Introduction

Agriculture plays a significant role in the development of an economy like India. It provides a livelihood for more than one-third of the population and contributes about 20% to the national GDP (GoI 2022). Further, with the continuously growing population in India, the demand for food increased, which put pressure on agricultural land (Chatterjee 2011). The technical change promoted through the green revolution in the 1960s played a significant role in enhancing agricultural productivity and helped India become self-sufficient in food grains. But its impact did not last for a longer period, and the sustainability of the Indian agricultural system has become questionable. The intensive use of agrochemicals such as fertilizers and pesticides/insecticides has decreased soil fertility, resulting in stagnation of agricultural productivity in recent years and environmental degradation (Narayanan 2005; Priya and Singh 2022). Hence, the adoption of organic farming is considered one of the strategies for sustainable agricultural growth. It has been proven to be one of the sustainable methods to address environmental challenges and enhance productivity (Aryal et al. 2018; Rigby and Cáceres 2001; Tey et al. 2014).

“Organic farming is a holistic production management system which promotes and enhances agro-ecosystem health, including biodiversity, biological cycles, and soil biological activity” (FAO 2015). It is considered an alternative to chemical farming that largely avoids synthetic chemicals and depends on crop rotation, crop residues, green manure, bio-fertilizers, and bio-pesticides (Azam & Shaheen 2019; Narayanan 2005; Ramesh et al. 2005). National and international research institutions have taken several initiatives to promote organic farming. Similarly, in India, central and state governments and various organizations and NGOs (e.g., APEDA, ICAR) are promoting organic farming. Moreover, Pramparagat Krishi Vikas Yojana (PMKVY), Mission Organic Value Change Development for North Eastern Region (MOVCDNER), National Mission on Oilseeds and Oil Palms (NMOOP), National Food Security Mission (NFSM), Rashtriya Krishi Vikas Yojana (RKVY), and National Project on Organic Farming (NPOF) are some of the initiatives taken by the Indian government for its promotion.

Because of several policy initiatives, the cultivated area under organic farming in India increased from 0.78 million ha in 2010–2011 to 2.66 million ha in 2021 (Manik & Tanwar, 2021). As per the FIBL & IFOAM Year Book 2022, India ranked 4th in terms of area under organic cultivation. Although India’s share of organic agricultural land has increased from 0.1% in 2005 to 1.08% in 2018, the growth rate is still low, and farmers hesitate to convert conventional farming to organic farming (Chadha & Srivastava 2020).

Additionally, the Indo-Gangetic Plain is agriculturally the most fertile region of the country (Aryal et al. 2018). However, the toxic chemicals used in intensive cultivation in the Gangetic plains eventually end up in the water bodies and pollute the river system (Shah & Parveen 2021). Hence, to curb it, the Indian government runs a campaign to develop organic farming corridors along the Ganga River in five states through which it passes (starting from Gangotri in Uttarakhand to Ganga Sagar in West Bengal). The main aim of the campaign is to promote organic farming clusters in a 5 km stretch on both sides of the Ganga River. Moreover, the regional governments of Uttarakhand and Uttar Pradesh are also encouraging the practice of organic farming in the Ganga River basin with a scheme called the Organic Agriculture Development Scheme or Jaivik Krishi Vikas Yojan (2020). However, the rate of adoption is slow in the area. There are various factors studied in the literature that affect the adoption of organic farming; however, it differs in the geographic and biophysical characteristics of the area. Hence, there is a need to conduct study for a better understanding of the adoption rate among farmers in the Indo-Gangetic Plain.

In the existing literature, the significant factors that affect the adoption of sustainable agricultural practices, including organic farming, have been divided mainly into six categories, viz. socio-economic, agro-ecological, institutional, technological, financial, and psychological (Ashari et al. 2017; Knowler & Bradshaw 2007; Lesch & Wachenheim 2014; Melisse 2018; Mozzato et al. 2018; Priya & Singh 2022). The common factors in the socio-economic dimension are gender, age, education, ethnicity, experience, household size, and household income. It has been observed in the studies done by Kassie et al. (2020) and Okon & Idiong (2016) that aged farmers are less likely to adopt new technologies due to their risk aversion behavior. Further, education plays an essential role in adoption; higher education often introduces farmers to new ideas and enhances their environmental concerns (Digal & Placencia 2019; Joshi et al. 2019; Xie et al. 2015).

