Status of rural women in the context of farm technologies advancement in Haryana

Introduction

Rural women historically had limited access to contemporary agricultural technology, frequently as a result of cultural and patriarchal norms that hindered their ability to acquire resources and participate in decision-making (Rehman, 2000) [6] . Nonetheless, women's participation in the agricultural and related fields has significantly increased in recent years (Mallik et al., 2009) [3] . Women have historically played a minor role in decision-making, despite their substantial commitment to agricultural work, which reflects their lower social standing in rural households (Dudi et al., 2017) [1] . Scholars contend that in order to achieve gender parity in rural development, cultural views toward acknowledging and appreciating women's responsibilities and contributions must change dramatically, affecting both men and women (Rani, 2017; Singh et al., 2020) [5,7] . Innovation in technology is essential to raising agricultural output. Effective technology in rural areas must be properly customized, which means it must be scientifically sophisticated yet adjusted to the sociocultural and environmental context of the area. Crucially, evaluating the practical efficacy of such technical advancements depends on their gendered effects. Research indicates that boosting rural women's use of technology can have a transformative effect, strengthening women's autonomy and well-being, expanding job options, and reducing the gender gap in agriculture (Quisumbing et al., 2014; FAO, 2011) [4,2] .

Objective of the study

The goal of the current study was to investigate how women's roles and status are affected by the use of technology.

Methods

Locale of the study:

The study will be carried out in Haryana state purposively as the state has witnessed fast technological advancement.

Selection of the districts:

The study will be conducted in two districts selected purposively, Kurukshetra from high developed districts and Mahendragarh from low-developed districts using the criteria of the agricultural and socio-economic composite index of development.

Selection of the blocks:

From each selected district, two blocks will be selected randomly.

Selection of the villages:

From each selected block, three villages will be selected at random. Thus, a total of twelve villages will be included in the present study.

Selection of the respondents:

