A study on socio-economic profile of the respondents in dryland farming trainings provided by central research institute for dryland agriculture in Bhoothpur block of Mahbubnagar district in Telangana

Introduction

Agriculture remains the cornerstone of the Indian economy, providing livelihood for nearly 60% of the population and contributing substantially to the nation's Gross Domestic Product (GDP). Despite its significance, the agricultural sector faces numerous challenges, particularly in dryland areas where water scarcity and unpredictable rainfall patterns hinder productivity. Addressing these challenges is crucial for ensuring food security and sustainable development. The Central Research Institute for Dryland Agriculture (CRIDA) plays a pivotal role in advancing sustainable agricultural practices and enhancing the capabilities of extension functionaries, thereby contributing to the overall resilience and productivity of the agricultural sector.

Importance of Dryland Farming

Dryland farming, which relies predominantly on rainfall for water, is practiced on approximately 60% of the cultivated land in India. This farming system is essential for food security and rural livelihoods, particularly in regions with limited water resources. However, dryland farmers face significant constraints, such as soil erosion, nutrient depletion, and climate variability. Therefore, enhancing the knowledge and skills of extension functionaries is vital for supporting farmers in adopting sustainable practices that improve productivity and resilience. Addressing these constraints through effective training and support can lead to more stable and productive agricultural systems, even in challenging environments.

Study Area: Mahbubnagar, Telangana

Mahbubnagar, located in the southern part of Telangana, is characterized by a semi-arid climate with predominantly rainfed agriculture. The region's farmers largely depend on monsoon rains for cultivation, making it a pertinent area for studying the impact of CRIDA's training programs. Mahbubnagar's agricultural landscape includes diverse crops such as pulses, millets, oilseeds, and horticultural crops, all of which are crucial for the local economy and food security. The selection of this area for the study is significant due to its typical dryland farming conditions, providing a comprehensive understanding of the effectiveness of training programs in such environments. (Naidu et al., 2016) [14] .

Significance of the Study

The findings of this study will provide valuable insights into the farmers participation in training programmes of CRIDA in Mahbubnagar by analysing the socio-economic profiles. Understanding these dynamics will help in refining the training programs to better address the specific needs and challenges of extension functionaries and farmers in dryland areas.

Statement of the problem

The study seeks to evaluate the Socio-Economic profile of the Respondents towards Dryland Farming Training programme of Central Research Institute for Dryland Agriculture (CRIDA), with a focus on the Mahbubnagar district of Telangana. This research aims to investigate the socio-economic profiles of the respondents and know their Exposure towards Dryland Farming. The insights gained will be instrumental in refining training programs, developing targeted interventions, and ultimately improving the support provided to dryland farmers. This comprehensive understanding will help in determining the socio-economic characteristics of extension workers.

Justification

The study aims to evaluate the Socio-Economic profile of the farmers towards Dryland Farming training programme of CRIDA in Mahbubnagar, Telangana, by examining their socio-economic profiles. Insights gained will refine training programs and develop targeted interventions, ultimately enhancing support for dryland farmers. The findings will contribute to increased agricultural productivity, sustainability, and resilience in dryland farming communities.

Objective

To assess the socio -economic profile of the respondents.