Since organic farming is labor-intensive, farmers with larger household sizes are expected to adopt organic farming earlier than those with smaller household sizes (Tey et al. 2014). Further, more investment in improved technologies is significantly affected by higher farming experience (Ganpat et al. 2014; Lemeilleur 2013). However, Kunzekweguta et al. (2017) and Srisopaporn et al. (2015) find the farming experience to be an insignificant variable in the adoption. Additionally, farmers with larger farms are more willing to invest in new technologies due to their greater capacity for investment and risk undertaking (Rajendran et al. 2016; Tey et al. 2014). However, literature also shows farm size as an insignificant adoption variable (Chichongue et al. 2020; Laosutsan et al. 2019).

The extension services by various agencies such as the government, NGOs and farmers provide training to the farmers and motivate them to adopt sustainable agricultural practices (Chichongue et al. 2020; Eliyas & Sumathi 2019; Okon & Idiong 2016). Further, farmers generally avoid taking risks by shifting from one farming system to another. Hence, financial factors like crop insurance and access to credit play a significant role in the adoption (Laxmi & Mishra 2007; Tey et al. 2014). Moreover, the literature shows that farmers’ perceptions and attitudes toward sustainable farming influence their adoption. They are more likely to adopt sustainable farming if they perceive that adoption would reduce the input cost and benefit human health and the environment (Joshi et al. 2019; Sarker et al. 2005; Sriwichailamphan et al. 2008).

Although several studies examine the factors affecting the adoption of organic farming globally, we hardly find any study on factors in the Middle Ganga River basin in India. Further, farmers’ opinion towards organic farming also plays an essential role in the adoption. Therefore, it is important to understand the adoption behavior of the farmers. Thus, this study aims to (i) identify the factors that affect the adoption of organic farming in the Middle Ganga River basin, India, and (ii) investigate farmers’ perceptions towards adopting organic farming in the study area. Therefore, by identifying significant factors associated with the adoption of organic farming, the findings of the current study can provide better information to the government and policymakers for designing effective plans for promoting organic farming in India. This study was motivated to understand the reasons for the non-adoption and adoption of organic farming among farmers in India, irrespective of significant efforts taken by the central and state governments. Such an understanding will help to show the different prospects available to enhance the adoption of organic farming for sustainable agricultural development in the study area. Further, this study is also important in accumulating the efforts to encourage sustainable agricultural practices that are environmentally sound and economically viable.

The sections of the current paper are designed as follows: in the “Materials and methods” section, we describe the materials and methods used in the study. The “Results” section discusses the results regarding the descriptive statistics of variables used in the model and the estimation of factors affecting the adoption. Further, in the “Discussion” section, the discussion of empirical results has been done. Finally, concluding remarks are provided in the “Conclusion” section.

Materials and methods

A cross-sectional study was conducted in the Middle Ganga River basin India. The Middle Ganga River basin lies from Haridwar in Uttarakhand to Varanasi in Uttar Pradesh. Two districts, Haridwar and Bulandshahr, have been chosen for the primary data collection from the Middle Ganga River basin. Out of these two districts, 20 villages were selected for the final data collection of 600 farmers (organic = 300, conventional = 300). The data were collected through a semi-structured questionnaire. The questionnaire was divided into two sections: the first section includes the socio-economic, bio-physical and demographic characteristics of the farmers, whereas, in the second section, qualitative data regarding their perception towards organic farming were asked.

To study the factors affecting the adoption of organic farming, a binary logistic regression model has been applied for data analysis, which helps to predict the probability of occurrence of the events with specific sets of independent variables. This model is commonly used in the literature to assess the relationship between adoption and the associated factors (Digal & Placencia 2019; Mlenga 2015; Okon & Idiong 2016; Xie et al. 2015). The estimated model’s results will help to identify the factors that show a statistically significant relationship with the dependent variable, i.e., adoption. The standard functional form of a logit model is given by:

$$Logit=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\beta_{3}X_{3}+\beta_{4}X_{5}+\dots\dots\dots+\beta_{n}X_{n}+\varepsilon$$

