Factors influencing farmers perception of Agri-tech Startup: Farmer Producer Organizations (FPO) integration for improved extension advisory services

Introduction

India is strengthening its position in global agriculture, yet about 82% of farmers are small or marginal landholders who face low productivity and persistent poverty. Fragmented holdings limit adoption of innovative practices, mixed cropping, and economies of scale, while restricting access to quality inputs, advanced seeds, and modern technologies (Adhikari et al., 2021) [1] . Integrating farmers into collective structures is essential to overcome these barriers (Vadivelu and Kiran, 2013) [20] . Farmer Producer Organizations (FPOs) have emerged as transformative models, legally recognized as cooperatives or producer companies, owned and governed by farmers. They enable collective procurement, produce aggregation, and access to finance and institutional support (Kurien, 2004) [11] , empowering smallholders and enhancing market participation (Markelova et al., 2009) [12] . Agri-tech startups complement FPOs by providing digital tools, precision technologies, crop monitoring, mobile advisory services, and smart supply chains. Partnerships between FPOs and startups improve farmers' access to markets and technologies while addressing operational and quality challenges in connecting with agribusiness corporates (Spender, 2014, Colombo & Piva, 2008; Davila et al., 2003; Mustar et al., 2008) [18,5,7,14] . The adoption of Agri-tech innovations is a decision-making process influenced by a complex confluence of socioeconomic, perceptual, technological, institutional, and behavioral factors (Cui & Wang, 2023) [6] . The adoption is not solely an economic calculation but is deeply influenced by a farmer's personal context and external support systems. Socio-economic features consistently emerge as fundamental determinants, with studies acknowledging the roles of age, education, marital status, income, gender, health, and cooperative membership in shaping opinions toward digital agriculture (Meijer et [13,15,6,9,16] . Specific findings indicate that older individuals and part-time farmers often express more negative perceptions due to limited digital familiarity (Bontsa et al., 2023) [3] . Conversely, while farming experience and income show a positive correlation with technology utilization, higher education does not always translate to positive attitudes, particularly when technologies fail to align with local practices (Bontsa et al., 2023; Izuogu et al., 2022; Kaur & Singh, 2021) [3,9,10] . Furthermore, farm-specific factors, such as farm size or the type of farming enterprise, indirectly affect the perceived feasibility and evaluation of digital tools (Cui & Wang, 2023; Reissig et al., 2022) [6,17] . Beyond static personal attributes, a farmer's perceptual and behavioral dimensions are key drivers of adoption. The perceived characteristics of the technology itself, specifically usefulness, ease of use (Caffaro et al., 2020) [4] , reliability, and safety (Pishnyak & Khalina, 2021) [16] , are critical. Innovations perceived to contribute to better productivity, enhanced precision, and improved results are seen favorably (Adrian et al., 2005) [1] . At a psychological level, perceived profitability and risk tolerance significantly shape adoption attitudes (Cui & Wang, 2023) [6] . However, these benefits are frequently tempered by constraints. Negative perceptions are associated with high costs, digital inequality, and the inadequate integration of Indigenous Knowledge systems (Caffaro et al., 2020) [4] . Challenges such as poor internet access, high costs of tools, and limited digital skills act as key deterrents, alongside concerns that digital technologies may displace traditional knowledge and limit social interaction (Izuogu et al., 2022) [9] . Finally, the social and institutional environment plays a crucial, mediating role. Subjective norms, like beliefs and practices accepted by peers, can profoundly shape a farmer's perception of innovation, often holding more weight than the technical attributes (Dissanayake et al., 2022) [8] . Social structures like cooperative membership are vital, enhancing peer learning, reducing uncertainty (Caffaro et al., 2020; Meijer et al., 2015) [4,13] , and utilizing informal communication channels as trusted sources of information (Thomas et al., 2023) [19] . Institutionally, factors such as government subsidies, extension services, and access to credit enhance adoption by mitigating financial and perceived risks (Cui & Wang, 2023) [6] . Therefore, promoting Agri-tech requires addressing not only technical gaps but also infrastructural deficits and ensuring the local relevance and affordability of the solutions to secure inclusive and sustained adoption (Izuogu et al., 2022) [9] .

