Introduction

The National Agricultural Market (e-NAM) is an electronic trading platform in India that intends to develop a single national market for agricultural commodities (Mehta et al. 2019). The site provides a forum for organic agriculture farmers to sell their goods directly to customers, removing the need for intermediaries and guaranteeing producers get higher pricing (Mehta et al. 2019). Odisha inaugurated the e-NAM platform in April 2018, to unify the state’s Agricultural Produce Market Committees (APMCs) into a single integrated market (Samantaray et al. 2021; Saravanan and Archana 2021). By providing real-time information regarding pricing, arrivals, and sales, the deployment of e-NAM in Odisha has helped to promote transparency and efficiency in the state’s agricultural market. Organic agriculture farmers may access the platform via mobile phones, PCs, or APMCs, where they can check pricing and offer information and make sales. Buyers may also utilize the site to purchase agricultural produce from organic agriculture farmers, lowering transaction costs and expanding market reach (Mishra and Narayan 2017). Overall, e-NAM has been a beneficial development for the agriculture industry in Odisha, helping to enhance farmer income and strengthen market performance (Ghosh 2013). e-NAM has been working successfully in Odisha since its adoption and has helped to improve the productivity of the state’s agricultural market. Organic agriculture farmers may now sell their goods to a larger spectrum of consumers and obtain higher prices, thanks to the platform. It has additionally contributed to increased market openness by giving real-time information on pricing, arrivals, and sales. One of the primary advantages of e-NAM in Odisha has been the removal of intermediaries, which has resulted in better agricultural prices (Selvaraj and Karunakaran 2022; Raju et al. 2022a, b). The program has also helped to minimize transaction costs and expand market reach, making it easier for organic agriculture farmers to sell their goods and consumers to buy it.

Despite these advantages, the deployment of e-NAM in Odisha has been fraught with difficulties. Some organic agriculture farmers, for example, have experienced difficulty utilizing the platform and a lack of knowledge about its capabilities and advantages (Saravanan and Archana 2021). There have also been significant challenges with infrastructure and connection in rural regions, limiting the platform’s reach in these areas. Overall, e-NAM has received positive feedback in Odisha and has yielded encouraging outcomes since its deployment (Gupta and Badal 2018). However, there is still space for development, and further efforts are required to solve the issues and guarantee that the platform realizes its full potential in the state (Gupta and Badal 2018).

Rationale of the study

According to Venkatesh et al. (2021), the performance of e-NAM is an important phenomenon that should not be underestimated. Many agricultural markets are primarily concerned with enhancing their performance and service delivery; therefore, it is critical to pay close attention. Organic agriculture farmers’ growth performance is examined by using various measuring criteria (Van Passel et al. 2007) and this has been a widespread practice; nevertheless, the focus on the qualifications of organic agriculture farmers and expertise in the training of e-NAMs executive officers appears to be an underserved area (Mishra and Bhatt 2019). Why is this so crucial to research? There are several reasons for this, including the need to give viable returns and advantages to organic agriculture farmers and cultivators in a nation like India, where agriculture plays a vital role in the economy and agriculture communities form a large part of the labour force. Organic agriculture farmers’ living conditions will not improve if they are unable to obtain a fair price for their goods and are subject to market fraud. Market committees are in charge of ensuring the smooth and transparent trade of agricultural products (Mahendra Dev 2014). A market committee that works efficiently would ensure that organic agriculture farmers in that region reap the full advantages of their labour in the fields (Sugden et al. 2021; Derpsch et al. 2010; Baumann 2000). The function of the APMC as the genuine representation and leader of a market committee is critical. APMC qualification and experience are essential elements that determine market committee performance. That is why this study is relevant in this context and should be accorded similar weight (Sugden et al. 2021; Derpsch et al. 2010). This research will also assist policymakers at service provider institutes in developing rules that assure optimum advantages in the actual world.

Research problem statement

In recent years, the rate of rise in farmer income has not kept pace with the rate of increase in wages, pensions, and other expenses (Sasmal 2015). The performance of market committees falls short of expectations. This increases the obligations of AMCPs (Jan 2012) in terms of pending pensions, gratuities, and employee pay. The APMC is also unable to give facilities to growers, organic agriculture farmers visiting markets (Chand 2019) and agriculture-based companies in their designated region. This gives justification for investigating the numerous aspects that influence a market committee’s income (Acharya 1998). The current study is an attempt to solve this issue by assessing the elements that influence the performance of a market committee comprehensively.

Review of literature

Farmers’ awareness programmes serve as an initial phase in introducing farmers to new agricultural concepts, technologies, and practices. These programmes raise awareness about the advantages of using modern techniques, environmentally friendly practices, and technological advancements (Raghuvanshi and Ansari 2017). Hands-on training supplements awareness programmes by providing practical experience and skill development. During hands-on sessions, farmers can apply the knowledge gained from awareness programmes in real-life scenarios, which reinforces their learning. (Sisson 2001). Extension officers deliver information from awareness programmes to farmers and provide guidance on how to effectively implement new practices, bridging the gap between knowledge dissemination and practical implementation (Cohen and Lemma 2011). Online tutorials and resources provide farmers with a flexible and easily accessible way to continue learning beyond awareness programmes (Brill and Park 2011).

