Factors driving the adoption of organic tea farming in the northern region of Bangladesh

Prodhan, Foyez Ahmed et al. Article Factors driving the adoption of organic tea farming in the northern region of Bangladesh Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Prodhan, Foyez Ahmed et al. (2023) : Factors driving the adoption of organic tea farming in the northern region of Bangladesh, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 7, pp. 1-13, https://doi.org/10.1016/j.resglo.2023.100145 This Version is available at: https://hdl.handle.net/10419/331072 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Factors driving the adoption of organic tea farming in the northern region of Bangladesh Foyez Ahmed Prodhan a , b , * , Md. Safiul Islam Afrad a , Md. Enamul Haque a , Muhammad Ziaul Hoque a , b , Mohammed Rokonuzzaman a , Hasiba Pervin Mohana c , A.K.M. Kanak Pervez d a Department of Agricultural Extension and Rural Development, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur 1706, Bangladesh b Institute of Climate Change and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur 1706, Bangladesh c China Agricultural University (CAU), Beijing, China d Department of Agronomy and Agricultural Extension, University of Rajshahi, Rajshahi 6205, Bangladesh ARTICLE INFO Keywords: Adoption Farmers’ belief Organic tea farming Attitude Theory of Planned Behavior ABSTRACT Tea is a regular export item for Bangladesh, but due to slow growth in production, the country’s tea exports have declined. However, organic tea has a great future in this regard. Exploring the underlying factors for the adoption of organic tea farming has great potential for the development of tea production in a sustainable manner. Therefore, our main objective was to identify factors influencing tea growers’ attitudes, perceptions, and adoption of organic tea farming in the Panchagarh district of Bangladesh. We adopted the Theory of Planned Behavior (TPB) to investigate the underlying factors affecting the farmers’ belief in the adoption of organic farming were measured based on control factors (marketing factors and cost and benefit factors), attitude towards organic farming, and social factors (extension factors) through a binary logistic regression model. Results of this study revealed that organic growers had a more favorable perception, which was significantly different from non-organic growers reading of various factors related to organic farming. Our results demonstrated that education and knowledge significantly influence the farmers to form a highly favorable attitude towards organic tea farming, which together explained 20.6 percent (R2 =0.206) of the overall variance. Moreover, attitudes towards organic farming and cost-benefit (CBF) factors had a significant impact on the adoption of organic tea farming. Results also revealed that the predicted adoption probability was found to be higher among those farmers who had a highly favorable attitude and a high perceived CBF level towards organic farming. Finally, based on the findings, we concluded that a participatory extension program to enhance the level of knowledge related to organic tea farming could significantly impact farmers’ attitudes and behaviors towards the practice of organic tea farming. Introduction Tea cultivation began in Bangladesh in 1854, and it has since grown into an agro-based industry that contributes to the national economy through job creation and export earnings (Ahammed, 2012). Recently, organic tea production has become very popular in Bangladesh, as it is free from the harmful effects of chemical fertilizer (Shabbir & Saיadat, 2010). However, organic farming is still in its early stages of adoption, with 0.177 million hectares of land under trial, accounting for just 2% of total cultivable land in Bangladesh (Sarker & Itohara, 2008a; Willer & Yussefi, 2005). Currently, a total area of 13,903 ha is documented for organic agriculture, accounting for approximately 0.1% of the total agricultural area (Ferdous et al., 2021). Organic tea production, that is, tea produced naturally without using chemical fertilizer, has begun in the Panchagarh district of Bangladesh. The Kazi and Kazi Tea Company has taken the lead in this respect. Since then, tea plantations have been rapidly expanded by small-holding tea growers and tea state establishments in the Panchagarh district of Bangladesh. However, Bangladesh’s tea exports to different countries have declined due to increased competition from other tea-exporting countries. Organic tea production has great potential in this regard in the future, and Bangladesh can make a dent in the world market for organic tea. Bangladesh is a country in * Corresponding author. E-mail address: [email protected] (F.A. Prodhan). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2023.100145 Received 17 January 2023; Received in revised form 9 July 2023; Accepted 9 July 2023 Research in Globalization 7 (2023) 100145 2 South Asia with a population of 169.4 million, of which 80% depend primarily on agricultural activities (World Bank, 2023). Given poverty elimination, environmental protection, and the strengthening of global cooperation, the government of Bangladesh focused more on the agriculture sector in its efforts to achieve the Millennium Development Goals (MDGs). As a result, to increase the profitability of agriculture, a special effort is needed, which requires a growth rate of 4% in the agriculture sector (International Monetary Fund, 2005). However, the excessive use of agrochemicals poses a longer-term threat to sustainable agriculture. In this regard, adoption of eco-friendly cultivation technologies has a significant effect on long-term agricultural production (Sarker et al., 2009). Organic farming (OF) is a highly regarded practice worldwide with economic and ecological benefits (Vogl et al., 2005). In many regions of Bangladesh, adoption of OF has been very slow, though the country has a great opportunity for OF owing to enormous crop diversity and considerable investment by the government and nongovernment organizations (NGOs) (Sarker & Itohara, 2008b). Hence, a significant approach for socio-economic development in developing countries is organic farming, which requires various national