Participation in Decision Making - A Study of Female Farmers in the Rural Area of Sikkim in North- Eastern India

Mrs Nidhi Dwivedy, (Dr.) N. Upadhyay

Department of Management Studies, Sikkim Manipal Institute of Technology, (SMIT), Management Department, Majitar, Sikkim, India

ABSTRACT

The topic of the researcher is “Role of female labour in farming sector: a study of state of Sikkim”. Various parameters have been studied under this heading of which decision making in crop production and animal husbandry and related activities is one of them. The present paper has presented the findings pertaining to this parameter. For this, data was collected from 230 female farmers through interviews using a pre-designed schedule from 24 circles from all the four districts of Sikkim State in North- Eastern India. Based on their subjective judgments, Descriptive as well as inferential statistics were used to analyze and interpret the data of decision making in fourteen crop production and animal husbandry activities using the Statistical Package for the Social Science (SPSS). Percentage, mean, standard deviation (SD), coefficient of variation (CV) of descriptive statistics and one sample t-test of inferential statistics has been used to interpret the data. The finding of the data has shown that they do participate in decision making for some of the activities in which their independent participation is more than that of men. Moreover, joint involvement in decision making was observed in agricultural/animal and the related activities even though women perform more in agricultural related activities than men. Even they need not be consulted at the time of purchase of animals or change of crop.

KEYWORDS: Decision making, Crop Production, animal husbandry, socio- economic factors.

INTRODUCTION

The extent of participation in the decision-making activities in house hold and agriculture related and other socio-culture affairs reflects the status of women in the family as well as in society. The major decision makers in agricultural activities are men even though women perform more in agricultural related activities than men. Even they need not be consulted at the time of purchase of animals or change of crop. An average, women spend 14 hours a day working in and outside the home. During harvesting season she spends about 16 hours a day (Chaudhary Sarmishta, 2004).

Sethi (1991) also confirms the poor participation of women in agriculture sector in Himachal Pradesh, where women’s opinion is normally not considered in the matters related to participation in developmental activities. Men continue to dominate the social scene as a decision maker in the production and the distribution of products and their participation and representations in village developmental activities has not changed over the time.

However, the Indian Himalayan region (IHR) displays a different picture in land use pattern and its dependency on agricultural land. The Himalayan people have traditionally practiced integrated agriculture, balancing cultivation, agro-forestry, animal husbandry and forestry. Mountain geography and inaccessibility have helped maintain agro-biodiversity; yet commercial agriculture is not as high-yielding and profitable as in the plains. Here forest is the major land use pattern, which covers over 52% of total reporting area followed by wastelands and agricultural land. However, the dependency on its limited arable land is marginally higher in the IHR as cultivators and agricultural labourers together comprise about 59% of total workforce in the region (Nandy and Samal, 2005).

Majority of the women in Manipur are the bread earners of their families, many of them the only bread earners. Being main earners, women had no time even to concentrate on their own plight. In the hills, much of the work is done by women and there is “iron like grip” rigidity in the division of labour. They work quietly and are invisible in any decision making bodies (Brara, N. Vijaylakshmi 2006).

Vaish, S. (1999) The different parameters in decision making of ladies were studied and the result showed that the main constraints in taking decisions about rice production technology were a lack of technical know-how (100%); lack of education in women (92%), men thinking that they know better (72%), the dominance of men in agriculture (69%) and opportunities not provided by men (59%). Sharma, (1992) also holds the similar views on the constraints faced by women in farming sector.

Some historians believe that it was woman who first domesticated crop plants and thereby initiated the art and science of farming. While men went out hunting in search of food, women started gathering seeds from the native flora and began cultivating those of interest from the point of view of food, feed, fodder, fiber and fuel (Prasad and Singh 1992). Women have protected the health of the soil through organic recycling and promoted crop security through the maintenance of varietal diversity and genetic resistance. Therefore, without the total intellectual and physical participation of women in agriculture including the decision making, it will not be possible to popularize alternative systems of land management to shifting cultivation, arrest gene and soil erosion, and promote the care of the soil and the health of economic plants and farm animals.