Precision farming entails using technology to precisely optimise and manage various aspects of farming. This method employs data collection, analysis, and technology to tailor agricultural practices to specific field conditions, resulting in increased efficiency and decreased resource waste (Blackmore 1994). Mechanisation and automation refer to the use of machinery and technology to perform various farming tasks, thereby reducing the need for manual labour and increasing efficiency (Karkee and Zhang 2012). Smart remote sensing collects data on soil health, crop health, weather patterns, and other topics using advanced technologies such as drones, satellites, and sensors. This information assists farmers in making informed decisions about irrigation, pest control, and fertiliser application (Slepnev et al. 2020). Hydroponics is the practice of growing plants directly in nutrient solutions, whereas aquaponics is the combination of aquaculture (raising fish) and hydroponics (Jon Schneller et al. 2015). Crop diversification is the practice of growing multiple crops on the same farm. This practice can improve soil health, reduce pest and disease pressure, and boost farm resilience overall (Acharya et al. 2011).

Soil health management refers to practices that aim to preserve and improve the quality and fertility of the soil. This includes crop rotation, cover cropping, minimal tillage, organic matter addition, and the use of green manures (Saha et al. 2012). Effective water management is critical for sustainable agriculture, particularly in water-stressed areas. Effective irrigation methods (drip, sprinkler), rainwater harvesting, soil moisture sensors, and the use of drought-resistant crops are among the practices (Rao and Mamatha 2004). Crop management entails a variety of practices designed to maximise crop growth, health, and yield. Crop management practices assist farmers in growing healthy, vigorous crops while minimising pest and disease losses (Reddy et al. 2003). Livestock management is concerned with the health and productivity of animals. Proper housing, feeding, healthcare, breeding, and waste management are all part of it (Khan et al. 2004). In agriculture, financial management entails resource management, budgeting, record-keeping, and financial planning. It’s essential for keeping track of expenses, income, and profits, as well as making sound decisions about investments, equipment purchases, and expansion (Fabozzi 2008). Farmers need marketing and business management skills to effectively sell their produce and manage their farming enterprises as businesses (Theron and Terblanche 2010).

Integrated farming is a comprehensive approach that integrates various agricultural activities on the same farm. It aims to improve resource utilisation, productivity, and sustainability (Gill et al. 2009). Organic farming emphasises the use of natural processes while avoiding synthetic inputs such as pesticides and synthetic fertilisers (Lampkin et al. 2000). Contract farming entails formal agreements between farmers and buyers or companies. Buyers typically provide farmers with inputs, technical assistance, and a guaranteed market for their produce (Otsuka et al. 2016). Multilayer farming involves growing crops in multiple layers or levels, often using vertical spaces such as shelves or racks. This method maximises space utilisation and is especially useful in cities or areas with limited land (Saxena and Rai 2022). Greenhouse farming involves cultivating crops in a controlled environment, typically inside a glass or transparent structure (Rubanga et al. 2019).

Direct Market Access, farmers can connect directly with consumers through many modern agricultural practices, such as farmers’ markets, community-supported agriculture (CSA), and online platforms (Goyal 2010). Price Discovery, farmers who have direct market access are often able to set prices based on actual market demand and supply (De Jong 2002). To increase market access certain farming practices, such as contract farming or value chain integration, can provide farmers with guaranteed markets. These arrangements frequently include contracts with buyers or companies to purchase the farmer’s produce, ensuring that their harvest has a home before it is even grown (Shiferaw et al. 2011). By eliminating middlemen or intermediaries, farmers and consumers can significantly reduce transaction costs. Farmers can now receive a larger share of the final price paid by consumers (Sharma et al. 2017). Farmers are directly accountable to their customers; direct market access encourages them to maintain consistent quality. This may result in improved agricultural practices and higher-quality produce (Mitra 2016). Farmers who participate in direct market access and value chain integration frequently need to improve their skills in production, packaging, marketing, and distribution (Eade 2007).

Bisen and Kumar (2018) has explored various difficulties encountered during the implementation of e-NAM in terms of the three I’s (Infrastructure, Institution, and Information) and advocates for strengthening the back-end of the supply chain through public–private interventions; amending state APMC Acts to encompass e- tendering operations; and broad public understanding of the advantages of e-NAM among organic agriculture farmers. Price discovery is the mechanism of the futures market that generates the best estimate of a commodity’s future spot price (Rout et al. 2021). To avoid arbitrage opportunities, price discovery also entails a fair and transparent selling and purchase of commodities. Trade in the futures market is increasing, although organic agriculture farmers’ participation in the agricultural futures commodities market is small. This is mostly due to a lack of integration in India’s futures and spot markets (Basu 2020) and a lack of information delivery to organic agriculture farmers (Grima 2018). Much study has been undertaken both worldwide and in India to determine the cointegration analysis and pace of adjustment in the futures or cash markets using various methodologies. Some Indian scholars have investigated the price discovery and lead-lag relationship between agricultural commodities derivative futures and spot markets. Except for turmeric, Sehgal et al. (2012) determined that the futures market had a dominant role in price discovery. Basavaraj and Chowdri (2013) observed that the Indian red chilli futures market outperformed the spot market in terms of anticipating future spot prices.