where β0 is a constant, β’s are the parameters, X’s are the independent variables, logit is the log of the odds ratio and can be shown as:

$$Logit=\mathrm{Log}\frac{{P}_{i}}{1-{P}_{i}}$$

where Pi is the probability of the dependent variable taking the value 1 and Pi/1-Pi is the odds ratio. The higher the odds ratio, the higher the chances of the dependent variable taking the value 1. In the model applied here, the dependent variable was the adoption of organic farming; adopter is represented by 1 and non-adopter by 0. The functional form of the logistic regression model is given below:

$$\begin{array}{c}Logit\;\left(adopter\right)=\beta_0+\beta_1region+\beta_2gender+\beta_3marital\;status+\beta_4social\;category+\beta_5\\education+\beta_6HHsize+\beta_7farmsize+\beta_8livestock+\beta_9training+\beta_{10}logexp+\beta_{11}\\loginc+\beta_{12}logdismandi+\varepsilon\end{array}$$

Additionally, to summarize the impact of selected independent variables on the adoption of organic farming, marginal effects were also calculated. Marginal effects in logistic regression measure the probability of a change in the dependent variable due to a change in an independent variable. In contrast, regression coefficients show only directional change (Serebrennikov et al. 2020). Further, post-estimation tests were also used to validate the model. A variance inflation factor (VIF) was estimated to check the multicollinearity among the independent variables. Further, Hosmer–Lemeshow test was also used to test the model's goodness of fit. Previous literature about the adoption of organic farming includes various variables, viz. age, education, gender, extension services, training, farm size, perception towards organic farming, etc. (Knowler & Bradshaw 2007; Lesch & Wachenheim 2014; Mutyasira et al. 2018; Priya & Singh 2022). Therefore, initially, we included multiple variables for data analysis, but due to the high correlation among the variables, we narrowed down only those variables which were not significantly correlated. For example, age was highly correlated with years of farming experience; hence we deleted the age variable from the analysis. Finally, this study uses the following variables shown in Table 1.

Table 1 Variables and description of variables used in logistic regression
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Results

Descriptive statistics

Table 2 describes the overall descriptive statistics of variables used in estimating the model. It shows that, out of the total sample of 600 farmers, 569 were male and only 31 were female. Moreover, most of the farmers belonged to OBC social category (66%), followed by the general (28.33%) and SC (5.68%) categories. The adoption level among general social category farmers was higher than other categories since 70% of total general category farmers are adopters. Table 2 further demonstrates that compared to non-adopters, a more significant percentage of adopters have a primary and secondary level of education, approximately 62% and 58%, respectively. The mean education level of the total sampled farmers was middle school, followed by secondary and senior secondary. In contrast, it is secondary level among adopters and senior secondary level among non-adopters. But the adoption rate among farmers with primary education is the highest (61.70%). Among all, 67.17%of farmers have taken training, of which 63.77 were adopters of organic farming.

Table 2 Descriptive statistics of variables used in logistic regression (N = 600)
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Moreover, the adopter farmers have a high average farming experience compared to non-adopter farmers, i.e., 35 and 23 years, respectively. Similarly, adopter farmers’ average monthly household income was more than two times higher than non-adopter farmers. Further, there is not much difference between the average household size among both categories; it is 5 for adopters and 4 for non-adopters.

Logistic regression

The results of the empirical model in Table 3 indicate that variables like region, social category, training, log of experience, and log of income play a statistically significant role in adopting organic farming in the study area. The positive significance (at a 5% level of significance) results of the region show that farmers belonging to the Haridwar region are more likely to adopt organic farming than Bulandshahr. Further, the social category also plays a vital role in adoption. The negative value of coefficients and marginal effects (dy/dx) indicate that farmers belonging to OBC and SC categories have approximately 16 and 21% fewer chances of adopting organic farming than the general category. Interestingly, the higher likelihood of adoption is associated with those with primary and secondary education levels; however, the adoption rate is 13 and 21%, respectively, in these two education categories.