Demographic Features:

According to (Census 2011), Sikkim has a total population of 607 688 persons (which is 0.05 percent of total population of India) of which 321661are males and 286 027 are females. From the year 1991-01 to 2001-11, decadal population variation recorded was 33.07 to 12.36 percentages, while India’s figure for the same is 17.64. In 2011 rural population consists of 480,981 people while urban population consists of 59,870 people. Sex ratio (females per 1000 males) also known as Gender Ratio, in the same decade has shown a little improvement i.e. from 875 to 889 but still lags behind India’s, which is 940. Though population density per sq. km. has increased in the same decade from 76 to 86 but is much less than national population density per sq. km. which is equal to 382. Literacy rate in 2001 was 68.81 which rose to 82.20 in 2011 which is above national average of 74.04 percent. This decade has seen an increase in male literacy rate from 76.04 to 87.30 as against all India’s rate which is 82.14 and female literacy rate also shows increased figures i.e. from 60.41 to 76.43 as against all India’s rate of 65.46.

Workers Profile:

According to (Census 2001), there are 37,936 cultivators (About 26,000 of them are small/medium farmers) out of which 19,725 are males and 18,211 are females in East district. Of them 37,889 live in rural and only 47 live in urban area. In rural area 19,701 are males and 18,188 are females. Total no. of agricultural labourers 8,143 out of which 4,076 are males and 4,067 are females. Of them 8,110 live in rural and only 33 live in urban area. In rural area 4,056 are males and 4,054 are females.

There are 35,764 cultivators (About 16,000 of them are small/medium farmers) out of which 20,634 are males and 15,130 are females in West district. Of them 35,762 live in rural and only 02 live in urban area. In rural area 20,632 are males and 15,130 are females. Total no. of agricultural labourers in the district are 4,112 out of which 2,389 are males and 1,723 are females. Of them 4,110 live in rural and only 02 live in urban area. In rural area 2,389 are males and 1,721 are females.

There are 9,180 cultivators (About 6,000 of them are small/medium farmers) out of which 4,831are males and 4,349 are females in North district. Of them 9,173 live in rural and only 07 live in urban area. In rural area 4,824 are males and 4,349 are females. Total no. of agricultural labourers in the district are 2,051out of which 1,045 are males and 1,006 are females. Of them 2,038 live in rural and only 13 live in urban area. In rural area 1,033 are males and 1,005 are females.

There are 48,378 cultivators (About 20,000 of them are small/medium farmers) out of which 24,917are males and 23,461 are females in South district. Of them 48,377 live in rural and only 01 live in urban area. In rural area 24,917 are males and 23,460 are females. Total no. of agricultural labourers in the district are 2,694 out of which 1,252 are males and 1,442 are females. All of them live in rural and no one live in urban area. In rural area 1,252 are males and 1,442 are females.

The above data, showed that in all the districts more than half of the cultivators are small/medium farmers. It was also observed that almost all of them live in rural areas and equal number of females participants were sighted as of men.

RESEARCH METHODOLOGY:

Universe or population:

The universe or population for the study consisted of total number of married females in rural areas who are employed in farming in the state of Sikkim. This formed the pivotal point of the present research.

Sampling method for selected area of study:

Multi-stage stratified random sampling technique of probability method is used to distribute the population into circles, revenue blocks and villages, then a combination of Judgment and Convenience sampling techniques of non-probability methods is decided upon for this study. Non-probability methods are of three types, namely Judgment sampling, Convenience sampling and Quota sampling. The state has only four districts; so, all of them have been taken for the study. Initially, under the multistage stratified random sampling technique- a selection of a tentative list of circles and revenue blocks from all the four districts was made followed by a selection of villages to be visited at the second and a selection of respondents at the final stage. A final list of the respondents from different farm households was prepared based on convenience and their accessibility to the researcher by stratified random sampling.

Sample size:

Rural areas from all 4 districts of Sikkim were selected. As is clear from the table 2 below, though North district contains maximum area of the State i.e. almost 60%, but it holds only 7-8% of the population. On the contrary East district contains only 13% area of the State, but it holds maximum i.e. 45% of the population. So, for this study, maximum no. of females for data collection are from East and minimum are from North. Here the size of the sampling female farmers from each district is neither proportional to the minimum size of the sampling female farmers of the district nor in the same ratio as is the percentage ratio of each district to the total population of the state. But the sample size of each district is just an indicative of the reason of taking maximum/minimum sampling units from that area.

Source- Figures extracted from census 2001.