and international policy interventions (Kilcher & Echeverria, 2011; Twarog, 2011). However, the expansion and development of organic production are affected by diverse aspects and fluctuate to a great extent from one country to another(Brodt & Schug, 2008) (Brodt & Schug, 2008). Since organic farming has been established as a sustainable farming approach, it may be the best solution for ensuring production sustainability (Taus et al., 2013). Farmers in Bangladesh care about the environment, yet many are ignorant, lack resources, and rely heavily on commercial inputs. Some have employed this strategy on their homestead land. Due to the unavailability of organic inputs, improper pricing, and consumers’ lack of trust, organic farming is not practiced on the majority of farmland (Ferdous et al., 2021; Schoonbeek et al., 2013). As a result, regulations must be followed so that local customers may trust the goods and farmers can sell their products both locally and outside by exporting organic food items. With the findings of this study, it will be possible to determine what elements influence tea producers’ intentions in organic tea cultivation and how farmers and other stakeholders can be aware of sustainable agricultural practices without sacrificing productivity or profit. In the literature, various theories are found to evaluate farmers’ behavior towards the adoption of different technologies. For example, Protection Motivation Theory (PMT) by Rogers (1975) is used to assess the intention or behavior of an individual. Another popular theory explained by Fishbein and Ajzen (1975), named the Theory of Reasoned Actions (TRA), was used to understand the behavioral intentions of an individual towards the adoption of a technology. The TRA theory, later reconstructed as the Theory of Planned Behavior (TPB) by Ajzen (1991), is thought to be one of the most important psychological models to understand human intentions towards the use of any technology. This theory has been used by several studies (Greiner, 2015; Soorani & Ahmadvand, 2019; Tama et al., 2021; Yazdanpanah et al., 2014) in evaluating the behavioral intentions of individuals towards technology adoption. In this study, we used the TPB model to understand the behavioral factors driving the adoption of organic tea farming in northern Bangladesh. However, the existing literature related to socio-demographic and psychological factors influencing the farmers’ adoption of organic tea farming is very scarce, and no study has focused on such a topic in Bangladesh. A number of studies have been found in the literature that explore different factors stimulating farmers’ decisions to convert to organic farming. This kind of research has been done in most cases in developed countries, and the identified factors only apply to North America or European countries. Subsidies for transitioning to organic farming in EU countries and Switzerland are a key consideration for producers (Dabbert et al., 2004; Daugbjerg et al., 2011). Other major inducements in developing countries include market entry and domestic customer desire (Bellon & Lamine, 2009), as well as better returns associated with organic farming activities (Dabbert et al., 2004; De Cock, 2005). Social, health, or environmental aspects considered non-economic factors act as momentous for organic farming development, but fewer studies have been found from developed counties determining these factors as important ones (Cranfield et al., 2010). According to Thamaga-Chitja & Hendriks (2008), the issues with regard to conversion to organic agriculture in developing countries are quite different with respect to policy and demand for organic products, market access, and training facilities. Padel (2001) identified economic and health considerations as motivations for migration to organic cultivation, despite the fact that others have reported the technological expertise required for organic production as the cause of conversion (Midmore et al., 2001). The literature outlines a wide range of other considerations that have led agriculturists to organic agriculture, such as moral and religious convictions (Rigby et al., 2001), coupled with viability and consumer demand (Howlett et al., 2002), as well as food protection and quality (Fairweather, 1999). Environmental issues (Henning et al., 1991), customer intimacy, family, or health and safety concerns (Hall & Mogyorody, 2001) can also serve as motivators in Canada and the United States. Cranfield et al.(2010) concluded that four main questions about conversion to organic farming arose from the need for profits, the climate, better food nutrition/safety, and ideological/philosophical beliefs. In recent years, a few number of academic research on critical aspects of the transition to organic agriculture in developed countries have been reported in the international literature (Kisaka-Lwayo, 2007; Pastor et al., 2011). The majority of studies have focused on farmers’ personal characteristics and farm characteristics as determinants of conversion to organic farming. For example Djokoto et al. (2016), Tiffin and Balcombe (2011), Mzoughi (2011), Jayawardana and Sherief (2012), and Thapa and Rattanasuteerakul (2011) explored the socioeconomic factors of the respondents affecting the adoption of organic farming for diverse crops in different geographic locations. However, Sarker et al. (2009) and Pornpratansombat et al. (2011) have published the only studies where they look at farmers’ motives and attitudes concerning organic farming among an extended range of Bangladeshi and Thai populations. Recently, Sumi and Kabir (2018) investigated the factors affecting consumers’ buying intentions for organic tea in Bangladesh. However, the need for an indepth analysis of the factors affecting organic tea farming adoption in Bangladesh is obvious in this circumstance. Therefore, our study aims to understand the factors influencing conversion to organic production, considering not only the personal characteristics of the farmers but also the control factors (marketing factors and cost and benefit factors) and social factors (extension factors or the influence of the extension officer). The current research is concentrating on organic tea cultivation in Bangladesh’s