of Agriculture recorded four farmer suicides per day between the periods from July, 2015 to June, 2016. Out of 1490 farmer suicides that occurred during this period in Karnataka, 80 per cent were committed by marginal and small farmers. The period of July, 2015 to October, 2015 Kharif was peak of farmer’s frustration in Karnataka as 56 per cent of suicides occurred during this period. The intensity of suicides in Mandya was extremely high at 62 suicides per lakh hectares of net sown area and 49 farmer suicides per lakh hectare of gross sown area. Haveri had the highest of 57 suicide cases per lakh of farming families. An amount of Rs. 39.20 crores were paid as compensation to 784 victim families at the rate of Rs. 5 lakhs per family during period from July 2015 to June 2016. While 408 cases were rejected, 298 cases were pending for which decision has to be taken A V Manjunatha and K B Ramappa (2017). In this the entire relationship between the farmer – state and agriculture comes under scanner. The very understanding of the farm and the personality of farmer itself is undergoing change. The 59^th round of NSSO bared in front of the nation the fragility of the farm sector, when almost 40% of the farmers across states desired to walk out of the profession in preference to any other work. The Population Census clearly tells us that 5 million cultivators have declined between the censuses joining the fold of agricultural labours. Acute poverty has become one of the most probable destination of the small and marginal farmers. The State power was being appropriated by some interest groups irrespective of the parties in power. The agricultural sector policies were largely determined by the corporatists and interest groups. The group of farmer that was also very close to the politicians and bureaucrats dominated the drafting of the policies, but more than 80 per cent of the farmers being small and marginal farmers with flimsy resources at their control, pushed for survival on margins R S Deshpande (2017). Naturally endowed with fertile soil, adequate rainfall, rich water resources and congenial climate, Bihar is poised for high farm productivity to lead the second green revolution in the country. Realizing its importance, Government of Bihar adopted planned approach of agriculture development through Agriculture Road Maps since 2008. It aims to increase production and productivity of food crops in a cost-effective manner and make it a viable means of livelihood. The approach has put considerable emphasis on ensuring availability of certified seeds at subsidized rate, creating storage space, promoting bio-farming, farm mechanization and new technique of System of Rice Intensification (SRI) cultivation Arvind Kumar Srivastava (2017). The Financial inclusion may be defined as the process of ensuring access to financial services and timely and adequate credit where needed by vulnerable groups such as weaker sections and low income groups at an affordable cost. Providing access to basic banking services is the first phase of the financial inclusion process. The relevance of financial inclusion in Tripura carries a lot of points. The economy of Tripura plagued by geographical isolation, poor infrastructure facilities, higher incidence of poverty and high unemployment, etc., is agrarian and more than 80 per cent of population lives in the rural areas. The Census-2001 data reveal that proportion of households availing of commercial banking services in the State was 26.5 per cent compared to all-India level of 35.5 per cent. There are different estimates on the extent of exclusion but most of the estimates establish that the NER region is financially excluded and the region lies at the bottom Sanjay Roy (2016). The period of the 2000s witnessed a sharp revival in agricultural credit in India that was largely policy induced. There were emerging shifts in institutional, functional and regional distributions of agricultural credit during the decade. This study attempts to explore the relationship between agricultural credit and agricultural production or productivity. The state-level panel model attempted in this article suggests a positive impact of the intensity of agricultural credit on total factor productivity in agriculture. The impact was relatively stronger with respect to direct agricultural credit Rekha Misra, Pallavi Chavan and Radheshyam Verma (2016). The study attempts to assess the potential of microfinance to overcome the limitations of formal institutional finance and effectively contribute to the economic empowerment of the vast majority of rural masses in India. Using a sample survey, information on various socio-economic indicators of beneficiaries of formal institutional finance and those of microfinance was collected to understand the limitations, advantages and impact of both forms of rural credit in India, by undertaking a comparative analysis. The sample households covered are 167 members of 14 SHGs and 100 members of the cooperative society. The study revealed that microfinance very well suits the socio-economic realities of the rural poor in India, and effectively contributes to their economic prosperity Amaresh Samantaraya and Aritraa Goswami (2015). The relationship between formal agricultural credit and agricultural GDP in India, specifically the role of the former in supporting agricultural growth, using state level panel data covering the period 1995-96 to 2011-12. The study uses a mediation analysis framework to map the pathways through which institutional credit relates to agricultural GDP relying on a control function approach to tackle the problem of indigenous. The findings from the analysis suggest that over this period, all the inputs are highly responsive to an increase in institutional credit to agriculture. A 10 % increase in credit flow in nominal terms leads to an increase by 1.7% in fertilizers consumption in physical quantities, 5.1% increase in the tones of pesticides, 10.8% increase in tractor purchases. Overall, it is quite clear that input use is sensitive to credit flow, whereas GDP of agriculture is not. Notwithstanding these aggregate findings detailed microstudies would be necessary to provide insights into this issue Sudha Narayanan (2015). To understand the changes in ownership of agricultural land and bovine holding in India, analyses of agricultural census data were carried out with a view to primarily providing some valuable inputs for the prospective policymakers in the field of agro-livestock sector. The said analyses are mainly motivated towards providing insights into the structure of rural ownership of agricultural lands and associated structural changes that follow in bovine asset. The study reveals that though the operational area remained constant, the operational landholding increased by 8.5 million during 2005-06 to 2010-11 with 1.7 million holdings getting added every year, mostly as marginal holdings and to a limited extent as small holdings T N Datta, Shrestha and G Chokkalingam (2015).