A data collected from a total of 24 circles from all the four districts in Sikkim has been analyzed. The district wise i.e. (East, West, North and South) distribution of circles selected is 6, 6, 4 and 8 respectively. A total of 80 females of farming community from East, 30 from North and 60 each from

West and South districts have been interviewed. Data for 115 samples (50% of 230), was collected by the researcher herself, while for rest of 115 samples (40, 30, 15 and 30 from East, West, North and South respectively), was collected through village heads by sending schedules to the village heads. Data thus collected from 230 females of the state, employed in farming sector has become the basis of the Primary Data analysis in this study.

Data collection and analysis:

In order to collect qualitative data, three group discussion sessions were arranged separately in three villages namely (Syari, Sichey and Rawtey rumtek); each group contained 10 participants. During these group sessions, several open-ended questions were asked from the respondents in order to collect deeper information about their accessibility to resources and their participation in different farms and the related activities along with many hidden facts and factors. Based on this information, the research instrument i.e. questionnaire containing dichotomous, multiple choice and open end questions was designed and a pre-test was conducted with 18 respondents for its necessary modification. It was then translated into Nepali also for the convenience of the farm population. Primary data was collected by researcher by visiting the farming females of rural area in Sikkim, using questionnaires. The primary data was collected between the months of March to September 2011 from all districts of Sikkim.

Books, journals, reports and internet documents were used as secondary sources of data supporting or supplementing the empirical findings of the study.

Data analysis:

Data were analyzed using the Statistical Package for the Social Science (SPSS) and some descriptive statistics, such as percentage, mean, standard deviation (SD), coefficient of variation (CV) and rank were used to interpret the data.

Decision making of a rural woman in different animal, farms and the related activities was measured using the Discrete Scale with a weight of 1 representing ‘no/poor decision making’, 2 for ‘rare’, 3 for ‘sometimes, 4 for ‘frequent’ and 5 for ‘always’ decision making. Finally, a rank order was developed based on mean score obtained for each item. For assigning ranks to means, 1 is used to represent ‘no/poor chances in decision making’, 2 for ‘limited’, 3 for ‘good’, 4 for ‘better’ and 5 for ‘best chances in’ decision making.

Table 1:- Selection of Sample Size:

District/ State	Total area(sq.km)		%of total area	Population Concentration	% 0f total Population	Total no. of circle	Total no. of circle ssampled	No.of female sample farmers
East		954	13.5	2,45,040	45.3	21	06	80
West		1166	16.5	1,23,256	22.8	21	06	60
North		4226	59.5	41,030	7.6	07	04	30
South		750	10.5	1,31,525	24.3	23	08	60
Sikkim		7096	100	5,40,851	100	72	24	230

There is only one sample in the study. Ordinal and nominal level data can be analyzed using parametric statistics; therefore One-Sample T-test for inferential interpretation of the data has been run to understand the nature of relation between the variables. For the inferences of the hypotheses, Information from literature survey is taken to support some assumptions.

Hypothesis Statement – Women are not consulted for decision making in farm, animal and related activities.

Ho - Decision making by women is not more in farm, animal and related activities.

Ha - Decision making by women is more in farm, animal and related activities.

RESULTS AND DISCUSSION:

Descriptive Details and Analysis of Decision Making:

(A), (B), (C), (D), (E), (F), (G), (H), (I), (J), (K), (L), (M), (N) in the (table-3) below, represents decision making by women in fourteen different farm, animal and related activities namely - selection of crops of the season to be sown (A), selection of harvesting time (B), changing of crops (C), purchase of agricultural equipment (D), procurement of fertilizer (E), selection and procurement of seeds of new variety (F), selling of crops/cereals/ vegetables (G), purchasing/selling of livestock (H), selection of breed of animals (I), storage of green fodder for lean period (J), selling of surplus dry fodder (K), procurement of dry fodder from the market (L), selling of green fodder in the market (M), selling of milk/poultry items (N).

In Table-2 below, on the basis of mean score it can be said that sample female farmers get better chances in decision making for activities like- Selection of crops of the season to be sown, Selection of harvesting time. In these activities, their relative participation is also greater than that of men.