Panchagarh district, where it offers tremendous opportunity for jobs and export revenue. The aim of this study is to broaden and deepen our understanding of the factors affecting organic tea farming adoption in order to address the following questions: What are the socioeconomic characteristics of organic tea growers? What are the important factors driving farmers to adopt organic tea farming practices? Methodology Conceptual frame work of the study According to Ajzen and Fishbein (1980), two significant factors that influence an individual’s behavior are the individual’s personality and perceived social stress. The motive of the person to conduct an action positively or negatively is the individual component. This element is connected to personal emotions and is described as the ’attitude towards behavior’ (Ajzen & Fishbein, 1980). Another aspect that influences an individual’s decision to commit or abstain from a behavior is his or her sense of social pressure. A positive attitude is developed based on an individual’s view of the result of performing a behavior, regardless of whether the bahavior is thought to be positive. Alternatively, if the bahavior is perceived to be pessimistic, a negative attitude may also arise. The assumed conduct against constructive or harmful bahavior is F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 3 referred to as a subjective norm. People can generally go ahead with performing actions because they have a favourable assessment of the behavior and believe that a large number of other people will want them to do so. Ajzen and Fishbein established the Theory of Reasoned Action (TRA) in 1980 in response to these objections. Although the TRA has been effective in imagining and recognizing bahavior that is entirely within an individual’s volitional control, it has failed to anticipate behavior that is not entirely within an individual’s volitional control. As a result, the Theory of Planned Bahavior (TPB) was established to enhance the TRA’s predictive ability for activities involving people with little volitional power. TPB now includes a third influencing element of behavioral purpose, perceived behavioral management, to account for any building or inspiring influences that may influence a strived action being carried out (Beedell & Rehman, 2000). We used TPB to develop the theoretical framework for this study, as shown in Fig. 1. First, the farmers’ behavioral beliefs about organic farming were assessed by looking at their personal characteristics, knowledge of organic farming, and environmental factors. Then, the social factors that form normative beliefs and the control factors that help to support the formation of control beliefs towards organic farming were used to evaluate the farmers’ adoption behavior. Study area The study was performed in northern Bangladesh, specifically in Tentulia upazila of Panchagarh district, the country’s most northern upazila, where organic tea is grown by the Kazi and Kazi Tea State Company as well as by small growers (Fig. 2). Tentulia covers a region of 189.12 square kilometres and is located between latitudes 26◦24 ′ and 26◦38 ′ north and longitudes 88◦21 ′ and 88◦33 ′ east. This upazila is bounded on the north, south, and west by West Bengal, India, and on the east by Panchagarh Sadar upazila. The study area is located in the Himalayan piedmont, where the weather and rainfall conditions are ideal for tea growth. Furthermore, the area’s soil is loamy and acidic with relatively well-drained conditions, making it more appropriate for tea cultivation. Tentulia has been designated as the district’s most significant economic zone in recent years owing to the development of the tea industry and the Banglabandha land port. Sample size and data collection The tea farmers living around the ‘Kazi and Kazi Tea State’ company at Rowshanpur union, Tentulia upazila in Panchagarh district of Bangladesh, were the population of the present study. There were 89 farmers who were directly involved with organic tea farming under the cooperative named Kazi Shahed Foundation of Kazi and Kazi Tea State’s company was purposely selected as the sample for the study. Another 89 small tea farmers whose tea gardens were within very close proximity of the cooperative farmers’ field but had not adopted organic farming techniques were also randomly selected as samples of the study to compare the perception of the growers towards organic farming. Therefore, the total sample size was 178. We developed an extensive interview schedule with the specific aim of gathering relevant information. The schedule’s questions and comments were straightforward, direct, and readily understood by the respondents. Both open-ended and closed-ended questions were part of the interview schedule. Appropriate scale and calculation methods were used to ensure the right reactions of the components involved. Until proceeding to final data collection from September to December 2022, it was pre-tested in the research field, and any required modifications, alterations, and changes were made in view of the tangible and realistic experiences and results of the pre-test. In accordance with the aims of this report, the data obtained from participants is coded, compiled, tabulated, and analysed. Where necessary, qualitative data is converted into quantitative type by conveying appropriate scores. Finally, a Focus Group Discussion (FGD) was held with the two selected groups (organic and non-organic farmers) of farmers to cross-check the information gathered. As a consequence, if the data enumerator had any doubts about the information supplied by the particular respondent during the discussion, he or she might review the acquired data. A descriptive study design was used in this study for factual observations that needed ample interpretation. It aids in establishing the characteristics of a specific circumstance, organization, or person. Alternatively, diagnostic or analytical designs are concerned with hypothesis testing and the specification and interpretation of relationships between variables (Ray & Mondal, 2022). Model specification for attitude towards organic farming and adoption of organic farming Farmers’ attitudes towards organic farming are influenced by a number of