Literature gap in organic farmers’ awareness and e-NAM

The literature gap in the context of organic farmers’ awareness and e-NAM (National Agriculture Market) refers to areas within research and studies where there is a lack of sufficient exploration or understanding. It points to the need for further investigation and analysis to fill these gaps and contribute to a more comprehensive understanding of the topic. Here are some potential literature gaps in the area of organic farmers’ awareness and e-NAM:

  1. a.

    Limited Studies on Organic Farmers’ Awareness of e-NAM: There might be a scarcity of studies specifically focusing on the level of awareness among organic farmers about e-NAM and its functionalities. Research that delves into the extent to which organic farmers are familiar with e-NAM, how they perceive its benefits, and the challenges they face in accessing and utilizing it would be valuable.

  2. b.

    Factors Influencing Organic Farmers’ e-NAM Awareness: There might be a lack of research examining the factors that influence organic farmers’ awareness of e-NAM. This could include their education, location, socio-economic status, access to technology, and extension services. Exploring these factors and their interplay in shaping awareness levels could provide insights for targeted interventions.

  3. c.

    Impact of e-NAM Awareness on Organic Farmers’ Practices: Research gaps might exist in understanding how increased awareness of e-NAM impacts organic farmers’ decision-making and agricultural practices. Studies could investigate whether higher e-NAM awareness leads to changes in marketing strategies, crop choices, income levels, or market access for organic farmers.

Objectives of the study

  1. I.

    To study the organic agriculture farmers’ awareness of operations, adoption and functioning of e-NAM in the study areas.

  2. II.

    To analyze the training programs of e-NAM on modern technologies, production management and advanced farming techniques.

  3. III.

    To access the overall impact on organic agriculture farmers’ growth and development.

Methodology

The study was conducted in the Jajapur district of Odisha in 2022 by adopting Exploratory and Ex-post facto research designs (Giuffre 1997). The e-NAM platform integrated APMCs in Odisha was chosen on purpose for the study. There are 10 Tahsils, 10 Blocks, 311 G. Ps, 1781 Villages and 18 Police stations functioning in the district. 12 G.Ps. (namely Basudevpur, Beruda, Bhubaneswarpur, Bhuinpur, Bichitrapur, Chainpur, Jhalapada, Khairabada, Maheswarpur, Malandapur, Nathasahi, and Panasa) are selected randomly purposively from the Jajpur Block. From each of the selected GPs, twenty e-NAM registered organic agriculture farmers were selected randomly, making a total 240 respondents. The respondent for the study was operationally defined as the organic agriculture farmers who registered and traded with e-NAM in Jajapur APMC of Odisha. The data were gathered using a personal interview approach using a pre-structured interview schedule that included closed-ended questions. To assess responders’ understanding of the operation and characteristics of e-NAM, an export-made exam was developed in conjunction with mandi authorities and agricultural marketing specialists. Organic agriculture farmers’ growth factors of e-NAM were categorized into five phases viz. market growth and developmental factors, training on usage of e-NAM skills, training on modern technology, training on production management factors, and training on advanced farming techniques. It consists of twenty-six items in total and was presented in the form of a five-point Likert-scale format, covering various new factors of e-NAM. The responses were then summarized and analyzed using statistical methods including frequency, percentage, standard deviation, and SmartPLS (Fig. 1) (Jan and Harriss-White 2012).

Fig. 1
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Map of Jajapur district

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Measurement of constructs

The PLS3-SEM method is used in this study. The application of this approach necessitates determining whether the latent variable is formative or reflective. The latent variables are not inherently formative or reflective; this is dependent on the method of analysis. The constructs are being measured. The main difference between measuring reflective and formative constructs is that the reflective construct causes variations in its indicators, whereas the formative construct causes variations in its indicators; thus, the direction of causality in formative constructs is completely reversed in reflective constructs (Bollen 2007).

The reflective variable is a latent variable that exists independently of its indicators’ effects and is the source of its observed measures. When an indicator is removed, the correlation between the remaining indicators and the latent variable remains unchanged (indicator interchangeability effects) (Simonetto et al. 2012). However, the formative variable is a latent variable that is determined by its indicators and is a function of its observed measures (Edwards and Bagozzi 2000). Each observed indicator in formative models describes a different aspect of the latent construct. Thus, removing one or more observed measures from the formative construct results in the removal of a specific part of the construct (Wilcox 2008).

