In this modern age dominated by data and information, timely access to the right and up-to-date information can prove a major game-changer, even in the fields of agriculture. Right information has multi-faceted benefits for agriculture and can prove instrumental in substantially improving farm productivity, efficient farm management, reduced risk and uncertainty, diversification of the farming systems, being competitive and consequently, boosting the farmers’ incomes (Van den Ban and Hawkins, 1998; Sharma, 2002; Adhiguru et al., 2009; Brhane et al., 2017; Krishna et al., 2019; Anang et al., 2020) and addressing the diverse needs of heterogenous farming community (Munshi, 2004; Negi et al., 2020). While a mere 40% of farm households in India benefit from access to agricultural information, a commendable three-fourths of those with access actively integrate it into their decision-making (Birthal et al., 2015). These contrasting figures underscore both the need for wider information access and the potential for positive impact once information barriers are overcome. Studies like Owens et al. (2003), Godtland et al. (2004), Davis et al. (2010), and Birthal et al. (2015) show a clear link between information access and increased farm income. Information’s impact on farm income is not one-size-fits-all. While formal sources might work better for cash crops (Birthal et al., 2015), local networks could be crucial for subsistence farmers. We need context-specific information delivery strategies to unlock the full potential of knowledge.

There have been multiple players in the Indian agricultural scenario, who cater to the extension and information needs of the farmers, namely, the public extension agents including Government organizations, KVKs, ATMA, Agricultural departments, Agricultural universities; the private extension players like the input dealers; Voluntary organizations; cooperatives; farmer-based organizations; mass media and the modern ICT tools, etc. A better understanding of the patterns in which the farmers access these sources for retrieving the needed agricultural information and the underlying factors which closely influence this pattern, may help us in formulating the content or guide the policy-makers in devising contextually appropriate extension programs for their easy uptake by the farmers. This is of immense importance for revamping the sluggish public-sector extension system, which has been ailing due to irrelevant and obsolete content and large-scale information failure (lack of feedback and limited reach to farmers) (Swanson and Mathur, 2003; Joshi et al., 2005; Anderson and Feder, 2007; Sontakki et al., 2010; Babu et al., 2012, 2013; Kumar et al., 2012; Sulaiman, 2012). The findings from the 59th and 70th National Sample Survey Office (NSSO) surveys in India underscore the limited accessibility of extension services for farmers, especially among those who are socially and economically marginalized (Sajesh and Suresh, 2016; Krishna et al., 2019). An in-depth understanding of how the farmers absorb and adapt to the available information, will also guide us what are the future trends of information search and help us in predicting how different organizational designs will influence the nature of information generation, transmission, adoption, and assimilation (Stiglitz, 2000; Mittal et al., 2010; Ali and Kumar, 2011).

Indian farmers are a heterogenous group and the factors that influence the likelihood of accessing an information source are not common to farmers of diverse agro-ecological regions in a country. Therefore, understanding the specific socio-economic and psychological factors functional at an individual level, which affects their information-seeking behavior, access, and use is the first and foremost step toward better targeting of our extension programs and advisory services (Bernard et al., 2015). Despite the extensive and pluralistic nature of India’s public extension systems (Raabe, 2008; Glendenning et al., 2010), there exists a huge information gap among the farmers, a tremendous disconnect between knowledge production and utilization by farmers. Bridging this gap requires actively listening to and understanding the needs of farmers, their clients. Understanding the relevance of the issue, the research study was designed to attempt to shed light on the prevalent information networks, farmers’ perceptions and determinants of the information-seeking behavior of the farmers of Indo-Gangetic plains in India with respect to the various extension advisory sources.

The present study analyses data particular to the Indo-Gangetic plains, extracted from the dataset (of 2018–19) generated from the 77th round of the “Situation Assessment of Agricultural Households and Land and Holdings of Households in Rural India, 2019,” conducted by the National Sample Survey Organization (NSSO), Government of India.

Indo-Gangetic plains (IGP) (Figure 1) in India is the major foodgrain-growing region, consisting of five major states (Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal) and considered as the “food bowl of India.” It produces about 50% of the total foodgrains to feed 40% of the population of the country (Pal et al., 2009). The IGP represents eight agro-ecological regions and 14 agro-ecological subregions (Pal et al., 2009). Based on climatic, hydrologic, and physiographic variations, the IGP consists of four homogeneous divisions, namely Trans-Gangetic Plains (TGP), Upper Gangetic Plains (UGP), Middle Gangetic Plains (MGP) and Lower Gangetic Plains (LGP). The Indian part of IGP extends from 73^° E longitude and 32^° N latitude to 89^° E longitude and 21^° N latitude. These five states cover nearly 15.65% of the total geographical area of the country and are home to 37.4% of the population of the country. The Plains gradually slope from north-west toward the Bay of Bengal in the east and undergo a gradual transition in climate, physiography, natural vegetation and cropping systems. The rainfall ranges from less than 400 mm per year in the north-west to more than 1,800 mm per year in the lower Gangetic plains of Bengal. It has the highest cropping intensity and most of the agricultural land is irrigated.