variables. These influential factors include the personal characteristics of the farmers, environmental factors, and the farmers’ knowledge of organic farming (Issa & Hamm, 2017). To analyze the interaction between influential factors and attitudes towards organic farming, this study used the multiple linear regression approach to determine how these contributing factors relate to the development of attitudes towards organic farming and the relationship between them. Regression analysis is one of the most significant instruments in Fig. 1. The conceptual framework of different factors persuading tea growers’ belief in the adoption of organic farming. F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 4 statistical analysis. Its purpose is to illuminate the response variables (dependent variables) using known explanatory variables (independent variables) (Hron et al., 2012). Regression analysis is used to evaluate the correlations between two or more factors that have cause-and-effect relationships and to create forecasts for the subject utilizing the relationship (Uyanık & Güler, 2013). Multiple linear regression is an excellent method for determining the association between these variables and the actual problems when there is more than one influential factor (McClave et al., 2021; Montgomery et al., 2013). It is crucial to determine the degree to which the independent variables, individually or in combination, will predict or relate to the dependent variable. The specification of the linear model that was used in this study is given below: y= α +β1x1+β2x2+⋯+βnxn+ ε ………………… (1). Where, y =dependent variable. β1 =slops or coefficient. x1 =independent variable. ε =error term. α =intercept. The empirical model to determine the attitude towards organic farming is specified as: y= α +β1AGE +β2EDU +β3FEX +β4FS +β5EF +β6KOF + ε ………………. (2). Where, AGE =Age of the respondents. EDU =Education of the respondents. FEX =Farming experience. FS =Farm size. EF =Environmental factors. KOF =Knowledge on organic farming. In this analysis, we used logistic regression to ascertain the acceptance of organic farming and the contribution of other variables to organic farming adoption. Since logistic regression is a dichotomous procedure, it is often used in adoption studies (Conteh et al., 2015). Farmers’ adoption activity was classified as ’adopters’ or ’nonadopters’, depending on a dichotomous result that identifies response variables (Y). As a result, adopter farmers are classified as Yi =1, whereas non-adopter farmers are defined as Yi =0. The Logit model is used in this case as a methodology to analyze a decision-making process in which there are two competing options (Greene, 2012). Thus, the dependent variables in the following binomial logistic model are the value of organic adoption (which is equal to one) and the probability of non-adoption (which is 0). The logistic regression model is a kind of generalized linear model that incorporates the linear regression model by comparing the set of real numbers to the range of 0–1 (Ullah et al., 2015). π i=1/1+e−zi…………………….. (3). where π i denotes the likelihood that the i th farmer would follow organic farming and zi denotes the magnitude of the i th unobserved continuous variable. Additionally, the model incorporates that z is a feature of the n-explanatory variables, which is also described as: zi= α +β1x1+β2x2+⋯+βnxn+e1……………… (4). The term zi refers to the i th meaning of the dependent variable (adoption probability), the α term is the regression constant, and ei denotes to the error term. Where x1, x2 are the independent variables (tested farmer characteristics) for zi, with the first x1 corresponding to the n th variable. The parameters (β) of the model were estimated using maximum likelihood estimation method which maximize the conditional probability given the observed data. Assuming that farmers (respondents) would or would not adopt organic farming, the following empirical model was used to determine organic farming adoption in the study region. log(z)i= α +β1MF +β2CBF +β3ATT +β4EXTF +e1……………… (5). Where, MF =Marketing factors. CBF =Cost and benefit factors. ATT =Attitude of the farmers towards organic farming. EXTF =Extension factors (Influence of the extension officer to the farmers for adopting organic tea farming). Fig. 2. Bangladesh map indicating the research region. F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 5 Description and measurement of variables used in the model The study’s dependent variable was adoption of organic farming, while the study’s independent variables were divided into three categories: farmers’ personal attributes, farmers’ attitudes toward organic farming, and perception-related variables. Factors that could impact the personal traits of farmers include age, education, experience in farming, and farm size. Alternatively, variables associated with perception included environmental factors, control factors (marketing, cost, and benefit), and social aspects (influence of the extension officer, viz., extension factors). Table 1 contains a description of the variables and the technique of measurement. All the socio-economic variables (presented in Table 2) except age, farm size, and farming experience were categorized based on the standard method, i.e., the mean ±standard deviation. While age was classified as suggested by Khalil et al. (2014), farm holding according to Sakib and Afrad (2014) and farming experience based on Prodhan and Afrad (2015) were classified. We used SPSS and R software to analysis all the data used in this study. Results and discussion The selected characteristics of the respondents Six features of the farmers have been chosen for analysis in the current research. Age, education, farm size, farming experience, knowledge of organic farming, and attitude toward organic farming were all considered. The characteristics of the respondents’ farmers and their descriptive statistics are presented in Table 2. An individual’s age is a major social component of existence. It is one of the most essential aspects of a person’s personality. The age of a person is the stage of development at which the individual is expected to make decisions and analyze events (Bayei & Nache, 2014). The respondents