Conceptual framework

The research design is illustrated visually in Fig. 2, making it easier to grasp the hypothesized association of variables. Each arrow begins with the independent variable and ends with the dependent variable. The research design’s graphical depiction demonstrates that two variables experience of organic agriculture farmers (EF) and qualification of organic agriculture farmers (QF) are mediators in the model.

Fig. 2
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Conceptual framework of organic agriculture farmers’ growth

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The model we selected contained five latent variables and every variable was measured through structured questionnaires, just like the first latent variable was training on usage of e-NAM awareness (TNA) and it had four factors for measurement, 2nd latent variable training on modern technology (TMT) and it had five factors for measurement, 3rd latent variable training on production management (TPM) and it had six factors for measurement, 4th latent variable training on advance farming techniques (TFT) and it had five factors for measurement, and 5th latent variable organic agriculture farmers growth and development (FGD) and it had six factors for measurement (Table 1) (Vinzi et al. 2010). The details are:

Table 1 Latent variables and their source
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Hypothesis of the study

  • H1: There is a direct association between the experience of organic agriculture farmers, e-NAM awareness and growth and development.

The hypothesis suggests that there is a direct relationship between organic agriculture farmers’ experience and their awareness of the e-NAM platform, as well as a direct relationship between e-NAM awareness and these farmers’ growth and development. It does not, however, specify the nature of these relationships (e.g., positive or negative) or their strength. To test this hypothesis, data on organic agriculture farmers’ experience (possibly in terms of years engaged in organic farming), their awareness of the e-NAM platform (via surveys or interviews), and various indicators of growth and development (such as yield improvement, income increase, market access, and so on) will be collected.

  • H2: There is a direct association between the experience of organic agriculture farmers, training on modern technology, and growth and development.

In this context, “experience of organic agriculture farmers” signifies the amount of duration or a few years spent by these farmers practising organic agriculture. The term “training on modern technology” refers to the level of education or knowledge these farmers have received in the use of modern agricultural technologies such as improved cultivation techniques, pest control, irrigation, and so on. The phrase “growth and development” most likely refers to the overall progress and improvement of these farmers’ agricultural operations, as well as their socioeconomic circumstances. The hypothesis implies that there is a direct relationship between organic agriculture farmers’ experience and their modern technology training, as well as a direct relationship between modern technology training and these farmers’ growth and development.

  • H3: There is a direct association between the experience of organic agriculture farmers, training in production management and growth and development.

“Training on production management” is most likely education or instruction related to effectively managing various aspects of agricultural production, such as crop planning, resource allocation, pest management, and overall farm management practices. “Growth and development” is most likely referring to the advancement and improvement of farmers’ agricultural activities as well as their overall well-being. The hypothesis proposes that there is a direct relationship between organic agriculture farmers’ experience and their production management training, as well as a direct relationship between production management training and these farmers’ growth and development.

  • H4: There is a direct association among the experience of organic agriculture farmers, training on advanced farming techniques and growth and development.

The term “training in advanced farming techniques” refers to the education or training that these farmers have received in cutting-edge or modern agricultural production methods. These techniques could include novel approaches to soil health, crop management, irrigation, and other practices. “Growth and development” refers to the advancement and improvement of farmers’ agricultural operations, as well as the overall socioeconomic conditions. The hypothesis proposes a direct relationship between organic agriculture farmers’ experience and advanced farming technique training, as well as a direct relationship between advanced farming technique training and these farmers’ growth and development.

  • H5: There is a direct association between the qualification of organic agriculture farmers, e-NAM awareness and growth and development.

“Qualification of organic agriculture farmers” refers to the farmers’ educational attainment or level of formal education. “e-NAM awareness” still refers to farmers’ understanding and awareness of the e-NAM platform. “Growth and development” continues to denote the advancement and improvement of farmers’ agricultural activities as well as their overall well-being. The hypothesis proposes that there is a direct relationship between organic agriculture farmers’ qualification and their e-NAM awareness, as well as a direct relationship between e-NAM awareness and these farmers’ growth and development.

  • H6: There is a direct association among the qualification of organic agriculture farmers, training on modern technology and growth and development.

The term “qualification of organic agriculture farmers” refers to the educational background and level of formal education attained by these farmers. “Training on modern technology” refers to the education or training that these farmers have received in the use of modern agricultural technologies. “Growth and development” refers to the advancement and improvement of farmers’ agricultural operations as well as overall socioeconomic conditions. The hypothesis proposes that there is a direct relationship between organic agriculture farmers’ qualification and their training in modern technology, as well as a direct relationship between training in modern technology and these farmers’ growth and development.

  • H7: There is a direct association between the qualification of organic agriculture farmers, training in production management and growth and development.