They get good chance in decision making for- Selling of milk/poultry items, Changing of crops, Selling of crops/cereals/ vegetables, Purchase of agricultural equipment, Selection and procurement of seeds of new variety, Storage of green fodder for lean period. They get limited chance in decision making for-Selection of breed of animals, Procurement of fertilizer, Purchasing/selling of livestock. Availability of organic fertilizer at almost everybody’ place was the reason told by the respondents for less participation in decision making in this particular activity. While decision making was found to be poor in activities like-Selling of green fodder in the market, Procurement of Dry fodder from the market, selling of surplus dry fodder.

Inferential Analysis of Participation in Decision Making:

From the table 3 we find that confidence intervals lie entirely above 0.0 and also it is positive. The value of ‘t’ for decision making in selection of crops of the season to be sown, selection of harvesting time, changing of crops and selling of milk/poultry items is 11.256, 8.970, 4.193 and 4.822 respectively which is higher than 1.96, mean difference column for them also shows positive values. This is further confirmed by significance levels which are 0.00 and also by confidence intervals, both limits of which lie entirely above 0.0 for all these activities. We can safely say that null hypothesis for these activities is rejected and thus alternate hypothesis for these activities is accepted. Further, we conclude it by saying that decision making by women in these farm, animal and related activities is significantly more than 3 on an average. That means for these activities women frequently/always make decisions.

Table- 2-Participation in decision making for sample female farmers in Farm and related activities of Sikkim State

Decision making of Farm and related activities		Extent of decision making ( % )								Mean*	Rank order
		Never		Rarely	Sometimes	Frequently		Always
Selection of crops of the season to be sown		06(03)		25(11)	66(29)	26(11)		107(46)		3.88(30.64)	1
	Selection of harvesting time		13(06)	24(10)	64(28)		40(17)		89(39)	3.73(33.1)	2
	Selling of milk/poultry items		17(07)	33(14)	85(37)		33(15)		62(27)	3.39(36.3)	3
	Changing of crops		09(04)	44(19)	83(36)		58(25)		36(16)	3.30(32.3)	4
	Selling of crops/cereals/ vegetables		26(11)	36(16)	109(47)		29(13)		30(13)	3.00(37.4)	5
	Selection/procurement of seeds of new variety		23(10)	60(26)	97(42)		28(12)		22(10)	2.85(37.6)	6
	Storage of green fodder for lean period		50(22)	45(20)	68(30)		24(10)		43(19)	2.85(48.3)	7
	Purchase of agricultural equipment		28(12)	45(20)	115(50)		23(10)		19(08)	2.83(36.8)	8
	Purchasing/selling of livestock		55(24)	55(24)	92(40)		16(07)		12(05)	2.46(44.2)	9
	Selection of breed of animals		81(35)	65(28)	59(26)		06(03)		19(08)	2.20(54.1)	10
	Procurement of fertilizer		84(37)	70(30)	55(24)		12(05)		09(04)	2.10(51.3)	11
	Procurement of Dry fodder from the market		127(55)	60(26)	41(18)		02(01)		00(00)	1.64(48.8)	12
	Selling of green fodder in the market		157(68)	52(23)	21(09)		00(00)		00(00)	1.41(46.3)	13
	Selling of surplus dry fodder		180(78)	37(16)	13(06)		00(00)		00(00)	1.27(44.09)	14

* Mean values of items ranging from 1 to 5, where 1 indicates ‘no chances in decision making’ and 5 indicate ‘best’ chances in decision making. Figures in the parentheses of mean column indicate CV = (SD / Mean) × 100.

Figures in the parentheses indicate the % decision making

Inferential Analysis of Participation in Decision Making:

From the table 3 we also find that value of ‘t’ for decision making in purchase of agricultural equipment (D), procurement of fertilizer, purchasing/selling of livestock, selection of breed of animals, selling of surplus dry fodder, procurement of dry fodder from the market, selling of green fodder in the market is -2.529, -12.728, -7.576, -10.123, -46.768, -25.698, -36.970 respectively which is negative. This is further confirmed by significance levels which are 0.00 and also by confidence intervals, both limits of which lie entirely below 0.0 for all these activities. Mean difference column for it also shows negative values. Thus there are valid reasons for null hypothesis to be accepted for these activities, which says That decision making by women is significantly not more than 3 on the average in these farm, animal and related activities. That means for these activities women never/rarely/sometimes make decisions.