varied in age from 26 to 66 years of age, with an average of 46.04 years and a standard deviation of 10.93. We divided respondents into three groups, which are summarized in Table 2. The data exhibited in Table 2 indicates that the middle aged constituted the highest proportion (43.82%) of the respondents, followed by the old aged category (35.96%) and the young aged category (20.22%). The collective percentage of the middle and old aged categories was 79.78%, which constituted the huge majority of the respondents. Because of their age, young and middle-aged respondents have a wider outlook and are significantly more exposed to both social and mass media influences than elderly respondents. It helps people become more aware of and sensitive to organic farming challenges, as well as develop a positive perception of organic agriculture. However, the findings show that the majority of respondents ranged in age from middle-aged to elderly, implying a less favorable attitude toward organic agriculture. The most critical socio-demographic attribute of an individual is education, which has an effect on the individual’s views and manner of perceiving and comprehending certain social occurrences (Badiger, 2015). In the process of improving knowledge, comprehension, and character, education is also regarded as a need for the mind. This is an imperative concern for the adoption of better agriculture technology skills, methods, and procedures (Zahid et al., 2013). The average education score of the respondents under the study ranged from 0 to 14, with an average education score of 6.52 and a standard deviation of 3.71 (Table 2). The results presented in Table 2 show that more than two-fifths (43.26%) of the respondents had no education, while 41.57% belonged to the primary level, followed by 10.11% at the secondary level, and only 5.06% above the secondary level. A considerable number of respondents (51.68%) fell into the primary to secondary education category, while only a small number passed above secondary education. From the above findings, it can be said that respondents were progressive, but they are still far from higher education. Whereas, the average education of the organic farming adopter farmer was higher than that of the non-adopter farmer, and the difference between them was significant. That means adopter farmers were more aware of organic farming, which subsequently helped them form a positive attitude towards organic farming. According to the farm holding category, the respondents were classified into three categories. It was found that the utmost quantity of respondents belonged to the medium farm holding category (73.03%), followed by the large (21.35%) and small (5.62%) farm size categories. (Table 2). However, the overall average farm size of the farmers was 2.28 ha, which was higher than the national average of 0.52 ha (BBS, 2019). That means farmers in the study area possess a higher average farm size than the national average farm size. Conversely, the organic farming adopter farmers had a bit higher average farm size compared to non-adopter farmers, but the difference was non-significant. Larger farms have a better chance of transforming a portion of their land to organic farming (Oluwasusi, 2014). This might be due to the fact that large farm-size farmers had the option to trial organic practice on small parts of their farms before they eventually adopted organic practice on the entire farm as a sustainable decision. Similarly, it can be assumed that large farmers might not have as much financial stress to look for substitute means to increase their income by replacing traditional farming technology with organic farming technology (Genius et al., 2006). This can allow a farmer to escalate the benefits and shortcomings associated with this activity. Agriculture is a complex undertaking, and some information can only be gained through years of experience. Respondents’ farming experience ratings varied from 3 to 29, with an average of 15.31. (Table 2). On the basis of their experience, respondents were divided into three groups. According to the findings in Table 1, the proportion of those who fell into the medium farming experience category was 39.89%, while the proportion of those who fell into the low farming experience category was 34.27%, and only 25.84% of them fell into the high farming experience category. The cumulative Table 1 Variables used in the multiple linear regression and binary logistic regression models. Variables Type Measurement Dependent variable (y) Adoption Categorical variable 1 for adoption of OF, 0 for nonadoption Explanatory variables Age of the respondents Continuous Actual year of the respondents Education of the respondents Continuous Year of schooling Farming experience Continuous No years of the farmers involved in farming Farm size Continuous Amount total land (hectare) Knowledge on organic farming Score Ten questions were selected to measure farmers’ knowledge on organic farming. Each question was allocated 2 marks. For a correct answer a respondent was given full marks and for partial answer half mark (i.e. 1). In case of incorrect answers a sore of ‘0 ′ was assigned. Attitude towards organic farming Score A five point Likert scale such as “strongly agree”, “agree”, “undecided”, “disagree” and “strongly disagree” was used. Assigned scores against each response were 5, 4, 3, 2, and 1 respectively. Perception related variables Environmental factors Score Perception related variables were assessed based on a five point Likert scale such as “strongly agree”, “agree”, “undecided”, “disagree” and “strongly disagree”. Assigned scores against each response were 5, 4,3, 2, and 1 respectively. Control factors (marketing factors, cost and benefit factors) Social/Extension factors (influence of the extension officer viz. extension factors) F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 6 percentage of respondents in the low and medium farming experience categories was 74.16 percent, which constituted the huge majority of respondents. We also observed that adopter farmers had less farming experience than non-adopter farmers, and the difference was significant. It can be said that farmers with less farming experience are more likely to form a