In this context, “qualification of organic agriculture farmers” refers to the farmers’ educational background and level of formal education. “Training on production management” continues to represent the education or training that these farmers have received in effectively managing various aspects of agricultural production. “Growth and development” continues to represent the advancement and improvement of farmers’ agricultural activities as well as overall socioeconomic conditions. The hypothesis proposes that there is a direct relationship between the qualification of organic agriculture farmers and their production management training, as well as a direct relationship between production management training and these farmers’ growth and development.

  • H8: There is a direct association among the qualification of organic agriculture farmers, training on advanced farming techniques and growth and development.

In this context, “qualification of organic agriculture farmers” signifies the farmers’ educational background and level of formal education. “Training on advanced farming techniques” refers to the education or training that these farmers have received in innovative and modern agricultural production methods. “Growth and development” continue to represent the advancement and improvement of farmers’ agricultural operations as well as overall socioeconomic conditions. The hypothesis suggests that there is a direct relationship between organic agriculture farmers’ qualification and their training in advanced farming techniques and that there is also a direct relationship between training in advanced farming techniques and these farmers’ growth and development.

Education of organic agriculture farmers

Table 2 shows that 27 organic agriculture farmers out of 240 organic agriculture farmers were educated up to the primary level, 34%, 24%, 19.58% and 7% had done their matric, intermediate, graduation and master’s degree. Only 3.75% of organic agriculture farmers had professional degrees.

Table 2 Educational qualifications of organic agriculture farmers (QF)
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The educational qualifications of organic agriculture farmers can have a variety of effects on e-NAM, which may include:

  1. a.

    Awareness and participation: Farmers with higher education levels, particularly those educated in modern technologies and market dynamics, are more likely to be aware of platforms such as e-NAM. They may be more open to using digital tools and trading online.

  2. b.

    Market understanding: Farmers can benefit from education to better understand market trends, pricing mechanisms, and consumer preferences. This knowledge will allow them to make more informed decisions when listing their organic produce on e-NAM, potentially resulting in lower prices.

  3. c.

    Quality and grading: Educated farmers may have a better understanding of quality standards and grading systems. This can result in improved organic product sorting, packaging, and presentation, making them more appealing to e-NAM buyers.

  4. d.

    Negotiation skills: Farmers with higher education may have better negotiation skills, allowing them to engage with buyers more effectively on the platform. This can result in better deals and increased profits.

  5. e.

    Digital literacy: The e-NAM platform requires a certain level of digital literacy to navigate and operate effectively. Farmers who have received an education are more likely to be comfortable with online platforms and technology, making it easier for them to list their products, interact with buyers, and complete transactions.

  6. f.

    Information access: Educated farmers are more likely to access and comprehend market data, such as current prices, demand trends, and supply requirements, available on e-NAM. This information can assist them in developing production and marketing strategies.

  7. g.

    Record keeping and compliance: Compliance with specific standards and certifications is frequently required in organic farming. Educated farmers may be more conscientious about keeping records and adhering to regulations, which can increase their credibility on e-NAM.

  8. h.

    Adoption of best practices: Education can expose farmers to best practices in organic farming, resulting in higher-quality produce. As a result, more buyers will be drawn to e-NAM, which will lead to repeat business.

While educational credentials can be beneficial, they are not the only determinants of success on e-NAM. Regardless of educational background, infrastructure, access to technology, market demand, government policies, and overall platform usability, all play important roles in influencing a farmer’s experience and outcomes on e-NAM.

Agricultural experience of organic agriculture farmers

Table 3 shows that 13% of organic agriculture farmers had agricultural working experience of up to 5 years. 27.5%, 30.83%, and 15.84% of organic agriculture farmers had 6–10 years, 11–15 years, and 16–20 years of agricultural working experience, and only 12.5% of organic agriculture farmers had more than 20 years experience.

Table 3 Agricultural experiences of organic agriculture farmers (EF)
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The agricultural experiences of organic agriculture farmers can have a significant impact on their engagement and outcomes in the National Agriculture Market (e-NAM). The following are some of the factors that may influence their participation in e-NAM:

  1. a.

    Produce quality: Farmers with extensive agricultural experience, especially organic farming, are more likely to understand cultivation practices that result in high-quality produce. In the e-NAM market, where buyers frequently prioritise quality, this can provide a competitive advantage. Organic farmers may be able to charge a premium for their products.

  2. b.

    Market knowledge: Seasoned organic farmers have a better understanding of market trends, demand patterns, and consumer preferences. Because of this understanding, they can tailor their production and marketing strategies to market demands, increasing their e-NAM success.

  3. c.

    Variety and diversity: Farmers with farming experience are more likely to have experimented with various crop varieties and diversified their produce. This could lead to a wider range of offerings on e-NAM, attracting a wider range of buyers.

  4. d.

    Trust and reputation: Long-term organic farmers frequently build a reputation for producing reliable, high-quality organic products. This reputation may inspire buyer confidence in e-NAM, resulting in repeat business and positive feedback.

  5. e.