A sample of 30 women in the age group of 20-45 years will be selected randomly from each village, thus making a total sample of 360 rural women. Age is significantly correlated with the degree of farm technology use, according to the chi-square value (χ² = 9.688*). The age range of 38-45 has the highest adoption rate (59.5%), followed by 29-37 (52.67%) and 20-28 (40.74%). Because they have more expertise, decision-making power, and access to resources, older farmers may be more likely to adopt cuttingedge technologies. Younger farmers have a moderate level of adoption, suggesting that more focused training and assistance are required to increase their involvement with contemporary farming methods. The use of agriculture technology and caste hierarchy are significantly correlated, according to the chi-square value (χ² = 20.503*). The adoption rate is significantly lower among Backward Classes (29.85%) and Scheduled Castes (25.53%), whereas the greatest adoption rate is among general category farmers (51.63%). Better access to resources, training, and support networks may be the reason for higher adoption in the General category. The necessity for inclusive policies and focused interventions to eliminate caste-based gaps in the use of agricultural technology, on the other hand, is highlighted by the structural obstacles marginalized groups must overcome. The adoption of farm technology and education level are significantly correlated, according to the chi-square value (χ²=20.265*). The greatest adoption rate (60.20%) and lowest low adoption (10.70%) are found among respondents with higher education (graduation and above), whereas the lowest high adoption (44.60%) and highest low adoption (36.50%) are found among those who are illiterate. This trend highlights the significance of education in fostering agricultural innovation and reducing the knowledge gap among farmers by indicating that education improves awareness, access, and confidence in employing farm technology. The use of farm technology and family education are significantly correlated, according to the chi-square value (χ² = 29.267*). The highest high adoption rate (55.60%) and the lowest low adoption rate (14.80%) are seen among families with higher education levels (Graduation and above). Illiterate families, on the other hand, have the greatest low adoption rate (40.60%) and the lowest high adoption rate (23.40%). According to this tendency, a family atmosphere with higher levels of education has a favorable impact on awareness, decisionmaking, and confidence when it comes to implementing farm technology, which encourages innovation and agricultural growth. Type of Family and Adoption Scope (χ²= 8.239) The use of farm technology and family type have a modest relationship, according to the chi-square value. Joint families had a higher adoption rate (30.23%) than nuclear families (18.18%), indicating that adoption may be encouraged by shared duties and resources. Nonetheless, a greater percentage of low adoption (28.57%) is reported by nuclear families, suggesting potential shortages in funding, personnel, or assistance with decision-making. Adoption Extent and Family Size (χ²=10.282) Technology adoption and family size are more significantly correlated. The highest high adoption rate (35.35%) and lowest low adoption rate (14.14%) are shown by small households (up to 4 members), which suggests improved resource availability and decision-making effectiveness. The low adoption rates (~29%) for medium and big families are similar, indicating that adoption may be hampered by resource pressure and coordination issues after a certain size. A substantial correlation between **annual family income** and **adoption of farm technologies** is indicated by the chisquare value (χ²=16.590*). The **highest adoption rate (55.37%) ** and **lowest low adoption (5.79%) ** are found in households with incomes over ₹4 lakh, suggesting that adoption is significantly supported by financial ability. On the other hand, adoption rates are lower (23.94%) for families with incomes under ₹2 lakh. Economic position is therefore a major factorbetter access to and application of farm technology are made possible by higher wealth. A statistically significant correlation between **subsidiary family occupation** and the **extent of agricultural technology adoption** is indicated by the chi-square value (χ² = 16.962*). The highest rates of high adoption (38%) and lowest rates of low adoption (12.90%) are seen in households with **government service**, indicating that steady income encourages the use of technology. On the other hand, due to budget limitations, **agriculture labour families** exhibit the lowest adoption rate (58.10%). Although they are underrepresented, artisan and small business families exhibit moderate to low adoption rates. Therefore, adoption behavior is greatly influenced by work. **Landholding size** and **farm technology adoption** are significantly correlated, according to the chi-square value (χ² = 17.701*). Better investment ability is shown by the highest high adoption rate (63.89%) and lowest low adoption rate (5.56%) reported by larger landholders (above 25 acres). On the other hand, because of resource constraints, marginal farmers have a larger percentage of low adopters (29.09%). In Kurukshetra, landholding has a favorable impact on availability, affordability, and willingness to embrace agriculture technology, as seen by the adoption rate rising steadily with land size. A **significant relationship** between the **extent of agricultural technology adoption** and mass media exposure is shown by the chi-square value (χ² = 4.653*). The rates of high adoption (16.20%) and low adoption (10%) are higher in households with **high media exposure**, indicating that they make well-informed decisions. Conversely, the lowest adoption rate (55%) is seen in people with **low exposure**, suggesting a lack of awareness. This pattern emphasizes how important the media is for spreading agricultural breakthroughs and promoting adoption through increased awareness and outreach. The use of farm technology and socioeconomic class have a **strong significant relationship**, according to the chi-square value (χ² = 28.370*). Because they have more access to resources and knowledge, respondents in the **high SES group** had the greatest adoption rate (76.30%) and the lowest medium adoption rate (7.40%). Conversely, **low SES households** exhibit greater medium and low adoption rates and a relatively lower high adoption rate (51.30%), indicating a restricted ability to invest in new technology. This emphasizes how important SES is in determining adoption behavior.

Results and Discussion

Conclusion

In summary, while socio-economic factors such as education, income, landholding, caste, family structure, and occupation play a crucial role in enabling rural women to adopt technology, their impact remains inconsistent. These elements have contributed to greater involvement in agricultural and household advancements, fostering empowerment and improving decisionmaking abilities. However, the full benefits of technological adoption can only be achieved by addressing the existing gaps in awareness and education. Many women still lack sufficient knowledge, training, and confidence to independently access and utilize modern technologies. To bridge this divide, in addition to economic and structural reforms, focused initiatives on skill development, gender-sensitive extension services, and grassroots awareness programs are essential. Providing women with education and accessible information will not only enhance their participation in the development process but also promote a more inclusive and sustainable transformation in rural communities.

References

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