positive attitude towards the adoption of organic farming, as those who have been farming for a very long time are usually old and resistant to change (Adesope et al., 2012). It is one of the most important human behaviors that increases his or her consciousness and makes him or her conversant with facts that ultimately affect the covert and overt behaviors of human beings. The knowledge scores on organic farming of the respondents ranged from 9 to 20, with an average of 15.67 and a standard deviation of 3.05 (Table 2). Results in Table 2 indicate that the largest percentage (61.80%) of respondents were in the medium knowledge class, compared to 21.35% in the highest and 16.85% in the lowest knowledge category. However, it was shown that the vast majority of respondents in the research region (83.17 percent) had a medium-to-high understanding of organic farming. It was also found that there was a significant difference between adopter and non-adopter knowledge of organic farming. That indicates organic farmers are more likely to show a favorable attitude towards organic farming. This could be due to the fact that farmers’ knowledge gained over time in an agricultural farming system may aid in assessing farm practices and influence their adoption decisions (Sall et al., 2000). Attitude is a very influential aspect of moving towards sustainable agriculture for the farming community. The computed attitude scores of the respondents ranged from 17 to 48, the mean being 34.31 with a standard deviation of 8.62 (Table 2). The data revealed that over half (53.37%) of respondents were in favor of organic farming and 26.97% were very positive, while just about two-fifths (19.66%) of respondents had unfavorable attitudes towards organic farming. However, an overwhelming majority of survey respondents (80.34 percent) were positive about unfavorable behavior in organic farming. We observed a substantial difference in attitudes towards adopters and non-adopters, with adopters having a more positive view. This is because the adopters had a higher educational level, which increased their knowledge and beliefs, thus determining their positive attitude. The perceptions of the growers towards organic farming on different factors Perception empowers an individual to recognize his attitudes concerning the objects and circumstances in his environment and to act accordingly. Farmers’ perceptions regarding organic tea growing were examined across four aspects in this study. These include environmental concerns, marketing aspects, cost and benefit considerations, and social/ extension factors. Environmental factor The organic farming system is based on the uniformity of landscape production and non-production functions, where the emphasis is placed on environmental aspects (Birkhofer et al., 2016). Environmental perception is a very important factor in the motivation for the adoption of organic tea farming. Concerning all environmental claims, there was a substantial difference in perception between organic tea farmers and non-organic tea growers (Table 3). Organic growers have a more favorable perception than non-organic growers because most organic growers have a better understanding of the real effects and long-term impact of chemical fertilizers. Alternatively, non-organic farmers were unaware of natural resource conservation since most of them concentrated more on high yields and increasing short-term production. This may mean that the two groups of farmers have divergent perceptions and points of view on various environmental factors, implying that they also have divergent levels of environmental consciousness and care for organic farming. As a result, there was a substantial difference in environmental awareness between organic and non-organic tea farmers (Table 3). Information presented in Table 4 revealed that education and knowledge of organic farming were significantly correlated with environmental factors, i.e., farmers who had more education and knowledge of organic farming were more concerned about environmental conservation. Marketing factor Agricultural marketing covers the wide range of activities encompassed in moving agricultural products from the farmhouse to the customer. When it comes to organic farming, it is critical for farmers to be aware of not just the nature of organic agriculture but also the market for organic goods (Khaledi et al., 2010). The marketing factor is another important motivational factor for the adoption of organic farming. In all statements except one, organic tea farmers had a more positive image than non-organic tea growers in terms of marketing considerations, i.e., consumers tend to buy more organic agricultural products than products grown using chemicals because both organic and non-organic growers realize that customers are very concerned about their health and the nutritional value of the product. That’s why there was no significant difference (Table 3). The overall perception of marketing factors was Table 2 Salient features of the selected characteristics of the respondents. Characteristics (unit) Category No. of respondents Percent Mean SD tstatistics All farmers Adopters Nonadopters Age (yrs) Young aged (up to35) 36 20.22 46.04 44.18 47.91 10.39 −1.802 ns Middle aged (36–50) 78 43.82 Old aged (>51) 64 35.96 Education (yrs) No education (0) 77 43.26 6.32 7.66 5.3864 3.71 3.343 ** Primary (1–5) 74 41.57 Secondary (6–10) 18 10.11 Above secondary (11–12) 9 5.06 Farm size (ha) Small (up to 1 ha) 10 5.62 2.28 2.44 2.10 0.84 1.952 ns Medium (1.01–3 ha) 130 73.03 Large (>3ha) 38 21.35 Farming experience (yrs) Low (up to 10) 61 34.27 15.31 13.3636 17.2727 7.05 −2.997 ** Medium (11–20) 71 39.89 High (>20) 46 25.84 Knowledge on organic farming (score) Low (up to 12) 30 16.85 15.67 18.14 13.20 3.05 13.767 ** Medium (13–18) 110 61.80 High (>18) 38 21.35 Attitude towards organic farming (score) Un-favorable (up to 26) 35 19.66 35.31 42.04 28.57 9.19 12.163 ** Favorable (27–42) 95 53.37 Highly favorable (>43) 48 26.97 F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 7 higher in the case of organic growers than in non-organic growers (Table 3), because the majority of non-organic tea farmers believed that there was no difference in farm-gate prices between organic