We are currently working in our institute’s project focused on the Indo-Gangetic Plains region. The IGP, especially the eastern part has been identified as a region where there is a lot of untapped potential to realize higher agricultural productivity. There are significant differences in these regions in terms of natural resource endowment, demography, and agricultural productivity. Despite vast fertile potential, the Indo-Gangetic plains grapple with stagnant agricultural productivity due to limited embrace of advanced farming technologies (Taneja et al., 2014). Today, the Indo-Gangetic states are facing with some of the most serious agro-ecological problems like declining productivity and falling ground water table, increasing soil salinity (Abrol et al., 2000) and agricultural pest problems due to excessive and improper use of resources. This region has also been demarcated as an area of high social vulnerability (Chhetri and Chaudhary, 2011), which may become food insecure by the early 2030s due to the changing climate scenario (Swaminathan, 2010). We have extracted data specific to this region from the NSSO survey for this area to derive meaningful insights and implement targeted interventions tailored to the selected belt. Various past studies indicate that accessibility and availability of relevant, and timely information can lead to enhanced penetration of improved agricultural technologies and have profound impact on the agricultural productivity, and thus consequently, ensure livelihood security of the agricultural households. Our focus on this specific belt underscores our commitment to addressing region-specific challenges and contributing to the overall advancement of agricultural practices.

Agriculture is dominated by a large number of marginal farmers and smallholders with varying levels of knowledge, skills, capital, and resource bases. The information requirements of such a heterogenous community are diverse. A farmer’s choice of information source is not one-size-fits-all. A multitude of factors, from social identity to farm practices, are believed to influence how farmers seek agricultural information. It is hypothesized that farmers’ choices for information sources is differentiated by various explanatory attributes like the backgrounds of the farmers, age, gender, education, caste, size of operational landholding, irrigated area, per capita monthly expenditure, primary income source, access to institutionalized credit sources, choices like membership in FPOs, insurance of crops and sale of produce at MSP. Knowing what influences their choices will allow us extension professionals to tailor information delivery for maximum impact. Thus, the present study was an attempt to delve deeper into the intricate patterns of information-seeking behavior of the farmers and devise suitable mechanisms with various institutional stakeholders to address the existing information gap. In this study, the issues for investigation were therefore as follows:

The data consisted of 14,782 households, of which 14,046 cultivated crops and 10,539 owned livestock. The study aimed to find out the level of farmers’ access, their preferences and the determinants of access to various agricultural extension service sources. The sources were broadly categorized into five groups: (a) Public Players (Government extension agents/ATMA, Krishi Vigyan Kendras, Agricultural universities/colleges, and veterinary departments); (b) Private Players (input dealers, private commercial agents, private processors, and ACABC); (c) Farmers’ organizations (cooperatives/dairy cooperatives, and FPOs); (d) Mass media/ICT (Kisan Call Centre, print media, radio/TV/other electronic media, and smartphone apps); and (e) Progressive farmers.

The descriptive statistics including mean, frequencies, and percentages were used to analyze the trends and preferences of farmers’ access to the extension service sources. Then, probit regression analysis was employed to analyse the determinants of the access by modeling “Access dummy” as 1 = access to any of the extension service sources and 0 = Otherwise. But, since there were multiplicity effects from various sources (not mutually exclusive) used by the farmers, Multivariate probit (MVP) regression analysis was performed. The MVP model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. It can jointly predict the likelihood of accessing two or more sources of extension services. The MVP estimation technique takes into account the correlation in error terms by simultaneously modeling the impact of a group of covariates on each of the agricultural extension sources. It involves estimating a series of binary probit models while permitting the error terms of these sources to correlate independently. Neglecting to address these interrelations may result in distorted estimates (Lin et al., 2005; Velandia et al., 2009; Dougherty, 2011; Kassie et al., 2013).

The general equation can be expressed as:

Y i k = a i k ∗ x i k + e i k .

where 𝑌_i𝑘 is the dependent variable of access to different k extension service sources (public players, private players, farmers’ organizations, mass media and progressive farmers), 𝑥_i𝑘 is the vector of variables hypothesized to determine the access to various sources enlisted, 𝛼_i𝑘 is the estimated parameter, and e_i𝑘 is the error term. The variables that are modeled to determine access to the extension service sources are age, gender, education, caste, landholding size, number of family members in working age group, per capita monthly expenditure, primary income source, access to irrigation, membership in FPOs, sale of produce at MSP, the experience of crop losses and insurance of the crops.