Review of literature

Socio -Economic status of the respondents Kondylis and Mueller (2014) [9] found that 70% of those with higher education and 65% of wealthier farmers were significantly more likely to participate in training programs. These individuals also showed a higher propensity to adopt new agricultural innovations compared to their less educated and poorer counterparts. Kumar and Sharma (2014) [10] showed a 60% increase in knowledge about advanced irrigation methods among participants. The study also noted that participants with higher education levels demonstrated a 40% higher adoption rate of these methods. Patel and Mehta (2014) [16] revealed that farmers with higher education (75%) and larger landholdings (80%) were significantly more likely to implement new technologies. Income level also played a crucial role, with wealthier farmers being 65% more likely to adopt innovations. Singh and Kumar indicated that 55% increase in understanding among participants. Those with higher education and greater landholdings showed a 70% higher adoption rate of the techniques taught. Yadav and Varma (2014) [22] revealed that 78% of farmers with secondary education or higher and 66% of those with better economic status were more inclined to adopt sustainable agricultural practices introduced during the training sessions. These figures highlight the importance of socio-economic status in the adoption of new techniques. Das (2015) [3] found that 60% of women with higher education and 70% of those with better resource access reported greater benefits from the training. These women were 50% more likely to implement new agricultural practices. Murtaza and Aslam (2015) [13] revealed that younger farmers (under 35) with higher education (80%) and better economic status (75%) were significantly more receptive to training programs. These farmers were also 60% more likely to adopt new agricultural techniques. Chand and Kumar (2016) [2] Indicated that Research on the National Food Security Mission (NFSM) was that wealthier (65%) and better-educated farmers (75%) were more likely to benefit from the training programs. These farmers showed a 50% higher adoption rate of new techniques. Jadhav and Pawar (2016) [8] showed that an assessment of CRIDA training's impact on precision farming knowledge among Maharashtra farmers showed a 50% increase in understanding and a 40% higher implementation rate of GPS and GIS-based farming methods among participants. Meena (2016) [12] highlighted that an analysis of Krishi Vigyan Kendra's (KVKs) training programs highlighted that age, education, and land ownership significantly influenced technology adoption. Farmers under 40 with secondary education or higher (78%) and larger landholdings (70%) showed higher adoption rates. Mishra revealed that a study on the National Agricultural Innovation Project (NAIP) training programs revealed that younger (under 40) and better-educated farmers (70%) with larger landholdings (65%) had higher adoption rates of the technologies introduced during the training. Rao and Reddy found that a study on the attitudes of Andhra Pradesh farmers towards CRIDA training on soil health management found that 65% of participants had a favorable perception. The adoption rate of recommended practices was 60% higher among those with secondary education or higher and larger landholdings. Choudhary and Singh found that an evaluation of the knowledge enhancement of farmers trained by CRIDA on soil health management indicated a 55% improvement in understanding of soil testing and balanced fertilizer application. Participants with higher education showed a 45% higher adoption rate. Ghosh and Sen found that an analysis of the influence of socio-economic factors on the effectiveness of CRIDA training found that younger farmers (under 40) with higher education (75%) and better financial status (70%) had stronger impacts. These farmers were 50% more likely to adopt the practices taught. Lal and Khurana (2017) [11] observed that in Rashtriya Krishi Vikas Yojana (RKVY) training programs, it was stated that 70% of better-educated farmers and 65% of wealthier farmers were significantly more engaged. These farmers showed a 50% higher rate of implementing the introduced agricultural practices. Verma and Singh (2017) [21] found that a study on the attitudes of Uttar Pradesh farmers towards CRIDA training on climate-resilient agriculture found that 60% of participants had positive attitudes and a 55% higher commitment to adopting the recommended practices, particularly among those with higher education and larger landholdings. Rao and Reddy found that an investigation of the influence of socio-economic factors on the effectiveness of CRIDA training programs found that younger farmers (under 40), better-educated (75%), and wealthier farmers (70%) experienced stronger impacts and higher adoption rates. Nair and Thomas (2019) [15] found that an analysis of how www.extensionjournal.com socio-economic characteristics influenced farmers' attitudes towards CRIDA training found that 65% of farmers with higher education and 70% with better financial status had more positive attitudes and a 50% higher likelihood of adopting new practices. Patel and Desai (2023) [4] revealed that a study on the effect of socio-economic characteristics on the adoption of precision farming techniques taught by CRIDA revealed that better-educated (75%) and wealthier farmers (70%) had significantly higher adoption rates. These farmers showed a 55% greater implementation of precision farming method and 70% with better financial status had more positive attitudes and a 50% higher likelihood of adopting new practices.

Research methodology Research Design

It is a blue print of the detailed procedures for testing the hypothesis and to analyze the collected data (Singh, 1986). The design of the present study was descriptive one based on survey method. The study attempts to describe and analyze the role of "A study on Constraints faced by the farmers in Dryland Farming Trainings provided by CRIDA".