For activities i.e. Selection/procurement of seeds of new variety (F), selling of crops/cereals/ vegetables (G) and storage of green fodder for lean period (J), we find that value of ‘t’ is -2.092, 0.059 and -1.673, which is lower

than 1.96.

This is further confirmed by mean difference column for it also shows negative value (except for selling of crops/cereals/ vegetables (G) for which it is positive).

But if we look at the significance level it is not 0 but has got the value which is .038, 0.953 and .096 respectively and also it is more than 0. Besides, the magnitude of upper limit is more than the magnitude of the lower limit for G and the magnitude of upper limit is less than the magnitude of the lower limit for F and J. We can safely say that Null hypothesis is accepted for (F) and (J). That means for these activities women never/rarely/sometimes make decisions for F and J.

Also we can safely say that null hypothesis is rejected for (G) and thus alternate hypothesis is accepted. Further, we conclude it by saying that decision making by women in these farm, animal and related activities is significantly more than 3 for (G) on an average. That means for this activity women frequently/always make decisions.

CONCLUSION:

We can conclude about participation in decision making of sample female farmers on the basis of mean score that they get better chances for making decision in activities like-Selection of crops of the season to be sown, Selection of harvesting time. In these activities, their relative participation is also greater than that of men.

· They get good chance in decision making for-Selling of milk/poultry items, Changing of crops, Selling of crops/cereals/ vegetables, Purchase of agricultural equipment, Selection and procurement of seeds of new variety, Storage of green fodder for lean period.

· They get limited chance in decision making for-Selection of breed of animals, Procurement of fertilizer, Purchasing/selling of livestock.

· Decision making was found to be poor in activities like-Selling of green fodder in the market, Procurement of Dry fodder from the market, selling of surplus dry fodder.

Table-3 - One-Sample Test
	Test Value = 3
					95% Confidence Interval of the Difference
	t	Df	Sig. (2-tailed)	Mean Difference	Lower	Upper
Q.2A	11.256	229	.000	.883	.73	1.04
Q.2B	8.970	229	.000	.730	.57	.89
Q.2C	4.193	229	.000	.296	.16	.43
Q.2D	-2.529	229	.012	-.174	-.31	-.04
Q.2E	-12.728	229	.000	-.904	-1.04	-.76
Q.2F	-2.092	229	.038	-.148	-.29	.00
Q.2G	.059	229	.953	.004	-.14	.15
Q.2H	-7.576	229	.000	-.543	-.68	-.40
Q.2I	-10.123	229	.000	-.796	-.95	-.64
Q.2J	-1.673	229	.096	-.152	-.33	.03
Q.2K	-46.768	229	.000	-1.726	-1.80	-1.65
Q.2L	-25.698	229	.000	-1.357	-1.46	-1.25
Q.2M	-36.970	229	.000	-1.591	-1.68	-1.51
Q.2N	4.822	229	.000	.391	.23	.55

· These finding of descriptive analysis also gets confirmed by one-sample ‘t’-test of inferential analysis, which also revealed the same results as are revealed by descriptive statistics about decision making of a rural woman in different animal, farms and the related activities. Activities for which descriptive statistics has shown good and better chances in decision making (except for Selection and procurement of seeds of new variety, Storage of green fodder for lean period for which null hypothesis is accepted) ‘t’-test of inferential analysis rejects null hypothesis for those activities and thus accepts alternate hypothesis for these activities hence showing women sometimes/frequently make decisions for these activities.

· For rest of the activities for which descriptive statistics has shown poor and limited chances in decision making,‘t’-test of inferential analysis accepts null hypothesis for those activities and thus rejects alternate hypothesis for these activities hence showing women never/rarely make decisions for these activities.

SUGGESTIONS:

Keeping in view the above mentioned problems/needs of the area and conclusions derived there from, the researcher has made a fair endeavor to suggest some points for the upliftment of the beneficiaries.

· Arrangements should be made to pool up the small products market it under a major brand name

The findings of the table above show that women frequently/always make decisions for selling of crops/cereals/ vegetables. Therefore the important suggested activity that emerges in marketing is to make efforts to organize these small farmers and make arrangements to market their pooled products under a major brand name so that it can be sold nationally and internationally. The same product when produced and sold by different farmer groups in small quantities under different brand names usually does not reach the international market. Organic products have a parallel market which, if captured in a strategic manner, can lead to improve the financial position of the female farmers thus leading to the rapid development of these hill districts. As the organically produced products are free from chemical ingredient, it can fetch better prices. It is very crucial to mention here that the benefits so generated should directly reach the producer i.e. females.