Methodology

The present study employed an exploratory research design to investigate the relatively nascent and under-explored ecosystem of Agri-Tech startups and Farmer Producer Organizations (FPOs) in the context of Extension Advisory Services (EAS). This design was considered appropriate for capturing emerging institutional linkages, operational mechanisms, and contextual dynamics in a domain where empirical evidence is still evolving. The research was conducted during 2022 to 2024 in the Indian states of Telangana and Chhattisgarh. These states were purposively selected to represent contrasting agricultural and socio-economic environments. Telangana is characterized by relatively higher levels of digital adoption, stronger institutional support, and improved market connectivity, whereas Chhattisgarh reflects a transitional agricultural landscape where modernization processes are progressively unfolding. Examining these diverse contexts enabled the development of insights that are both regionally grounded and broadly applicable. An initial database of active FPOs was compiled from multiple institutional sources, including the Small Farmers' Agribusiness Consortium (SFAC), National Bank for Agriculture and Rural Development (NABARD), State Departments of Agriculture, and relevant non-governmental organizations. From this pool, ten FPOs-five from each state-were purposively selected based on the following criteria: (i) a minimum operational history of four years, (ii) at least 350 member farmers, (iii) active engagement in core business functions such as aggregation, input supply, marketing, value addition, or processing, (iv) adoption of at least one digital or Agri-Tech tool, and (v) established institutional linkages with startups, Krishi Vigyan Kendras (KVKs), NGOs, or government agencies. Subsequently, 30 member farmers were randomly selected from each FPO, resulting in a total sample size of 300 respondents, with equal representation (150 each) from both states. This sampling approach ensured adequate heterogeneity while maintaining statistical robustness. The study incorporated 21 independent variables, i.e., age, education, farming experience, landholding, annual income, extension contact, innovativeness, risk orientation, economic orientation, information-seeking behavior, mobile inclination, management orientation, FPO membership, attitude toward FPOs, credit acquisition, cosmopoliteness, training received, social participation, participation in FPO activities, level of satisfaction, and farmer empowerment. The dependent variable was to study the farmers' perception of Agri-Tech startup-FPO integration in enhancing extension advisory services. Primary data pertaining to farmers' socio-personal, economic, communicational, and institutional characteristics were systematically collected using a structured interview schedule. The data were coded, tabulated, and analyzed to examine the distribution of respondent characteristics and their relationship with the dependent variable. To identify the determinants influencing farmers' perceptions of Agri-Tech-FPO integration for improved advisory services, correlation analysis and stepwise multiple regression were performed using the IBM SPSS Statistics platform. These statistical techniques facilitated the identification of significant predictor variables and provided a robust explanation of the factors shaping farmers' views regarding the relevance and effectiveness of such collaborative extension models.

Results

Profile characteristics of farmers (Independent variables)

The data presented in Table 1 reflect the profile characteristics of farmers from Telangana and Chhattisgarh. Across both states, most respondents (37%) belonged to the 46-57 years' age group. Regarding education, 25.67% had completed high school (9 th -10 th class). A considerable share www.extensionjournal.com (30.67%) had more than 18 years of farming experience. The majority (83%) reported an annual income between Rs. 50,000 and Rs. 1,50,000 lakhs, with 42.33% earning Rs. 1 to 1.5 lakh and 40.67% between Rs. 0.5-1 lakh. Marginal farmers (up to 1 ha) constituted the largest group (41%). In terms of extension contact, 70.33% of respondents were in the medium category, while 57% exhibited a medium level of information-seeking behaviour. Social participation was moderate, with 53% holding memberships in multiple organizations and 22.33% in leadership roles. Similarly, 62.67% showed a medium level of cosmopoliteness and 49% a medium level of innovativeness. More than half (53%) displayed a high level of risk orientation, and 52% had high economic orientation. Management orientation was moderate among 43.33% of respondents. With respect to mobile inclination, 56.67% demonstrated medium mobile inclination. A majority (77%) had undergone some form of training, and 74.33% had accessed agricultural credit, with a slightly higher share in Chhattisgarh (78.67%) than Telangana (70%). Among FPO activities, participation was highest in training orientation (69.50%), followed by market orientation (67.33%), formation of FPOs (64.83%), and production orientation (63.17%). In terms of attitude towards FPO, 59.34% exhibited a moderately favourable attitude, while 24.33% had a highly favourable one. Overall satisfaction with FPOs was medium among 60.67% of respondents. Half of the respondents (50%) reported a medium level of empowerment, reflecting balanced social, economic, and leadership gains through FPO participation.

