Socio-economic profile of rural farm women and their decision making pattern in agricultural activities in Mulugu district of Telangana

Women in general play a pivotal role in agriculture in Telangana state. They carry out various activities in agriculture like cleaning of fields, preparation of field, sowing, intercultural practices, weeding, harvesting, picking, cleaning of grains, drying of grains and so forth. But, unfortunately, they remain as invisible people. However, their involvement as decision makers regarding these activities is questionable. Hence the study was carried out to analyze the contribution of rural farm women in decision - making in agriculture. For this the primary data was collected through structured questionnaire using a sample size of 120 respondents from eight villages of two mandals i.e Mangapet and Eturnagaram of Mulugu district. From each village 15 respondents were selected and from each mandal 4 villages were selected. The results of the study revealed that the majority 50.84 percent of the farm women were middle-aged between 36 to 50 years and most of the farm women were illiterates (44.17%). Majority of the farm women maintain nuclear family with 4-6 members. Most of the farm women belonged to small- medium landing categories who are involved in agricultural activities. The annual income of the majority of the respondents ranged between Rs. 50,001/- 1,00,000/-. The participation of farm women was recorded as both family labor and paid wage labor as agricultural workers in this study. It was observed that the joint decision-making power of farm women was highest in utilization of farm income and lowest in irrigation management.

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Introduction

India is an agricultural country, with an economy that is preliminary agrarian in nature. India ranks second in the world with a population of 139 crores, 64.6 crores of which are females (Census of India, 2021) [3] . In rural India, agriculture and allied industrial sectors employ 89.5 percent of all female labor (Singotiya et al. 2014) [22] . Agriculture plays a vital role in Indian economy and contributes 17 percent to the total GDP (Meshram et al. 2019) [11] . Women make significant contributions to the agricultural and rural economies in all developing countries and contribute one-third of the labor required for farming oactivities. About 70 percent of agricultural work was carried out by women (Ahuja and Narayan, 2016) [1] . At present almost all rural development activities of the government are centered on male and female farmers. However, it is not only the male farmer alone who needs to be changed to attend to the growth and development of rural India but rural women also need to be involved in the developmental activities as they are the backbone of Indian agriculture. More than 60% of the rural population of Telangana is employed in agriculture and its allied activities and it is clear that, in Telangana, the agriculture sector is a major contributor to the GSDP as the Gross State value added (GSVA) of the agriculture and allied sector has seen a growth of 186% from its formation. Mulugu district has the highest share of rural population (96.10%). It also has the highest percentage of agricultural laborers (64.8%) (Telangana Socio Economic Outlook, 2023) [24] . The participation of rural women in co-operation with their husbands in decisions making not only affects family but farm business too. Women usually contribute to the harvesting of crops, weeding, threshing, irrigation, plant protection, use of bio-fertilizers, supervision and postharvest operations etc. Women work with male members and participate in agricultural activities putting much of hours of productive manual family labor, in rural families women always work much more than males.

Materials and Methods

The study was carried out in Mulugu district which was selected purposively from the Telangana State as it has the highest share of rural population (96.10%). It also has the highest share in agricultural labourers (64.8%). Mulugu www.extensionjournal.com district comprises of nine mandals out of which two Mangapet and Eturnagaram were selected. Under each mandal four villages were selected randomly by using simple randon sampling method, the selected villages in Mangapet Mandal were Kamalapuram, Komatipally, Gamponigudem, Narsimhasagar and the selected villages in Eturnagaram Mandal were Gogupally, Chennaboinapally, Shivapuram and Ramannagudem. From the selected eight villages, 15 women respondents from each village were selected purposively. The criterion for selection of rural farm women was their participating in participating in agricultural activities of major crops such as Rice, Cotton, maize, chilli etc. The total sample selected for the study was 120, thus comprising of 60 samples from four villages of Mangapet Mandal and 60 samples from four villages of Eturnagaram mandal of Mulugu district.

Results and Discussion Age

It can be seen from Table 1 that majority (50.84%) of the respondents belonged to middle age followed by young age (30.00%) and old age (19.16%). Majority of the respondents belonged to middle age may be due to the reason that old aged women were unable to perform the tedious agricultural activities due to their physical weakness. The findings are in accordance with the work of Singh (2017) [21] and Kiran et al. (2018) [8] , Waris et al. (2020) [27] , Sneha et al. (2022) [23] and Paradkar et al. (2022) [16] .

S. No.	Age (in years)	F	%
1.	Young (15-35)	36	30
2.	Middle(36-50)	61	50.84
3.	Old (45-59)	23	19.16
	Total	120	100.00

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Distribution of rural farmwomen according to their age

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Education

The data presented in table 2 indicated that nearly (44.17%) of the respondents were illiterates followed by primary school education (14.17%), middle school education (15%), high school education (13.33%) followed by intermediate education (8.33%) and graduation (5%). These findings are supported by the work of Verma and Singh (2019) [11] , Waris et al. (2020) [27] , Sneha et al. (2022) [23] .