Our study reported that the private agencies were the most popular information source among farmers, accessed by 35.78% of them. A study by Mukherjee and Maity (2015) indicates that private extension service providers can meet the information needs of farmers by supplying quick, timely, efficient and appropriate services. Agri-input dealers are the most trusted information source for the farmers in the rural domain (Ferroni and Zhou, 2012); as they not only provide information but also new variety/technology, other agricultural inputs and conduct demonstrations in farmers’ fields (Kumar et al., 2020). Progressive farmers also served as preferred sources for agricultural information. This could be attributed to their easy accessibility (Mittal and Mehar, 2013), high credibility and socio-cultural compatibility, and potential for efficient and cost-effective information dissemination (Lukuyu et al., 2012). Bachhav (2012) and Mittal and Mehar (2013) argued that fellow farmers are the first preferred sources of information for the farmers followed by the print media and public extension personnel. Instead of favoring large and well-equipped farmers, the extension services should be redirected to prioritize farmers who truly reflect the local farming conditions. Oluwasusi (2014) found that the extension service needs of farmers were met by progressive farmers followed by input dealers, radio broadcasts and by public extension services.

For the most demanded information in agriculture like inputs (seeds, fertilizers, plant protection chemicals), the popular choices were traditional media, progressive farmers and input dealers. The findings of Meitei and Devi (2009), Babu et al. (2012), Bachhav (2012), Ghimire et al. (2012), Mittal and Mehar (2013), and Saravanan (2007) were in line with the study findings. There should be inclusion of multiple aspects which are of emerging importance in agriculture like climate change, post-harvest management, value chain systems management, market mobilization and quality control. Gulati et al. (2018) also highlighted Krishi Darshan as one among the major sources of agricultural information for the farmers. However, the information provided from multiple sources like private players, progressive farmers and mass media need to be triangulated and validated from Government agencies to establish their credibility before dissemination to the farming community. Prior to formulating any information set, it is essential to consider the varied needs and heterogeneous socio-economic and agro-ecological conditions of the farming population, especially the regional variations. Also, the information inclusiveness should be taken into consideration by giving special care to delivering information to small and marginal farmers, women and marginalized groups.

Inspite of many studies reporting the public players being the major providers of extension services in developing nations (Reardon et al., 2011; Kassem, 2014; Maoba, 2016; Berhane et al., 2018), the present study revealed that the public extension system served only 10% of the households for their information needs. The poor access can be attributed to unsustainable public costs, shortage of human resources, staff over-burdened with other routine works (Birner and Anderson, 2007), the tendency to neglect the women farmers (Raabe, 2008), a top-down and supply-driven approach, lack of farming system approach (Singh et al., 2012), bias toward the large farmers (Brhane et al., 2017), over-reliance on traditional means of public outreach, lack of information specificity with respect to quality, time and location (Sulaiman and Hall, 2002; Sajesh and Suresh, 2016; Vincent and Saravanan, 2020). The key is to establish partnerships among public, private, and non-governmental organizations to meticulously shape information and achieve a synergistic convergence of social welfare, speed, and efficiency. Also, we need to tap the potential of modern ICT tools for wider information dissemination and coverage.

Babu et al. (2013) reported that the preference toward the agricultural information source was primarily determined by proximity, assured quality, sole option, and timely availability. The findings of poor access to FPOs and Cooperatives were reiterated in the studies conducted by Nagar et al. (2021), Babu et al. (2012), and Ferroni and Zhou (2012). There is a need to promote the role of FPOs and other similar farmer groups or collectives in catering to the information needs of farmers. We observed that multiplicity of information sources accesses increased with increasing landholding size. The findings are in line with Babu et al. (2012) and Shiri et al. (2014). Mittal and Mehar (2013) also concluded that small and marginal farmers use a single information source, especially the farmer-to-farmer interaction; 33% of the farmers use any combination of three sources and 22% of the farmers use all four categories of information. Nagar et al. (2021) concluded that with increasing education and land size, households tend to diversify their information sources.

The present study inferred that access to agricultural information sources is significantly determined by age, gender, education, landholding, irrigated area and sale of produce at MSP. Studies by Babu et al. (2012), Maake and Antwi (2022), and Mittal and Mehar (2013) indicate that age, education level, and farm size significantly influence the farmer’s access to advisory services from public extension. Age and education have been reported to be positively significant for access, however age does not contribute to the adoption (Tchaogang et al., 2019; Nagar et al., 2021). However, a negative effect of age was studied by Jenkins et al. (2011), citing that increasing age makes them averse to search for new information and making changes in the cropping system. Study findings of Abraham (2008), Jensen (2010), and Mittal et al. (2010) highlighted that education, landholding size and the per capita income have a significant effect on access to information sources. Our findings were contradicted by Nagar et al. (2021) who reported that household size had no effect on access to information services. But it was congruent with our findings of male headed households accessing more information as compared to their female counterparts and increased information collection by households selling at MSP and with assured means of irrigation. Babu et al. (2012) studied that membership in FPOs significantly determine information access, which was not confirmed through our study. The study by Joshi et al. (2015) reported that when the principal source of income is “Livestock” as compared to “Crop cultivation,” the likelihood of farmers accessing agricultural information goes down by 61% for livestock. Our findings were reiterated by Anderson (2011) and Nagar et al. (2021) who reported that as compared to SCs and STs, the OBCs and General categories have an advantage of access to extension service providers, attributed to their strong social networking and favorable socio-economic-cultural position in the society.