Discussion 4.1 Factors influencing farmers' perception of Agri-Tech startups-FPO integration

In this section, an attempt was made to identify the various factors (independent variables) that influence the perception of farmers towards the integration of Agri-Tech Startups with Farmer Producer Organizations (FPOs) for improved Extension Advisory Services. To achieve this, both correlation analysis and stepwise regression analysis were carried out.

Correlation Analysis

The data presented in table 3 provides insights into the relationship between the selected independent variables and the farmers' perception towards the integration of Agri-Tech Startups with Farmer Producer Organizations (FPOs) for improved Extension Advisory Services.

Management

Orientation and Perception: Management Orientation showed a non-significant correlation in Telangana (0.089 NS), Chhattisgarh (0.030 NS), and Overall (0.057 NS), indicating that a farmer's planning and decision-making skills do not significantly influence their perception of Agri-Tech-FPO integration. While managerial aptitude is valuable for farm operations, it alone does not determine receptiveness to technology-based advisory services. Awareness, training, and exposure to digital tools are essential to enhance understanding and adoption, as farmers may rely on traditional knowledge despite strong management skills.

FPO Membership and

Multiple Regression Analysis

The multiple regression analysis, as shown in the table 4, conducted to determine the influence of 21 independent variables on farmers' perception towards Agri-Tech Startup-FPO integration revealed noteworthy findings. In Telangana, the model explained 67.7% of the variation in perception (R² = 0.677), suggesting a strong influence of the selected variables on farmers' attitudes towards enhanced extension advisory services. In Chhattisgarh, the R² value was 0.597, indicating that 59.7% of the variation in perception was explained by the model, reflecting a moderately strong association. At the overall level, the R² value stood at 0.637, confirming the substantial explanatory power of these variables across both states.

Step-wise Regression Analysis

To determine the most significant variables influencing farmers' perception regarding the integration of Agri-Tech Startups with Farmer Producer Organizations (FPOs), a

Step-wise Multiple Regression Analysis was conducted. This statistical approach enabled the systematic elimination of non-significant variables to arrive at a more parsimonious and interpretable model while retaining sufficient explanatory power. The data presented in the table 5 reflects that, the initial www.extensionjournal.com farmers' perception toward Agri-Tech-FPO integration. The inclusion of these factors underscores their relevance in shaping farmers' behavioural and attitudinal responses to institutional and technological interventions.

Conclusion

The study concludes that farmers' perception of Agri-Tech Startup-FPO integration for improved extension advisory services is largely shaped by behavioural, communicational, and institutional factors rather than structural farm characteristics. Correlation analysis revealed that younger age, higher education, income, extension contact, innovativeness, risk orientation, information-seeking behaviour, mobile inclination, FPO membership, positive attitude towards FPOs, satisfaction, participation in FPO activities, and empowerment significantly influenced perception across regions, while landholding, economic orientation, and management orientation remained largely non-significant.

Step-wise regression analysis identified age, annual income, innovativeness, mobile inclination, FPO membership, cosmopoliteness, social participation, participation in FPO activities, satisfaction, and empowerment as key predictors, jointly explaining 59.10% of the variation in perception. Satisfaction emerged as the strongest determinant, underscoring the central role of positive institutional experiences. Therefore, policy and extension efforts should prioritize strengthening FPO service quality, enhancing farmer satisfaction, promoting digital and mobile literacy, encouraging active participation in FPO activities, and empowering farmers through targeted training and exposure to Agri-Tech innovations to ensure effective and sustainable integration.