Research gap:

Based on the literature identifying the source of agro finance was studied in scant. Many farmers in Karnataka were found to be having very low financial wellbeing. Every farmer is suffering from financial losses. The reason for the financial loss is to be interesting to study the present study tries to throw light on the factors influencing the farmers financial wellbeing in different places of Karnataka where farmers are committing suicides after undergoing financial trauma. The studies to understand the financial wellbeing and debt burden level is not addressed in many literatures. Hence it is entrusting to understand the credit risk and debt burden level and understand the financial wellbeing of farmers.

3. FACTORS AND VARIABLES:

Factors for Farmers financial wellbeing

The factors contribute farmers financial wellbeing and credit risk associated with agro credits, which implies different variables used for getting improvement in their financial position. Socio-Economic factor which influencing the participation of MGNREG scheme and Social capital, it builds the relationship between farmers within them and helps to assist agricultural operating activities. The second factor is Agro credits which is considers agricultural subsidies and availability of funds in many sources of financial beneficiaries like getting interest discount, building good relationship between customers and bank employees then it turns into financial wellbeing of farmers. The last one is Physical factor which influences the productivity of crops and failure of irrigation system, the overall farmer’s improvement is depending on these two variables. In case failure of these two variables the total relation of dependent and independent factors will shows negative result.

Participation of MGNREGS:

The variable found in “Determinants of financial inclusion in tribal district of Odisha: an empirical investigation (2016)” In rural areas most of the people are daily wage labourers and low level cultivators of farm operations. So, every farmer need financial background from government schemes. It will help to improve every individuals financial position. Cost of credit: This variable found in “Commercial bank finance to agriculture in India-the recent trends and issues” (2014). When we take the credit or loan from the bank or any financial institutions for that we need to pay some percentage of amount as interest by grace period as discount for the principle amount called cost of credit. Agricultural credit subsidiaries: This variable found in “Commercial bank finance to agriculture in India-the recent trends and issues” (2014). The financial assistance provided by government to farmers through government-sponsored price-support programs. The objective behind providing agriculture subsidies is to provide benefits to farmers and thereby stabilize food prices, ensure plentiful food production, and to guarantee basic income to farmers. Availability of funds: This variable found in “Commercial bank finance to agriculture in India-the recent trends and issues” (2014). Agricultural credit in India is available to farmers and other people working in the farming sector in India from various sources. Short and medium term agricultural credit requirements of farmers and others employed in the agricultural sector in India are usually met by the government, money lenders, and co-operative credit societies. Banks and Other Institutions: This variable found in “Commercial bank finance to agriculture in India-the recent trends and issues” (2014). In agriculture sector many farmers are getting loan from institutional and non-institutional banks. In that time farmers are facing lot problems related to repayment of loan. Debt burden: This variable found in “Commercial bank finance to agriculture in India-the recent trends and issues” (2014). It described as impoverishment by debt or where farmer as in the situation of spiral debt. Some of others talk about indebtedness means large number of outstanding loans, high rate of interest and there is no hope to clear the principle amount of credit or loan. Productivity of crops: This variable found in “Credit for agricultural households in India: growing inequities (2015). Crop productivity is the quantitative measure of crop yield in given measured area of field. The use of new crop varieties and the efficient application of agrochemicals, immensely contributed to increased plant productivity. The total output of agricultural productivity is shows the farmer financial position. Infrastructure: This variable found in “Credit off-take from formal financial institutions in rural India; Quantile regression results” (2014). Infrastructure assets such as rural roads, tracks, bridges, irrigation schemes, water supplies, schools, health centres and markets are needed in rural areas for the local population to fulfil their basic needs and live a social and economic productive life. Demand: This variable found in “Credit off-take from formal financial institutions in rural India; Quantile regression results” (2014). It is also play one of the major role in improving the financial needs of farm cultivator. While produced some agricultural productivity as much as needs but in case there is no demand for that, it directly effects to farmer. Supply: This variable found in “Credit off-take from formal financial institutions in rural India; Quantile regression results” (2014). Overall raw materials which is needed to cultivate the farming activities are very important tools for developing agricultural activities.