and conventional tea. Further, non-organic tea growers believed that there were no adequate buyers for organic tea. On the contrary, the organic growers had a favorable attitude towards marketing because the Kazi and Kazi tea states purchase organic tea directly from the organic tea growers. Table 4 shows that older farmers with greater agricultural experience and an understanding of organic agriculture had a more positive attitude towards marketing. Cost and benefit factor Economics provides a rational basis for making decisions to use a technology among various options (Caswell et al., 2001). At the moment, most organic farmers are motivated by economic rather than non-economic considerations (Flaten et al., 2005; Padel, 2001) whereas cost and benefit are important factors in organic farming conversion in Bangladesh (Sarker & (Sarker & Itohara, 2008b). Regarding the farmers’ perceptions on cost and benefit factors, five statements were analyzed, and only two statements were significant between organic and nonorganic tea growers (Table 3). There was no significant change in cost-benefit perception for the other three statements, i.e., ‘total OF cost is higher than chemical farming cost’, ‘cost of production can be reduced because family labor can be utilized in OF’ and ‘cost of labor in OF is less than chemical farming.“ This is because both organic growers and nonorganic growers thought that organic farming involved only organic inputs and processing of organic inputs like organic fertilizer, organic pesticide, etc., and for intercultural operations required more labor. In addition, due to the decreasing cattle population, all organic inputs are not sufficiently available. That’s why they have to buy organic inputs at a higher cost. Because of this, the ultimate cost of production in organic farming gets higher. But the overall mean average of organic tea growers is higher than that of non-organic tea growers (Table 3), as organic tea growers are aware that the demand for organic tea is increasing day by day and that organic tea fetches higher prices in the market. In addition, organic tea remains sold by targeting both the top and international classes, ultimately contributing to more profit. Conversely, non-organic tea growers assumed that organic products were only for upper-class people due to their higher price. In addition, a lack of proper marketing channels means they can’t sell it in urban areas, and local people are not very interested in buying organic products at the maximum price from the local market. As a result, the organic product remains unsold, resulting in a lower profit. For these reasons, non-organic growers tend to have a lower perception of the cost and benefit aspect. According to the data in Table 4, the farming experience and knowledge of organic farmers have a positive, significant connection with their cost and benefit perspective. This implies that more farming experience and knowledge of organic farming among the growers leads to a tendency towards a more favorable perception of the cost and benefit factor. Social/Extension factors In this study, we considered five statements to analyze the farmers’ perceptions related to the extension factors (Table 3). The results presented in Table 3 reveal that only one statement, i.e., “training progressive farmers and early adopter farmers to accept and develop organic farming,” was insignificant among organic and non-organic tea farmers. This might be due to the fact that both organic and non-organic tea farmers perceived that the training program could help them learn more organic farming practices, which motivated them to learn organic farming. However, in the case of the other four statements, the organic tea farmers had a more favorable perception in terms of extension factors than the non-organic farmers. Organic tea farmers had a higher perception of extension factors than non-organic tea farmers (Table 3). A significant difference in the perception of extension factors was observed. This is because organic farmers feel that the extension worker’s influence will increase the adoption of organic farming by Table 3 Perception of the respondents towards organic tea farming. Statements Mean score tvalue Sig. OF* NOF* Environmental factor 1. OF enhances soil fertility 4.52 3.00 8.21 0.020 2. OF will conserve water resources and other organism compared to ordinary farming 4.25 2.11 9.42 0.000 3. Organic fertilizer used in farm does not affect one’s health 3.45 2.61 3.69 0.001 4. OF will not contaminate the environment or deplete natural resources 3.38 2.93 2.14 0.038 5. Non-organic farming uses inorganic fertilizer, pesticides, and other chemicals that have long-term negative impacts on the ecosystem 3.68 2.34 5.19 0.000 Marketing factor 1. Consumers tend to buy more organic agricultural products than products farm using chemical 3.61 3.15 1.87 0.081 ns 2. Consumers can buy organic agricultural products readily from the farm 4.11 2.59 7.10 0.000 3. Consumers from both inside and outside their communities like to buy organic products from you 3.5 2.7 3.09 0.003 4. Organic goods are in great demand 3.47 2.00 5.53 0.000 5. There are adequate buyers for organic tea 4.06 2.45 6.50 0.000 Cost and benefit factor 1. Total OF cost is higher than chemical farming cost 3.52 3.20 1.22 0.227 ns 2. OF can give more profit than products from chemical farming 3.95 2. 