    Effective communication: Experienced farmers have better communication skills when interacting with buyers and negotiating deals. In the e-NAM online trading environment, effective communication is critical.

Organic agriculture farmers’ experiences are critical in their engagement with e-NAM. Their farming knowledge, skills, and insights can help to improve product quality, communication, and transaction success on the platform.

Measurement model result assessment

The goal of testing reliability is to determine the material’s internal consistency. Cronbach’s Alpha is a popular reliability test. According to Taber (2016), a decent reliability test number is more than 0.7. Meanwhile, a composite dependability value greater than 0.6 was considered reliable (Hair et al. 2019; Crandall et al. 2011). According to Table 4, all of the items were dependable and met the value given by the scholar. Hair et al. (2019) suggested that the factor loading threshold be set between 0.5 and 0.7. The loading factor was all greater than 0.5. Moreover, the average variance extracted (AVE) is defined as the overall weighted mean of such construct-related components' squared loadings, and it is a common metric for assessing convergent validity. Whenever the AVE is 0.5 or above, it suggests that the construct explains more than half of its component variation (Hair et al. 2019). Table 4 shows that Cronbach’s Alpha and composite reliability values are larger than 0.7, while AVE values are greater than 0.5. As an outcome, the convergent validity of such constructs is established.

Table 4 Measurement model result assessment
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The value of Cronbach’s Alpha for Training on the usage of e-NAM awareness (TNA), Training on modern technology (TMT), Training on production management (TPM), Training on advanced farming techniques (TFT), and Organic agriculture farmers growth and development (FGD) 0.825, 0.824, 0.775, 0.838, and 0.916 respectively. All the values are greater than 0.7. The value of CR was also measured to check the internal consistency and reliability of constructs. The results show that the values for TNA, TMT, TPM, TFT, and FDG are 0.894, 0.889, 0.845, 0.877, and 0.945. All the values of CR are greater than 0.7. The results of CR indicate that the model possesses an acceptable level of reliability (Chin 1998; Hair et al. 2011). The AVE values of the latent variables were also computed and reflected in Table 4. The AVE values for TNA, TMT, TPM, TFT, and FDG are 0.726, 0.654, 0.716, 0.622, and 0.749 respectively. All these values are greater than 0.5 which shows that there exists an acceptable level of convergent validity (Chin 1998; Hair et al. 2017).

Composite reliability (CR) and average variance extracted (AVE)

A value of CR = 0.70 or higher is recommended for adequate composite or construct reliability (Nunnally and Bernstein 1994). Table 4 shows that all of the constructs had composite reliabilities greater than the recommended 0.70. The results also show that the AVE estimate for all of the constructs is greater than or equal to the recommended threshold of 0.50. (Fornell and Larcker 1981). This demonstrates good composite or construct reliability for the constructs in this study (strong tie, weak tie, and opportunity recognition).

Discriminant reliability

The Fornell-Larcker criterion was used to verify and confirm discriminant validity by assessing the extent to which each latent variable was distinct from other constructs (Chin 1998; Hair et al. 2017). Table 5 displays the results of this criterion (Fig. 3) (Lutz et al. 2017).

Table 5 Fornell-Larcker criterion
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Fig. 3
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The structural model with estimated parameters

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Fornell and Larcker (1981) proposed that each latent variable should have a higher correlation with its indicators than other latent variables. To ensure discriminant validity, the AVE of each construct should be greater than the highest squared correlation of the latent variable with any other latent variable. The AVE values of all latent variables in our case are as follows: TNA is 0.854, TMT is 0.753, TPM is 0.826, TFT is 0.647, FDG IS 0.586, QF is 1.000 and EF is 1.000. The values all indicate that the self-AVE of each latent variable is greater than the other variables (Table 6). (Chin 1998; Götz et al. 2009).

Table 6 The heterotrait-monotrait ratio (HTMT)
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The heterotrait-monotrait ratio (HTMT)

According to Henseler (2017), when using the HTMT criterion to assess discriminant validity, if the HTMT value is less than 0.90, discriminant validity has been established between two reflective constructs. The p-values of all constructs tested by bootstrapping the heterotrait-monotrait ratio show statistical significance in our case, and the formative constructs are valid. The variance inflation factor is used to calculate the multicollinearity test of Manifest variables in a formative block (VIF).

Testing of hypothesis

Hair et al. (2017) proposed evaluating the structural model by looking at the R2, beta (β), and corresponding t-values using a bootstrapping procedure with a resample of 5000. They also suggested that, in addition to these basic measures, researchers report predictive relevance (Q2) and effect sizes (f2).