S. No.	Educational Level	F	%
1.	Illiterate	53	44.17
2.	Primary School	17	14.17
3.	Middle School	18	15
4.	High School	16	13.33
5.	Intermediate	10	8.33
6.	Graduate	6	5
	Total	120	100.00

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Family Type

From Table 3 we can see that majority (80.84%) of the respondents belonged to nuclear families followed by joint families (19.16%). This could be due to the reason that there is a cultural shift and the joint family system even in villages is on a decline as reflected from this study too.

S. No.	Family type	F	%
1.	Nuclear	97	80.84
2.	Joint	23	19.16
3.	Extended	0	0.00
	Total	120	100.00

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Similar results were quoted by Machhaliya (2014) [10] , Mohanta (2017) [13] , Kiran et al. (2018) [8] and Paradkar et al. (2022) [16] .

Family Size

It can be seen from table 4 that majority (73.34%) of the respondents had medium family size (4-6 members) followed by 26.66 percent of the respondents who had small families (Up to 3 members). This could be because most of the families desire to have medium-sized families in order to improve their quality of life and economic standards by adhering to small family norms. The findings were in support with the work of Landage (2011) [11] , Sneha et al.

S. No.	Family size	F	%
1.	Small (Up to 3 members)	32	26.66
2.	Medium (4 to 6 members)	88	73.34
3.	Large (7 to 9 members)	0	0.00
4.	Very Large (10 above)	0	0.00
	Total	120	100.00

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(2022) [23] and Paradkar et al. (2022) [16] . Large (7 to 9 members) 0 0.00 4.

Very Large (10 above) 0 0.00 Total 120 100.00

Land Holding

The findings in Table 5 indicated that majority (35.84%) of the respondents had small-medium landholding (2.00-4.00 acres) followed by 25.84 percent having small land holding (1.00-2.00 acres), 21.66 percent of the respondents had marginal landholding (below 1 acre), 15.00 percent of the respondents had medium land holding (4.00-10.00 acres) and only 1.66 percent of the respondents had large land holding (>10 acres). This might be due to the fact that the division of inherited landholdings from generation to generation led to most of the large land holdings turning into small, small-medium and marginal. The results were consistent with that of Kaur et al. (2017) [8] .

S. No.	Categories	F	%
1.	Marginal- (Below 1 acre)	26	21.66
2.	Small- (1.00-2.00 acres)	31	25.84
3.	Small-Medium-(2.00-4.00 acres)	43	35.84
4.	Medium-(4.00-10.00 acres)	18	15
5.	Large-(>10.00 acres)	2	1.66
Total		120	100.00

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Annual Income

The results in the Table 6 clearly indicated that majority (39.17%) of the respondents belonged to the income range of Rs. 50,001 to Rs. 1,00,000/-followed by (25.8%) belonged to the range of up to Rs. 50,000/-, (21.67%) of the respondents belonged to range of Rs. 1,00,001 to 1,50,000/www.extensionjournal.com and (13.33%) of the respondents belonged to a range of Rs. 1,50,001/-2,00,000/-.The main reason for majority of the respondents having an income range between Rs. 50,001 to Rs. 1,00,000 may be because agriculture was their major occupation along with agricultural labour and they are not earning high income from these occupations. The findings were in line with the findings of Jain (2007), Waris et al.

S. No.	Categories	F	%
1.	Up to Rs 50,000/-	31	25.83
2.	Rs. 50,001 to Rs. 1,00,000/-	47	39.17
3.	Rs. 1,00,001 to Rs.1,50,000/-	26	21.67
4.	Rs. 1,50,001 to Rs. 2,00,000/-	16	13.33
	Total	120	100.00

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(2020) [27] , Prathibha et al. (2020) [18] . Occupation: It can be observed from Table 7 that for majority (54.17%) of the respondents agriculture along with agricultural labor was the primary occupation followed by agriculture alone (17.5%), agriculture along with allied occupation (15.83%), agricultural laborers (12.5%) and none of them belonged to agriculture with job and agriculture with small business categories. Similar findings were quoted by Machhaliya (2014) [10] , Mohanta (2017) [13] and Kiran et al. (2018) [8] , Paradkar et al. (2022) [16] .

S. No	Categories	F	%
1.	Agriculture along with Agricultural labor	65	54.17
2.	Agriculture alone	21	17.5
3.	Agricultural labor	15	12.5
4.	Agriculture along with allied occupation	19	15.83
5.	Agriculture along with job	0	0.00
6.	Agriculture along with small business	0	0.00
	Total	120	100.00

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Table 7: Distribution of respondents according to their occupation

Agriculture along with Agricultural labor 65 54.17 2.

Agriculture alone 21 17.5 3.

Agricultural labor 15 12.5 4.

Agriculture along with allied occupation 19 15.83 5.

Agriculture along with job 0 0.00 6.