Acknowledgement

I would like to extend my heartfelt gratitude to my advisors, Dr. M. L. Sharma and Dr. Arun Kumar, for their trust, insightful discussions, invaluable advice, and unwavering support throughout the entire study. The authors of this research study are thankful and acknowledge the help, support and co-operation from staff of Department of Agricultural Extension, IGKV, Raipur and Transfer of Technology (TTT), IIRR, Rajendranager, Hyderabad.

Conflict of Interest

The authors of the paper declare no conflict of interest.

References

1. Adhikari A, Pradhan K, Chauhan JK, Reddy SK. Analysing the perceived impact of farmers' producer organization FPOs on sustainable economic development. Indian Research Journal of Extension Education. 2021;21(2-3):80-82.
2. Adrian AM, Norwood SH, Mask PL. Producers' perceptions and attitudes toward precision agriculture technologies. Computers and Electronics in Agriculture. 2005;48(3):256-271.
3. Bontsa NV, Mushunje A, Ngarava S. Factors influencing the perceptions of smallholder farmers towards adoption of digital technologies in Eastern Cape Province, South Africa. Agriculture. 2023;13(8):1471.
4. Caffaro F, Micheletti Cremasco M, Roccato M, Cavallo E. Drivers of farmers' intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of Rural Studies. 2020;76:264-271.
5. Colombo MG, Piva E. Strengths and weaknesses of academic start-ups: A conceptual model. IEEE Transactions on Engineering Management. 2008;55(1):37-49.
6. Cui L, Wang W. Factors affecting the adoption of digital technology by farmers in China: A systematic literature review. Sustainability. 2023;15(20):14824.
7. Davila A, Foster G, Gupta M. Venture capital financing and the growth of start-up firms. Journal of Business Venturing. 2003;18(6):689-709.
8. Dissanayake CAK, Jayathilake W, Wickramasuriya HVA, Dissanayake U, Kopiyawattage KPP, Wasala WMCB. Theories and models of technology adoption in agricultural sector. Human Behavior and Emerging Technologies. 2022;2022:9258317.
9. Izuogu CU, Kadurumba C, Azuamairo GC, Njoku LC, Olaolu MO. Perception of utilization of digital technologies among arable crop farmers in Imo State, Nigeria. Proceedings of the 27th Annual Conference of the Agricultural Extension Society of Nigeria AESON. 2022:52-58.
10. Kaur A, Singh R. Perception and attitude of agripreneurs toward social media tools for attaining agribusiness benefits. Indian Journal of Positive Psychology. 2021;12(4):379-384.
11. Kurien V. India's milk revolution: Investing in rural producer organizations. Gujarat: The World Bank; 2004.
12. Markelova H, Meinzen-Dick R, Hellin J, Dohrn S. Collective action for smallholder market access. Food Policy. 2009;34(1):1-7.
13. Meijer SS, Catacutan D, Ajayi OC, Sileshi GW, Nieuwenhuis M. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. International Journal of Agricultural Sustainability. 2015;13(1):40-54.
14. Mustar P, Wright M, Clarysse B. University spin-off firms: Lessons from ten years of experience in Europe. Science and Public Policy. 2008;35(2):67-80.
15. Pfeiffer J, Gabriel A, Gandorfer M. Understanding the public attitudinal acceptance of digital farming technologies: A nationwide survey in Germany. Agriculture and Human Values. 2021;38(1):107-128.
16. Pishnyak A, Khalina N. Perception of new technologies: Constructing an innovation openness index. Foresight and STI Governance. 2021;15(1):3955.
17. Reissig L, Stoinescu A, Mack G. Why farmers perceive the use of e-government services as an administrative burden: A conceptual framework on influencing factors. Journal of Rural Studies. 2022;89:387-396.
18. Spender JC. Business strategy: Managing uncertainty, opportunity, and enterprise. Oxford: Oxford University Press; 2014.
19. Thomas RJ, O'Hare G, Coyle D. Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production. Technological Forecasting and Social Change. 2023;189:122374.
20. Vadivelu A, Kiran BR. Problems and prospects of agricultural marketing in India: An overview. International Journal of Agricultural and Food Science. 2013;3(3):108-118.