The study reveals significant disparities in the accessibility of agricultural extension services for agricultural information among farmers with varying landholding sizes. This underscores the pressing need for the development of targeted policy measures aimed at fostering inclusivity. These measures are essential to ensure that extension services become more accessible across the farming community, especially targeting the women, small and marginal farmers and those belonging to the marginalized sections of society, ultimately guaranteeing equitable benefits for all. The information should be so designed to cater to the diverse needs of the heterogenous farming community. The input dealers have emerged as the most preferred information sources, thus they need to be trained to provide authentic and relevant information to the farmer clientele. The promotion of farmer-to-farmer cross-learning can be enhanced by actively encouraging and acknowledging the accomplishments of successful farmers within the region. This can be achieved through the implementation of regular exposure visits, providing a valuable platform for knowledge exchange and recognition of best practices. The use of social networking in agriculture goes beyond mere communication; it builds communities, fosters knowledge exchange, and empowers farmers with the information they need for sustainable and successful agricultural practices. As technology continues to evolve, leveraging social networks will remain a dynamic tool for transforming how agricultural information is shared and utilized within farming communities. The increasingly popular farmer-based organizations merit effective promotion to fully realize the advantages offered by economies of scale. The government should play a pivotal role in validating and promoting agricultural content within mobile apps, ensuring its reliability and user-friendliness for the intended target group. Additionally, there is a pressing need to converge traditional mass media with cutting-edge Information and Communication Technology (ICT) tools to create a more comprehensive and effective information dissemination platform.

The extension services provided needs to be tailored to the specific needs and socio-economic conditions of all farmers; taking into account their different farming systems, livelihood strategies, and cultural contexts. The public extension system needs to be strengthened to deliver timely and need-based information in smart ways. The study identified the factors like age, gender, education, size of landholding, irrigated area, and households that sold crop produce at MSP as the underlying factors determining access to sources of extension services. Thus, these factors can serve as the foundational basis for designing better and well-informed policy decisions to further improve access to extension and advisory services in future. The study recommends robust government support for reinforcing the education system and enhancing the capacity of small and marginal farmers, as well as those from marginalized communities and women. This approach aims to uplift their economic conditions and guarantee fair access to resources. In essence, fostering inclusive extension services emerges as a crucial factor in advancing sustainable agriculture and environmental well-being.

The implications of the above study hold significance for farmers, agricultural universities, and state agricultural departments. For farmers, the findings provide insights into potential challenges and variations in accessing agricultural extension services, allowing them to better understand and navigate available resources. Agricultural universities can utilize this information to tailor their outreach programs, ensuring they address the specific needs of diverse farmer groups. State agricultural departments can benefit by refining their support strategies based on the identified patterns, ultimately contributing to more effective and targeted agricultural extension services. Overall, the study offers actionable insights that can inform policy decisions, resource allocation, and support mechanisms for the benefit of the agricultural community. As future research endeavors, we are also looking ahead to undertake a comparative analysis of factors influencing access and usage pattern of agricultural extension services across various regions of India; especially highlighting the socio-cultural, geopolitical, agro-climatic factors, etc. Through this exercise, we aim to identify nuanced challenges and opportunities that can deliver more targeted and effective agricultural extension services. In addition, the analysis of the impact to explore the effectiveness of the accessed information on farmers’ practices or livelihoods; is a planned aspect for future research studies.

Publicly available datasets were analyzed in this study. This data can be found here: The data was extracted from the dataset generated from the 77th round of the “Situation Assessment of Agricultural Households and Land and Holdings of Households in Rural India, 2019,” conducted by the National Sample Survey Organization (NSSO), Government of India.

SSa: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. SBi: Writing – review & editing, Methodology, Data curation. PS: Writing – original draft, Software, Methodology, Data curation. Satyapriya: Writing – review & editing, Project administration. GM: Writing – review & editing. RB: Writing – review & editing, Project administration. SBa: Writing – review & editing, Formal analysis. MM: Writing – review & editing, Formal analysis. VS: Writing – review & editing, Investigation. SSi: Writing – review & editing, Validation. RS: Writing – review & editing, Visualization, Validation. MW: Writing – review & editing, Validation. PJ: Writing – review & editing, Validation. SSh: Writing – review & editing, Visualization.