Table 3 Estimates of logistic regression for the adoption of organic farming
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On the other hand, the insignificant values of gender, marital status, household size, farm size, livestock, and distance from mandi (local market) show that adoption decision is not much affected by these variables in the study area. Further, farmers who have received any training on organic farming have a 22% greater likelihood of adopting organic farming. Additionally, farmers with more experience in farming and a high monthly household income have higher chances of adoption since their coefficient values are positively significant at a 1% significance level. The marginal effects show that farmers with higher income and experience have approximately 60 and 35% higher likelihood of adoption.

Moreover, the p-value of the model shows that model is statistically significant, and 81.83% is correctly classified. The Hosmer–Lemeshow (HL) goodness of fit test can be utilized to get an equivalent summary of the test statistic for the sample authentication and risk prediction (Midi et al. 2010). The nominal value of HL test shows no problem with the model or there is no risk in prediction. Further, the variables have no multicollinearity since all variables’ variance inflation factor (VIF) values are less than 10.

Perceptions of the farmers regarding adoption and non-adoption

Besides the above-discussed factors that affect the adoption, the perception of the farmers regarding organic farming also plays a significant role in adoption. Table 4 explains the reasons for conventional farmers’ non-adoption of organic farming. The primary reason for the non-adoption of organic farming is a lack of financial support, followed by lower yields. Previous study done by Digal & Placencia (2019) also proves low productivity as a primary reason for non-adoption. Difficulties in finding a market for their organic produce further demotivates farmers to shift from conventional to organic farming.

Table 4 Reasons for non-adoption of organic farming by non-adopters (n = 300)
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Additionally, 85% of conventional farmers give the reason for not adopting organic farming as low profit. However, only twelve percent of farmers perceived the organic input cost as higher than the chemical inputs. Still, they are not adopting it due to their perception of lower yield and unavailability of markets.

On the other hand, the common reasons for the adoption of organic farming by adopters are shown in Table 5. Ninety-five percent of organic farmers agreed that they adopted organic farming due to its potential positive impact on human health. However, 91% of organic farmers believed that organic farming is not profitable, but they adopted it only because of government incentives (90%). Nevertheless, 87% of farmers also accepted that soil and water conditions are deteriorating due to the overuse of agrochemicals.

Table 5 Reasons for adoption of organic farming by adopters (n = 300)
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Discussion

The current study determines factors affecting the adoption of organic farming in the Middle Ganga Region in India. The estimated results of regression analysis show that region, social category, education, training, experience, and household income are among the factors that significantly influenced farmers' decision to convert to organic farming. The active involvement of the governments through various policies can significantly increase the chance of greater adoption. For example, in Sikkim, the state government opted Organic Mission plan in 2003 as an initial step to becoming the first organic state by banning the import of chemical inputs, which resulted in Sikkim becoming the first fully organic state in India (Govt. of Sikkim 2022).

Similarly, the results of the study show that farmers in the Haridwar region are more likely to adopt organic farming; this might be because the region belongs to the state of Uttarakhand, where central and various state agencies are focusing majorly on promoting organic farming to make the state fully organic. For example, the state has established a dedicated nodal agency, Uttarakhand Organic Commodity Board (UOCB) (2003), to promote sustainable agricultural development through organic farming (Meena & Sharma 2015). The UOCB aims to provide training and organize seminars/exhibitions to promote organic products in the state. It also provides marketing and certification for organic products, resulting in 12 villages in Uttarakhand being declared bio-villages (Meena & Sharma 2015). Hence, we can say that government plays a significant role in promoting and adopting organic farming with a strong policy framework.

Further, our findings indicate that farmers belonging to SC and OBC social categories are less likely to adopt organic farming since coefficient values are negatively significant. These results are similar to the studies conducted by Aryal et al. (2018) and Singh & Sharma (2019) in the Indo-Gangetic plains of Bihar & Haryana and Rajasthan, respectively. Moreover, it has been observed in previous literature that education plays a significant role in adoption as it enhances farmers' knowledge and influences them to adopt (Aryal et al. 2018; Digal & Placencia 2019; Singh & Sharma 2019). But the estimated results of the current study indicate (Table 3) that farmers with only primary and secondary education levels are more likely to adopt organic farming. Farmers who are illiterate or have higher education are not interested in organic farming; it might be because 90% of farmers have opted for organic farming in the study area because of government incentives and support (Table 5).