Ex:

Sufficient supply of labours, Supply of raw materials, Farming equipment’s, Modern machineries and Government accessories etc.

Failure of Irrigation system:

This variable found in “Credit for agricultural households in India: growing inequities (2015). In agriculture farming the major role plays by irrigation system, if there is no proper irrigation system there is no output from agricultural activity. So how irrigation system will help to get more yield and will get to know how it is favor of improving farmers financial wellbeing.

4. CONCEPTUAL MODEL:

Based on the definitions and latent variable, conceptual model is framed. Three constructs like Socio-Economic factors, Agro-Credit factors and Physical factors are the latent constructs having the impact on the farmers financial wellbeing.

Conceptual framework of the project Initial path model

5. HYPOTHESIS:

Hypothesis is the theory, methods, and practice of testing a hypothesis by comparing it with the null hypothesis. The null hypothesis is only rejected if its probability falls below a predetermined significance level, in which case the hypothesis being tested is said to have that level of significance.

H0 ≥ 0.05 (Null Hypothesis):

There is no significant or there is no relationship between two variables because the Alpha or P- value is more than 0.05 and then the Null hypothesis is a Rejected (P ≥ 0.05).

Ha ≤ 0.05 (Alternative Hypothesis):

There is a significant or there is a relationship between the two variables because the Alpha or P-Value must be less than 0.05 and then the Alternative hypothesis is accepted (P ≤ 0.05).

Hypothesis statements:

Availability of funds -> FFW	H0_AGC: There is no significant impact influenced by Availability of Funds on Farmers Financial Wellbeing H1_AGC: There is significant impact influenced by Availability of Funds on Farmers Financial Wellbeing
Failure of Irrigation -> FFW	H0_PF: There is no significant impact influenced by Failure of Irrigation on Farmers Financial Wellbeing H1_PF: There is significant impact influenced by Failure of Irrigation on Farmers Financial Wellbeing
PMG -> FFW	H0_SE: There is no significant impact influenced by participation of MGNREGS on Farmers financial wellbeing H1_SE: There is significant impact influenced by participation of MGNREGS on Farmers Financial Wellbeing
Productivity -> FFW	H0_PF: There is no significant impact influenced by Productivity of Crops on Farmers Financial Wellbeing H1_PF: There is significant impact influenced by Productivity of Crops on Farmers Financial Wellbeing
Socio capital -> FFW	H0_SE: There is no significant impact influenced by Social Capital on Farmers Financial Wellbeing Subsidies H1_SE: There is significant impact influenced by Social Capital on Farmers Financial Wellbeing
Subsidies -> FFW	H0_AGC: There is no significant impact influenced by Agricultural Subsidies on Farmers Financial Wellbeing H1_AGC: There is significant impact influenced by Agricultural Subsidies on Farmers Financial Wellbeing

6. RESEARCH METHODOLOGY:

Objective No.	Statement of the Objective	Method/ Methodology	Resources Utilized
1.	To study overall financial eco-system of farmers in Karnataka	Literatures, journals, thesis and website articles	Google scholar, EBSCO journals
2.	To identify the factors influencing farmers wellbeing	Survey had been conducted through face to face interview	Questionnaire
3.	To analyze the factors influencing farmers wellbeing	Descriptive statistics, Reliability and Validity test and Structural Equation Model	SPSS and SmartPLS
4.	To suggest suitable suggestions and recommendations for wellbeing of farmers in Karnataka	Based on analysis	Based on results and Interpretation

METHODS AND METHODOLOGIES OF EACH OBJECTIVE

7. SAMPLE DESIGN:

SL. No	Description	Methodology
1	Study	Empirical study
2	Target area	In Karnataka, Belagavi district Bailahongala, Kitturu and Ramadurga.
3	Sample size	200 respondents
4	Sampling method	Convenience sampling technique
5	Tools used	SPSS and PLS-SEM in Smart PLS
6	Analysis carried out	Descriptive statistics, Reliability & Validity test, Structural Equation Model

Research Design

Descriptive statistics:

1) Qualification of Farmers:

Following chart shows the qualification of the respondent’s farmers. Total sample size is 200 in that 1^st to 9^th standard was 30%, Illiterates was 52%, PUC was 5% and remaining 13% was SSLC. Here number of Illiterate farmers are more.