Agriculture along with small business 0 0.00 Total 120 100.00 With regard to decision making of farm women in agricultural activities, it can observed from findings in table 8 that decision score was highest in availing government schemes like Anganwadi and MNERGA (4.68 and 4.47), followed by utilization of farm income (4.42), purchase, sale and lending of agricultural land (3.91), agricultural loans (3.44), bank loans(3.36), management of labor (3.28), monitoring of crop (3.22), purchase, sale and lending of farm machinery implements or agricultural land (3.05), time of harvesting (2.95), time of sowing (2.9), home need loans (2.78), storage of farm produce (2.63), selection of crop (2.52),marketing the produce. (2.41), type and number of crops to be taken in a crop season (2.32),contact with extension workers regarding improved package of practice (1.67), variety of seeds (1.63), area allocation to each crop (1.58), purchase of inputs seeds, fertilizers, pesticides, insecticides etc, credit loans (1.46) and Irrigation management (1.38). Though farm women perform many crucial activities in farm activities their participation in decision making with respect to type and number of crops to be grown, choice of crop variety and purchase of inputs is not very encouraging, www.extensionjournal.com With the increasing feminization of agriculture it is very important to involve farm women in each of the decisions with respect to agricultural operations. There is an urgent need to create awareness and organize training programs for farm women to be assertive to be involved fully in the decision making for both farm and home activities. Godara et al. (2014) [5] revealed that women's participation in different aspects of decision-making process of agricultural sector have not recorded up to the mark. The decisions related to opting the measures to increase the production, buying activities, expenditure activities were found to be minimum. The relationship between the socio-economic characteristics (table 9) indicate that that the variables like land holding, family income, occupation exhibited positive and significant relationship with the decision making of respondents in agricultural activities. Whereas, variables like age, education family type and family size did not show any relationship with the decision making of farm women in agricultural activities. It may be due to the reason that age alone may not reflect the range of experience and majority of the farm women were illiterates in this study. Even if the farm women were educated their decision was not given importance due to dominance by males. Hence, there is a need for empowerment of farm women through extension education activities for effective and efficient decision making in agricultural activities.

S. No.	Independent variables	Decision making in Agricultural Activities
1.	Age	0.108NS
2.	Education	0.103NS
3.	Family Type	0.1774NS
4.	Family Size	-0.006NS
5.	Landholding	0.294**
6.	Family Income	0.272**
7.	Occupation	0.380**

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Co-relation between socio-economic profile of rural farm women in Decision making towards agricultural activities

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S. No.	Area of decision making	NI (1)	OS (2)	OC (3)	JD (4)	ID (5)	Score (Max.5)
1.	Selection of the crop	57	22	18	18	5	2.52
2.	Type and number of crops to be taken in a crop season	68	15	20	11	6	2.32
3.	Variety of seeds	102	6	3	5	4	1.63
4.	Area allocation to each crop	106	3	2	5	4	1.58
5.	Time of sowing	44	22	19	30	5	2.9
6.	Purchase of inputs like seeds fertilizer, pesticides, insecticides etc.	108	2	3	4	3	1.52
7.	Source of purchase of the inputs	72	11	13	21	3	2.32
8.	Management of Labor	41	12	10	52	5	3.28
9.	Taking advise from extension workers regarding improved package of practice	103	2	3	9	3	1.67
10.	Monitoring of crop	46	10	4	56	4	3.22
11.	Irrigation management	115	0	1	0	4	1.38
12.	Time of harvesting	43	20	19	35	3	2.95
13.	Storage of the farm produces	61	14	9	33	3	2.63
14.	Marketing the produce	67	16	10	23	4	2.41
15.	Utilization of family/farm income	13	13	5	57	32	4.42
16.	Purchase, sale and lending of farm machinery implements or agricultural land	47	15	8	46	4	3.05
17.	Purchase, sale and lending of agricultural land	17	16	9	75	3	3.91
18.	Availing Government schemes like
	MNERGA	19	4	2	61	34	4.47
	Anganwadi	9	10	18	30	53	4.68
	Credit	110	2	3	2	3	1.46
	Bank Loans	39	15	11	41	14	3.36
19.	Financial Issues like
	Home need loans	55	19	3	39	4	2.78
	Agricultural loans	29	20	15	50	6	3.44
	Mean	59.60	11.69	9.05	30.57	9.09	2.77

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Decision making pattern of farm women in agricultural activities

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Co-relation between the socio-economic profile of rural farm women in Decision making towards agricultural activities

Co-relation between the socio-economic profile of rural farm women in Decision making towards agricultural activities

Conclusion

It is concluded from the study that majority of the farm women had low involvement in decision making in agricultural activities like variety of seeds, area allocation to each crop, purchase of inputs like (seeds, fertilizers, insecticides, pesticides), taking advice from extension workers regarding improved package of practice, irrigation management and credit loans. It is revealed from the study that some of the farm women decisions were considered after consultation with the male members of the family in case of purchase, sale and lending of agricultural land, availing schemes like MNERGA, utilization of farm income, monitoring of crop and a very less number of farm women were involved in decisions independently on availing Anganwadi scheme. It is shown from the study that the illiterates were high and that is the reason farm women are not able to take decisions independently in agricultural activities. It is also shown from the study that landholding, family income and occupation had positive significant on decision making. This study would be valuable for future agricultural practice and would serve as a reference for gender-based agricultural policy work. Future research can be conducted with more numbers of independent variables in a broader perspective.

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