Moreover, farmers with longer years of experience tend to be more receptive to adopting organic farming (Digal & Placencia 2019; Giannakis 2014). Possibly, due to the high farming experience, farmers could observe the adverse effects of input-intensive farming on high input costs and environmental degradation. The findings of the current study also indicate that farmers with more experience in farming are more likely to adopt organic farming. Similar results have been found by Kumar et al. (2010) and Lemeilleur (2013) in their study done in India and Peru, respectively.

Similarly, training in agriculture plays a positively significant role in adopting organic farming. Because the training provided by various governmental and non-governmental agencies disseminates information and enhances farmers’ technical skills, which helps to increase the adoption rate (Joshi et al. 2019; Kafle 2011; Raghu et al. 2014). Further, the results of descriptive statistics also justify that 63.77% of farmers have shifted farming from non-organic to organic farming after training and 78.17% of farmers who have not received training are non-adopters (see Table 2).

Additionally, farmers’ decision to convert to organic farming is an economic decision. Profitability is the most crucial factor for a farmer in making a farming decision (Koesling et al. 2008). The risk of having poor financial prospects demotivates farmers to adopt it due to initial yield loss in the transition period (Uematsu & Mishra 2012). And farmers with a high level of monthly household income have the capacity to bear that risk. The results of this study analyze that the monthly household income of the farmers significantly affects the adoption rate in the Middle Ganga River basin in India. Similar results have also been observed in the studies conducted by Laosutsan et al. (2019) and Senanayake & Rathnayaka (2015) in Thailand and Sri Lanka, respectively.

Moreover, farmers’ perceived attitude towards organic farming also plays an essential role in its adoption. The non-adopters perceived that lack of financial support, low level of yield and difficulties in finding the markets were the major hurdles to adopting. Hence, more focus should be given to the establishment of the organic market and providing them premium prices at least during the transition period (initials 2–3 years when production declines). Our results are consistent with the literature where production and marketing barriers are discovered as significant constraints to adopting organic farming (Nandi et al. 2015; Panneerselvam et al. 2012). Further, adopter farmers were more aware of the environmental degradation and adverse impact of chemical farming on their health. Hence, more seminars and extension services regarding organic farming can make farmers aware and motivate about its potential benefits on environment and human health.

The above discussion concludes that there are multiple socio-economic factors (ranging from region to training and family income) that affect the rate of adoption of organic farming. However, there might be some other important factors, for example socio-political, socio-cultural and psychological which can also affect the adoption rate, which have not studied in the current study. Thus, this study can open the scope of future research for the parallel study.

Conclusion

In view of improving the sustainability of agricultural systems in India, this study aimed to identify the factors that could affect the adoption of organic farming in the Middle Ganga River basin, India. The results of binary logistic shows that the significant factors that affect the adoption of organic farming were region, education, social category, farming experience, training and monthly household income. This analysis has important policy implications as it highlights the areas where the government can intervene in order to promote organic farming among conventional farmers. For example, farmers in the Haridwar region with primary and secondary education levels and the general social category are more likely to adopt organic farming. Further, farming experience, training, and monthly household income are other significant factors affecting farmers' adoption rates. Therefore, policies must be focused on educating farmers by organizing various training programs and extension services. It will help spread awareness of environmental pollution due to conventional farming.

Moreover, although farmers believe that the input cost under organic farming is lower, they did not adopt it for various reasons. Lack of financial support, lower yield levels and unavailability of markets under organic farming are significant reasons that discourage farmers from adopting it. Hence, the policy must be focused on establishing markets, raising yield through reorientation of research and development (R&D), and providing premium during the initial years of conversion. Further, another reason for less adoption is their belief in low profitability in organic farming. Therefore, various seminars and programs should be organized to motivate and guide them on how it becomes profitable. Moreover, the adopter showed attention to continuing it because of its potential impact on human health, followed by government incentives and support. The study suggests that government incentives can play a significant role in adopting organic farm practices in the Middle Ganga River basin. Further, this research also has certain limitations such as the limited number of variables due to the limited resources, hence more comprehensive research including more variables can be conducted for a more holistic picture. Moreover, this study limits the area to two districts in Middle Ganga river basin, hence in order to understand the impact of various factors on the adoption of organic farming in the entire Ganga river basin, the study area can be expanded to include upper and lower Ganga river basin for further research.