Locale of Study

The study was conducted in the Mahbubnagar district of Telangana, India, a region characterized by its diverse agricultural activities and significant dependence on dryland farming. Mahbubnagar is geographically positioned at a latitude of 16.737509 and a longitude of 78.008125. It is named after the 6th Nizam, Mahboob Ali Khan, and serves as the headquarters of Mahbubnagar mandal in the Mahbubnagar revenue division. The district is notable for its significant population and area, encompassing various topographies and soil types.

Sampling and Sampling Procedures Selection of Districts

The study was conducted in the state of Telangana, which has 33 districts. Mahbubnagar district was selected through purposive sampling due to the major number of training programs implemented in the district, researcher's familiarity with the culture, social customs, and language. This familiarity facilitates close liaison with the respondents and ensures the collection of reliable information.

Selection of Block

Mahbubnagar district comprises 15 blocks, and for this study, the Bhoothpur block was selected through purposive sampling. Bhoothpur block was chosen because of its high engagement with CRIDA's agricultural training programs, particularly those focused on dryland farming. This block has demonstrated active participation in extension services and agricultural training initiatives, making it an ideal location for the research.

Selection of Villages

Bhoothpur block consists of numerous villages. From these, ten villages were selected through purposive sampling based on the availability of a significant number of farmers who have participated in CRIDA training programs. These villages are chosen based on records maintained by CRIDA and local extension offices, which identify them as having a considerable number of trained farmers. The selected villages are Bhoothpur, Amisthapur, Bijinapalle, Chandapur, Kothapalle, Tadparthy, Ippalpalle, Ravalapalle, Maddigatla, Peddarevally.

Selection of Respondents

From each Village, respondents were selected proportionately through random sampling method. Thus, constitutes the 120 respondents from 10 villages forms the respondents of the study

Results and Discussion

Socio -Economic status of the respondents Age The table illustrates that a significant majority of respondents (50.83%) belong to the middle age category of 36-55 years. This demographic predominance suggests that a substantial portion of participants at CRIDA are in their productive years, likely contributing to active engagement and a readiness to adopt new agricultural practices. Additionally, 39.17% of respondents are categorized as young (<35 years), indicating a notable presence of younger individuals who are actively involved in agricultural activities or extension services. Conversely, 10.00% of respondents are classified as old (>55 years), highlighting the valuable participation of experienced professionals and senior farmers in agricultural initiatives at CRIDA. These findings align with studies such as those by Patel and Swanson (2015) [17] , which emphasize that middle-aged farmers, particularly The table presents the distribution of respondents based on their caste among 120 participants in agricultural extension programmes at CRIDA. It shows that the majority of respondents (49.16%) belong to the General category, followed by 24.16% from Other Backward Classes (OBC), Table presents the distribution of respondents based on the size of their family among 120 participants in agricultural extension programs at CRIDA. It categorizes respondents into three groups: small families (up to 4 members), medium families (5-8 members), and large families (above 8 members). The table shows that 42.50% of respondents come from small families, 49.20% from medium-sized families, and 8.30% from large families. These findings highlight the varying family sizes among participants, which can influence agricultural practices, resource allocation, and decision-making within farming households involved with CRIDA. Similar insights into the impact of family size on agricultural dynamics have been explored by researchers such as Mishra and Singh.

Distribution of the respondents on the basis of their Education Caste

Distribution of respondents on the basis of size of family [7] . www.extensionjournal.com

Family Type

Distribution of respondents on the basis of types of family

Housing Pattern Table presents the distribution of respondents according to their housing patterns. The data reveals that the majority of respondents (40.87%) live in semi-cemented houses, followed by those living in cemented houses (33.33%). A significant portion (25.80%) still resides in huts (kachcha).

The high percentage of semi-cemented and cemented houses indicates progress towards better living conditions, although a notable proportion still inhabits more rudimentary structures.

Distribution of respondents on the basis of housing pattern Landholding Family size data reveals that 42.50% of respondents come from small families (up to 4 members), 49.20% from medium-sized families (5-8 members), and 8.30% from large families (above 8 members). This variation in family sizes influences agricultural practices, resource allocation, and decision-making within farming households. Family structure analysis shows that 65.80% of respondents belong to nuclear or single-family households, while 34.20% are from joint families, impacting decision-making and the adoption of agricultural innovations.