· Educational accessibility helps in understanding the things in the right perspective

One study in the introduction has shown that lack of technical know-how and education in women are the main constraints in taking decisions. Female farmers of the researcher’s study area also face these constraints in making decisions, consequently it is suggested that women should be provided with formal education so that they may get benefitted from modern scientific and technologic technique and not become victims of their ignorance and backwardness. It will also help them in understanding the things in the right perspective thus enabling them in making right decisions.

· Decision making authority on the animals they manage should be with the women only

In view of the fact that rural women traditionally play a very important role in raising livestock and in most cases they are solely responsible for the small animals kept near home. The researcher’s data regarding possession and gender-wise ownership of domestic animals confirms the fact of significant possession of milch and small sized livestock without the female ownership (only for poultry female ownership is noticed). At this point, it is suggested that the decision making authority on the animals they manage should be with the women only. Besides, it is easier to operate a productive enterprise with smaller animal and initial costs are lower. Profits may be low, but so are risks. Surely, women benefit most when they have decision making authority about the animals they manage even without legal ownership rights. Needless to mention here that women would be willing to work even harder if they could earn some more money from the livestock they manage.

· Women-specific development programmes should be encourage in the state

It is also suggested that women-specific development programmes should be encouraged in the state. Watershed development programme and Training for Women in Agriculture (TWA) are the programmes to benefit women. The watershed programme is a pro-women scheme, where women are expected to benefit more than men. These are either land conservation and water conservation and recharging programmes (that is dealing with ecological dimensions) or are also dealing with socioeconomic aspects. The latter are called watershed plus programmes. These programmes are women-specific leading to increase in women’s participation and therefore empowerment and status of women. So, these types of programmes should be encouraged.

Many studies have proved that empowering women with resource accessibility and giving them the independent right to make decision for the activities they are participating in, has helped them in improving their socio-economic condition. For example, in Zimbabwe, a major portion of GDP of which, comes from the agriculture sector, decision to allow women to sell produce directly to the Grain marketing Board, without the involvement of their husbands, has given them more control over their produce (Muchena 1994).

To sum it up we can say that by improving rural women's access to finance will give them a chance to become economically independent. By increasing their economic power, they will be able to organize themselves and to participate in decision making processes more efficiently and to draw up policies which concern them; as well as defending their own interests with public authorities and other relevant institutions which ultimately will help women to convert subsistence agriculture to commercial agriculture. Undoubtedly, by converting their role from passive recipients to active own managers helps them in improving their condition in the State.

REFERENCES:

1. Chaudhury Sarmishtha, 2004 .Invisible Activities of Rural Women, Kurukshetra, Vol. 52, No. 9, July 2004.

2. Sethi, Raj Mohini, 1991 .Women in Agriculture.Rawat Publications, Jaipur, Rajasthan.

3. Nandy, S.N. and Samal, P.K. 2005.An outlook of agricultural dependency in the IHR. ENVIS Newsletter: Himalayan Ecology 2 : 4-5.

4. Brara, N. Vijaylakshmi. (2006). A Situational analysis of women and girls in Manipur. New Delhi: National Commission for Women. 179 p.

5. Vaish, S. (1999). Involvement of rural women in decision making relating to rice production technology adoption in community development block Milkipur, Faizabad (U.P.) M.Sc. (Ag.) Thesis, NDUAT, Faizabad.

6. Prasad C. and Singh R.P., 1992 .Farm Women : A precious Resource. in Women in Agriculture, Vol. 2, Education, Training and Development edited by R.K. Punia, 1992, Northern Book Centre, Ansari Road, New Delhi.

7. Census of India (2011), available at http://censusindia.gov.in/ 2011-prov-results/prov_data_products_sikkim.html

8. Census of India, 2001. Economic characteristics of Indian Population, Office of the Registrar General, Government of India, New Delhi.

9. Muchena, O.N. 1994. The changing perceptions of women in agriculture.In M.R. Eicher and C.K. Eicher (eds.), Zimbabwe’s Agricultural Revolution. Harare, Zimbabwe: University of Zimbabwe Press.

Received on 24.04.2012

Revised on 10.05.2012

Accepted on 14.05.2012