79 5.14 0.000 3. The cost of production may be decreased since family labor can be used in OF 2.97 275 0.90 0.371 ns 4. The cost of production may be lowered in OF due to the utilization of agricultural residuals as fertilizer 3.88 2.75 4.19 0.000 5. Cost of labor in OF is less rather than chemical farming 3.20 2.97 0.990 0.328 ns Extension factor 1. Training progressive farmers and early adopter farmers to accept and develop the organic farming 3.61 3.33 1.560 0.122 ns 2. Notification and dissemination of organic farming information 3.31 2.95 2.127 0.036 3. Informing farmers and the general public about the necessity of eating nutritious, chemical-free foods 3.58 2.67 5.253 0.000 4. Informing farmers and the general public on the drawbacks of employing pesticides and chemical fertilizers in agricultural crop production 3.68 2.40 8.285 0.000 5. Holding workshops for farmers on the benefits of consuming organic products 3.35 2.16 7.831 0.000 OF =Organic farming; NOF =Non-organic farming. Table 4 Comparing views of organic and non-organic farmers on a broad scale. Sl. No Factors Mean score t-value Sig. OF NOF Environmental factor 19.21 13.00 12.18 0.000 Marketing factor 18.81 12.97 9.46 0.000 Benefit and cost factor 17.61 14.47 6.40 0.000 Extension factor 17.53 13.51 9.538 0.000 F.A. Prodhan et al. Research in Globalization 7 (2023) 100145 8 providing them with additional organic farming information to help them improve their farms. Moreover, the education of the farmers had a positive, significant correlation with extension factors, which implies that farmers with more education were more concerned about extension programs to enhance the adoption of organic farming (Table 4). Factors influencing the growers’ confidence for the formation of attitude toward organic farming The individual’s attitude towards organic farming is defined by his or her evaluation of its conduct. Regression analyses were used to examine the influence of factors on attitude development. Table 5 summarizes the findings. The regression coefficient of only two factors, namely education and knowledge, made a major contribution to organic farming out of six variables (Table 5). The remaining five factors had no meaningful impact on the outcome. The R 2 value is 0.248, and the F value is 6.51, both of which are significant at the 0.000 level. The R 2 result indicates that the two variables included in the regression analysis explained 24.8 percent of the overall variance in respondents’ attitudes about organic farming. However, due to the internal relations between the variables, the correct contribution of the elements could not be represented. As a result, it was decided to conduct a stepwise multiple regression analysis, the results of which are shown in Table 6. The regression model, which together represents 20.6 percent of the overall change in attitude towards organic farming, was only included in two variables out of 6, namely education and knowledge. The F value was 5.32, which is statistically significant at the 0.000 level. Given the substantial contributions of the two variables stated above to the variance in farmers’ attitudes about organic farming, the researchers rejected the null hypotheses and concluded that each of the two factors had a significant impact on the respondents’ “attitude”. Additionally, the unique contribution of each of the two variables was identified by examining the changes in the R 2 value that happened when a specific variable was included in the step-wise regression model. Table 7 displays the findings. The two factors may explain 20.6 percent of the overall variance in respondents’ attitudes, leaving the remaining 80.4 percent unexplained. Knowledge alone accounted for 11.1 percent of the variance in attitude towards organic farming, whereas education accounted for 9.5% of the variation. Factors boosting tea growers’ belief for adoption of organic farming To identify the factors triggering the adoption of organic tea farming, we employed a binary logistic regression model. This study identified some of the factors that have a significant influence on farmers’ adoption of organic tea farming. Table 8 shows the results of the binary logistic regression that identified two important factors, i.e., attitude and CBF, that significantly contributed to organic tea farming adoption. The results also showed that EXT and MF had no statistically significant contribution to organic farming adoption by the farmers. However, attitude and CBF were significantly positively related to farmers’ beliefs in organic farming adoption at 0.5% and 0.01% significant levels, respectively (Table 9). Our model also predicted adoption probability at different attitudes and CBF levels (Fig. 3). The adoption probability of the farmers shows a discrepancy with different attitudes and CBF levels. Results suggested that predicted adoption probability was found to be higher with those farmers having a highly favorable attitude and a high perceived CBF level towards organic farming. Table 5 Relationship of the growers’ personal characteristics and their perception. Variables Age Education Farm size Farming Experience Knowledge on organic farming Environmental factor 0.185 ns 0.256* 0.149 ns 0.168 ns 0.671 ** Marketing factor 0.236* 0.110 ns −0.049 ns 0.341 ** 0.584 ** Cost and benefit factor 0.203 ns 0.092 ns −0.009 ns 0.304 ** 0.377 ** Extension factor 0.266 0.345* 0.030 0.015 0.129 ‘ns’ represents non-significant; ‘*’ and ‘**’ indicate correlation is significant at 0.05 and 0.01 level respectively. Table 6 Regression coefficients between respondents’ attitudes about organic farming and their chosen attributes in a general linear model method. Selected characteristics of the respondents Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 32.635 8.420 3.876 0.000 Age 0.133 0.127 0.424 1.052 0.300 Education 0.397 0.152 0.431 2.610 0.013 Farming experience -0.278 0.215 -0.512 −1.289 0.205 Farm size 0.047 0.540 0.013 0.087 0.931 Knowledge 0.689 0.331 0.309 2.080 0.044 Environment -0.120 0.239 -0.079 -0.500 0.620 R 2 0.248 Adjusted R 2 0.126 F 6.51 Table 7 Regression coefficients of respondents’ attitudes toward organic farming with their chosen attributes. Selected characteristics of the respondents Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 44.400 1.144 38.820 0.000 Knowledge 0.701 0.316 0.314 2.218 0.032 Education 0.307 0.134 0.333 2.291 0.027 R 2 0.206 Adjusted R 2 0.168 F 5.328 Table 8 Changes in multiple R 2 for enter of a variable into the step-wise multiple regressions for respondents’ attitude towards organic farming. Model Independent variables R 2 value R 2 change Variance Explaining (percent) 1 Knowledge 0.111 0.111 11.1 2 Education 0.206 0.095 9.5 Table 9 Results of binary logit model. Variables Coefficients Std. Error z value Pr(>|z|) Intercept 27.131 6.655 −4.077 4.57e-05 ** Attitude 0.222 0.094 2.344 0.019* MF 0.281 0.191 1.113 0.265 EXF 0.281 0.170 1.653 0.098 CBF 0.634 0.205 3.084 0.002** Number of observations 178; Pseudo R 2 : Cox and Snell 0.672; Nagelkerke 0.896; Log likelihood: 48.295. Signif. codes: 0 0.01 ‘**’; 0.05 ‘*’. F.A. Prodhan et al.