Pie chart of Qualification Analysis

2) Age group:

Total 200 respondents different type of age group was there, out of that 34% was 46 to 55 years these were the highest in respondents, 27% of 26 to 35 years, 25% of 56 to 65 years and 14% of 36 to 45 years.

Pie chart of Age

3) Type of Farm Land:

Out of 200 farmers 87% of the farmers are going cultivate their land by irrigation and 13% of Non-Irrigated land were used in agricultural activities

Pie chart of Farm Land Analysis

4) Type of Crop:

Farmers are use their land to get output of commercial and non-commercial crops. In this 85% of farmers are getting commercial crops but remaining 15% of the respondents were grown non-commercial crops.

Pie chart of Crop Analysis

5) Sources of Loan:

The total number of farmers were get loan from different types of financial institutions, in that 40% of people were get loan from co-operative societies, 30% of respondents get loan from commission agents like land lords, middle man etc., and remaining 30% of farmers are getting loan from money lenders. Following pie chart shows the percentage of getting sources of loan.

Pie chart of Sources of Loan

6) Loan taken:

Following pie chart shows the types of loans taken from farmers. Out of 200 respondents 56% of the people were get loans from public sector banks in that Kisan credit card scheme is most popularized it is known as KCC schemes, 27% of people get development loans and remaining 17% of farmers are get crop loans.

Pie chart of Loan Taken

7) Family size:

The following chart shows the family size of total respondents. Out of 200 people 43% of households are more than 16 peoples in their family size, 29% of households are 11 to 15 members, and remaining 28% of households are 6 to 10 members in their family.

Pie chart of Family Size

8) Experience of farming:

The following chart shows the respondents experience in agricultural activities. 47% of farmers have 11 to 15 years’ experience, 25% of respondents are 6 to 10 years and remaining 28% of farmers more than 16 years of experience.

Pie chart of Experience Analysis

8. RESULTS AND DISCUSSION:

PLS-SEM models in contrast are path models in which some variables may be effects of others while still be causes for variables later in the hypothesized causal sequence. PLS-SEM models are an alternative to covariance-based structural equation modelling.

The advantages of PLS include ability to model multiple dependents as well as multiple independents ability to handle multi collinearity among the independents robustness in the face of data noise and missing data and creating independent latent variables directly on the basis of cross-products involving the response variable, making for stronger predictions.

Path coefficients:

Path coefficients are always standardized path coefficients. Given standardization, path weights therefore vary from -1 to +1. Weights closest to absolute 1 reflect the strongest paths. Weights closest to 0 reflect the weakest paths.

	FFW
Availability of funds	0.157
FFW
Failure of Irrigation	0.185
PMG	0.144
Productivity	0.315
Socio capital	0.153
Subsidies	-0.016

R Square:

R-square, also called the coefficient of determination. As for any form of linear regression, multi collinearity may be present. If present, the researcher cannot use structural path coefficients to reliably assess the relative importance of predictor variables, including of predictor latent variables in the structural model.

	R Square	R Square Adjusted
FFW	0.304	0.282

Discriminant Validity: The average variance extracted appears in the diagonal cells the least value of correlation is farmers financial wellbeing i.e. 0.659. The diagonal value of correlation is the top number in any factor loadings.

Hypothesis testing:

Path Coefficients Significance level: T-Values and P-Values:

	T Statistics (\|O/STDEV\|)	P Values
Availability of funds -> FFW	2.372	0.018
Failure of Irrigation -> FFW	2.286	0.023
PMG -> FFW	1.816	0.070
Productivity -> FFW	4.556	0.000
Socio capital -> FFW	2.616	0.009
Subsidies -> FFW	0.258	0.796

Path coefficient significance level

Hypothesis test	Result	Null hypothesis (H0 ≥ 0.05)	Alternate Hypothesis (Ha ≤ 0.05)
Test-1	Availability of funds -> FFW	Rejected	Accepted
Test-2	Failure of Irrigation -> FFW	Rejected	Accepted
Test-3	PMG -> FFW	Accepted	Rejected
Test-4	Productivity -> FFW	Rejected	Accepted
Test-5	Socio capital -> FFW	Rejected	Accepted
Test-6	Subsidies -> FFW	Accepted	Rejected

Hypothesis testing results:

Test-1: P-Value is less than significant value 0.05 i.e. 0.018 Here the Null hypothesis is rejected and Alternate hypothesis accepted. There is significant impact of Availability of Funds on Farmers Financial Wellbeing.