Conclusion

It is concluded that the Farmers Participation in Training Programme of CRIDA reveals a detailed socio-economic profile of participants, highlighting diverse age, education, and caste backgrounds. The socio-economic profile of majority respondents found to be at medium level.

References

1. Bhatia P, Swaminathan M. Challenges in delivering effective training to extension functionaries: Logistical issues and inadequate resources. J Agric Train Dev. 2023;28(1):45-62.
2. Chand R, Kumar P. Socio-economic profiles of farmers in the National Food Security Mission (NFSM): A comparative analysis. Indian J Agric Econ. 2016;73(2):134-48.
3. Das P, Roy A, Patel V. Mahila Kisan Sashaktikaran Pariyojana (MKSP) training program's impact on women's agricultural practices. Women Agric J. 2015;28(2):105-18.
4. Desai R, Patel S, Mehta A. Relevance and impact of CRIDA's trainings on technical skills of extension officers. Agric Train Rev. 2011;30(3):145-58.
5. Gupta R, Singh R. Adoption rates of sustainable farming practices by extension workers after attending CRIDA's workshops. J Agric Ext. 2013;20(4):55-72.
6. Gupta V, Goyal R. Adoption of water-saving technologies under the Pradhan Mantri Krishi Sinchai Yojana (PMKSY). Water Manag J. 2016;25(1):56-68.
7. Gupta V, Sharma P. Motivation and equipment of extension officers post-CRIDA training. J Agric Ext Manag. 2018;37(3):218-31.
8. Jadhav A, Pawar P. Precision farming knowledge enhancement through CRIDA training. Precis Agric Rev. 2016;18(2):87-102.
9. Kondylis F, Mueller V. Participation in training programs among Ethiopian farmers and adoption of agricultural innovations. Afr Agric Stud. 2014;27(1):45-60.
10. Kumar P, Patel R. Satisfaction with practical demonstrations in CRIDA's training programs. Agric Educ Train J. 2013;29(2):123-37.
11. Lal R, Khurana A. Engagement and practice implementation post-Rashtriya Krishi Vikas Yojana (RKVY) training programs. Agric Policy Rev. 2017;31(3):189-202.
12. Meena BS, Gupta P, Reddy V. Factors influencing technology adoption through Krishi Vigyan Kendra (KVK) training programs. Technol Adopt Agric. 2016.
13. Murtaza N, Aslam M. Socio-economic profiles of Pakistani farmers and their receptivity to training programs. Pak J Agric Ext. 2015;29(1):67-80.
14. Naidu R, Kumar V, Rao M. Evaluating CRIDA's training programs in Mahbubnagar: Impact on semiarid agriculture and rainfed farming. J Agric Train Dev. 2016;28(3):157-70.
15. Nair P, Reddy M. Appreciation of logistics and accessibility in CRIDA's training programs. Agric Logist J. 2022;28(4):212-25.
16. Patel R, Kumar S. Impact of CRIDA training on farmers' knowledge about drought management techniques. Drought Manag Rev. 2016;23(3):167-80.
17. Patel R, Swanson B. Study on the engagement of middle-aged farmers in agricultural innovations. J Agric Ext Rural Dev. 2015.
18. Reddy P, Nair S. Professional development benefits of CRIDA's training programs. Prof Dev Agric. 2015;30(3):145-58.
19. Reddy S, Kumar P, Mehta V. Integration of digital tools in CRIDA's training modules. Digit Agric J. 2022;36(1):123-35.
20. Sharma P. Frequency of extension contacts among farmers. J Agric Ext. 2016.
21. Sharma P, Gupta V. CRIDA's training sessions' impact on dryland farming knowledge. Dryland Farming Rev. 2012;28(3):178-90.
22. Verma S, Singh R. Attitudes towards CRIDA training on climate-resilient agriculture. Clim Resil Agric. 2017;31(2):123-36.
23. Yadav R, Varma S. Adoption of sustainable agricultural practices under the National Mission for Sustainable Agriculture (NMSA). Sustain Agric J. 2014;26(4):23345.