According to Sullivan and Feinn (2012), while a p-value can inform the reader whether an effect exists, it cannot reveal the magnitude of the effect. Both substantive significance (effect size) and statistical significance (p-value) are important results to report when reporting and interpreting studies (p.279). Hahn and Ang (2017) summarized some of the recommended rigour in quantitative study reporting, including the use of replication studies, effect size estimates and confidence intervals, Bayesian methods, Bayes factors or likelihood ratios, and decision-theoretic modelling. As previously suggested, we have included effect sizes and confidence intervals in our reporting (Table 7). H1 the experience of organic agriculture farmers, training on modern technology, and growth and development (β =  −0.014, t = 1.753, p = 0.037), H2 the experience of organic agriculture farmers, training on modern technology, and growth and development (β = 0.574, t = 7.148, p = 0.000), H3 the experience of organic agriculture farmers, training on production management and growth and development (β = 0.359, t = 15.681, p = 0.000), H4 the experience of organic agriculture farmers, training on advance farming techniques and growth and development (β = 0.469, t = 6.487, p = 0.023), H5 the qualification of organic agriculture farmers, e-NAM awareness and growth and development (β = 0.178, t = 9.285, p = 0.017), H6 the qualification of organic agriculture farmers, training on modern technology and growth and development (β = 0.690, t = 8.147, p = 0.000), H7 the qualification of organic agriculture farmers, training on production management and growth and development (β = 0.753, t = 2.147, p = 0.068), and H8 the qualification of organic agriculture farmers, training on advance farming techniques and growth and development (β = 0.268, t = 3.486, p = 0.004) positively influenced of e-NAM platform are explaining 68.8% of the variance in growth and development of farmer.

Table 7 Results of the hypothesis testing
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The discussion on the educational qualification and experience of organic agriculture farmers about their awareness of the National Agriculture Market (e-NAM) and training activities

Importance of educational qualification and experience

(a) Educational qualification and experience: The educational background of organic agriculture farmers varies widely, from those with limited formal education to those with higher degrees in agriculture or related fields. Farmers with higher educational qualifications might have an advantage in understanding and adopting new technologies and market platforms like e-NAM. Experience in organic farming, regardless of formal education, contributes to practical knowledge and expertise in sustainable agricultural practices. (b) e-NAM awareness: The level of awareness about e-NAM among organic farmers might be influenced by their educational background and access to information sources. Farmers with higher education might be more likely to seek out information about e-NAM and its benefits, while others might rely on peer networks or extension services. Training and outreach programs focused on e-NAM for organic farmers can play a significant role in enhancing awareness. (c) Growth and development: Farmers who effectively use e-NAM to market their organic produce might be able to invest in better farming practices, infrastructure, and diversification. Organic farmers need to be acquainted with modern agricultural technologies that can improve efficiency and productivity. This could include the use of mobile apps for pest management, soil testing kits, and precision farming tools. This training can be tailored to farmers with varying levels of technological literacy. It should emphasize the benefits of e-NAM in terms of market access, price transparency, and reducing intermediaries (Considine et al. 2005).

Various training programmes to increase knowledge and experience

(a) Training on usage of e-NAM awareness: e-NAM (National Agriculture Market) is a digital platform that connects farmers with markets to facilitate efficient trading. Training on e-NAM is essential to help organic farmers understand how to register, list their produce, participate in online auctions, and effectively negotiate prices. This training can be tailored to farmers with varying levels of technological literacy. It should emphasize the benefits of e-NAM in terms of market access, price transparency, and reducing intermediaries. (b) Training on modern technology: Organic farmers need to be acquainted with modern agricultural technologies that can improve efficiency and productivity. This could include the use of mobile apps for pest management, soil testing kits, and precision farming tools. The training should not only introduce these technologies but also provide practical demonstrations and hands-on experience to ensure effective implementation. (c) Training on production management: Proper production management is crucial for organic farmers to maintain quality and yield. Training in this area could cover crop planning, rotational farming, seed selection, and soil health management. Farmers should learn about sustainable practices that enhance soil fertility, conserve water, and minimize the impact of pests and diseases. (d) Training on advanced farming techniques: Advanced techniques such as hydroponics, vertical farming, and integrated pest management can significantly enhance organic farming practices. This training should emphasize the benefits and intricacies of these techniques, offering step-by-step guidance on their implementation. (d) Farmers growth and development training: This comprehensive training focuses on the holistic development of organic farmers as entrepreneurs and community members. It could cover aspects like financial management, business planning, marketing strategies, and building networks. These skills empower farmers to make informed decisions and seize growth opportunities. (e) Tailoring training to farmers’ needs: Effective training should take into account the specific needs, challenges, and existing knowledge of organic farmers. Conducting needs assessments before training can help identify areas where farmers require the most support.

Hands-on learning and demonstrations

Practical demonstrations and interactive sessions are crucial for hands-on learning. Farmers can better understand concepts when they see them in action. Continuous Learning Approach, training should not be a one-time event but rather an ongoing process. Follow-up sessions, refresher courses, and updates on new practices and technologies are essential. Collaboration and Peer Learning, encouraging organic farmers to share their experiences and learn from each other can be highly beneficial. This can happen through discussion groups, community meetings, and field visits.