Test-2: P-Value is less than significant value 0.05 i.e. 0.023. Here the Null hypothesis is rejected and Alternate hypothesis accepted. There is significant impact of Failure of Irrigation on Farmers Financial Wellbeing.

Test-3: P-Value is more than significant value 0.05 i.e. 0.070. Here the Null hypothesis is accepted and Alternate hypothesis rejected. There is no significant impact influenced by Participation of MGNREGS on Farmers Financial Wellbeing.

Test-4: P-Value is less than significant value 0.05. Here the Null hypothesis is rejected and Alternate hypothesis accepted. There is significant impact influenced by Productivity of Crops on Farmers Financial Wellbeing.

Test-5: P-Value is less than significant value 0.05 i.e. 0.009. Here Null hypothesis is rejected and Alternate hypothesis accepted. There is significant impact influenced by Social capital on Farmers Financial Wellbeing.

Test-6: P-Value is more than significant value 0.05 i.e. 0.796. Here Null hypothesis accepted and Alternate hypothesis rejected. There is no significant impact influenced by Agricultural subsidies on Farmers Financial Wellbeing.

Final Output Model

9. FINDINGS:

· Out of 200 respondents 105 samples are illiterates

· 50% of the age group respondents are 46 to 55 years, remaining people were different age group

· 87% of the farmers are using irrigated land to cultivate the crops

· 85% of the people were grown commercial crops but they won’t get proper price for the yields

· Majority 40% of the farmers getting the co-operative credit loan and remaining will prefer other kind of sources

· 56% of the farmers are getting the loan as crop loan and remaining respondents taking other type of loans like development loan, KCC loan, stock loan and others

· 43% of the farmers’ households exceeds more than 16 & above in family size

· Most of the respondents are more than 15 years of experience in agricultural activities

10. CONCLUSION:

Here twelve variables were used to connect the structural model measures out of which twelve variables supported only six variables for model in that six variables two has Null hypothesis called participation of MGNREGS and agricultural subsidies were there is no significant impact influence on farmers financial wellbeing. Remaining four has Alternate hypothesis i.e. availability of funds, failure of irrigation, productivity of crops and social capital. This clearly indicates that all the variables included in all factors had an influence on farmers financial wellbeing. However, two variables one from socio-economic factor called participation of MGNREGS and another one from agro-credit factor called agricultural subsidies do not support the alternate hypothesis and accepts as null hypothesis.

11. LIMITATIONS OF THE STUDY:

· Study was limited to Belagavi in Karnataka

· Consider only three dependent variables they are;

· Asset holding

· Expectation of future income and

· Economic status of household

· Farmers are the major respondents

· Risk hedging through productivity of crop

12. SUGGESTIONS:

· Crop failure was found to be the root cause for farmer suicides. Therefore, it is suggested that individual farmers should be brought under the ambit of crop insurance.

· More intensively the State Government must ensure through the proper policy framework that indemnity be paid within a week after reporting of the crop failure. Payments made months after the failure pushes the farmer into the debt trap.

· Risk hedging through crop and enterprise diversification should be encouraged to reduce farmers distress/risk aiming at sustainable income.

· Non-payment or delay of money to the producers by buyers should be avoided through designing suitable institutional mechanism.

· Regulating the informal credit market through licensing and fixing the norms for charging interest rate and terms of lending is required.

· There is a need to create indemnity to non-institutional borrowers.

13. FUTURE IMPLICATIONS:

· Study can be cover over other places of Karnataka

· Study can be concentrate on agricultural labors

· Focus on different type of sectoral banks

· Mainly concentrating on rural farmers

· Focus on specific type of loan disbursement

14. REFERENCES:

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3. Misra, R., Chavan, P. and Verma, R. (2016). Agricultural Credit in India in the 2000s: Growth, Distribution and Linkages with Productivity. Margin: The Journal of Applied Economic Research, 10(2), pp.169-197.

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Received on 13.09.2019 Modified on 18.10.2019

Asian Journal of Management. 2019; 10(4):368-376.

DOI: 10.5958/2321-5763.2019.00056.8

	Availability of funds	FFW	Failure of Irrigation	PMG	Productivity	Socio capital	Subsidies
Availability of funds	0.955
FFW	0.280	0.659
Failure of Irrigation	0.130	0.295	0.889
PMG	0.090	0.243	0.127	0.824
Productivity	0.225	0.421	0.141	0.173	0.813
Socio capital	0.149	0.265	0.182	0.054	0.154	0.799
Subsidies	0.466	0.169	0.112	0.066	0.232	0.056	0.953