In conclusion, a well-rounded training program for organic farmers should encompass e-NAM usage, modern technology adoption, production management, advanced farming techniques, and personal growth. By empowering organic farmers with these skills, they can effectively navigate the challenges of modern agriculture, improve their livelihoods, and contribute to the sustainable development of the agricultural sector.

Conclusion

The findings of the study reveal that the qualification of organic agriculture farmers is an important factor that affects the performance of the e-NAM platform. It means that the qualification of organic agriculture farmers of the e-NAM platform is higher, and the growth and development of organic agriculture farmers performing better as compared to other organic agriculture farmers (Viyyanna Rao 2020; Deshmukh and Patil 2021). The study also revealed that the awareness of e-NAM on competency and efficiency to acquire new knowledge of organic agriculture farmers mediates the relationship between qualification and performance of e-NAM platform (Joumard et al. 2017). The experience of agricultural production does have a good impact on training programmes of the e-NAM platform (Sahoo 2019). The results are also supported by Kaur et al. 2021 who found that training and qualification of organizational organic agriculture farmers have a positive impact on training programmes of the e-NAM platform. DAC&FW, A. S. C. I., & NSDC, M. e-Bulletin 2020 found that training, technology practices, and perceived qualification positively influence the performance of small and medium organic agriculture farmers.

Overall, the e-NAM platform has several growth factors that contribute to the economic growth and development of organic agriculture farmers in India (Bhusanar and Singh 2019). Direct access to markets, price discovery, improved market access, reduced transaction costs, improved quality control, and capacity building are some of the key factors that contribute to the success of the platform and the growth of organic agriculture farmers who use it (Bisen and Kumar 2018). According to Swaminathan et al. 2018, the e-NAM platform provides several advanced production factors for organic agriculture farmers that can help to improve their agricultural productivity and income. Training programs can be very effective in helping organic agriculture farmers to increase their production and improve their income. By providing organic agriculture farmers with the necessary skills and knowledge, training programs can help them increase their production and achieve the goal of doubling their income (Raju et al. 2022a, b). These programs can be provided by government agencies, NGOs, private sector companies, and academic institutions (Samantaray et al. 2021). Training in advanced farming techniques can be very beneficial for organic agriculture farmers in increasing their production and improving their income. By providing training on advanced farming techniques, organic agriculture farmers can improve their productivity, reduce costs, and increase their income. Such training can be provided by government agencies, NGOs, private sector companies, and academic institutions (Meena et al. 2019). The training can be delivered through various formats such as classroom training, on-farm training, and online training. Training programs for organic agriculture farmers on modern technology can help to increase the adoption and effective use of these technologies. Such training programs can be provided by government agencies, NGOs, private sector companies, and academic institutions (Ahmad et al. 2012). These training programs can cover a range of topics, including the benefits and challenges of modern technologies, the use and maintenance of machinery, the use of precision farming technologies, and the importance of sustainable farming practices. By providing organic agriculture farmers with the necessary skills and knowledge, training programs can help to improve their productivity, income, and overall livelihoods (Dubey and Srivastava 2016).

Here are some policy recommendations that could help in the development of organic agriculture farmers at the e-NAM platform:

  1. a.

    Enhance accessibility: The government should make efforts to enhance accessibility to the e-NAM platform by improving connectivity and digital infrastructure in rural areas. This would enable more organic agriculture farmers to participate in e-NAM and access better markets for their produce.

  2. b.

    Expand the reach of e-NAM: The government should aim to expand the reach of e-NAM by integrating more mandis into the platform. This would provide more opportunities for organic agriculture farmers to sell their produce and help in the development of the agriculture sector. (c) Increase transparency: The government should ensure that there is greater transparency in the operations of e-NAM. This could be done by publishing data on market prices, transactions, and volumes. This would help organic agriculture farmers to make more informed decisions on when and where to sell their produce.

  3. c.

    Provide training and support: The government should provide training and support to organic agriculture farmers on how to use the e-NAM platform effectively. This could be done through training programs, workshops, and on-site support. This would help to build the capacity of organic agriculture farmers to use the platform and improve their market knowledge.

  4. d.

    Encourage the use of technology: The government should encourage the use of technology by organic agriculture farmers, such as mobile apps and sensors, to improve their farming practices and access better markets. This could be done through training programs, subsidies, and incentives for technology adoption.

  5. e.

    Encourage aggregation: The government should encourage the formation of farmer-producer organizations and encourage organic agriculture farmers to aggregate their produce for sale on e-NAM. This would help to improve the bargaining power of organic agriculture farmers and enable them to access better markets and prices for their produce.

  6. f.

    Ensure payment security: The government should ensure payment security for organic agriculture farmers by establishing a secure payment mechanism on e-NAM. This would help to build trust among organic agriculture farmers and encourage them to participate in e-NAM.

By implementing these policy recommendations, the government can help in the development of organic agriculture farmers at the e-NAM platform and enable them to access better markets and prices for their produce.