Factors Driving the Adoption of Organic Tea Farming Practices ...

Factors Driving the Adoption of Organic TeaFarming Practices by the Farmers in PanchagarhDistrict of BangladeshFoyez Ahmed Prodhan  ( [email protected] )

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityMd. Sa�ul Islam Afrad 

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityMd. Enamul Haque 

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityMuhammad Ziaul Hoque 

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityMohammed Rokonuzzaman 

Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityHasiba Pervin Mohana 

China Agricultural UniversityA.K.M. Kanak Pervez 

University of Rajshahi

Research Article

Keywords: Farmers’ belief, organic tea farming, adoption, Bangladesh

Posted Date: April 18th, 2022

DOI: https://doi.org/10.21203/rs.3.rs-1555521/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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Factors Driving the Adoption of Organic Tea Farming Practices by the Farmers in Panchagarh District of 1

Bangladesh 2

Abstract 3

Tea is a regular export item for Bangladesh, but due to slow growth in production, the country's tea exports have 4

declined. However, organic tea has a great future in this regard. Understanding the status and underlying factors of 5

organic tea farming adoption has great potential to further enhance organic tea production and contribute to 6

sustainable development. Therefore, our main objective was to identify factors influencing tea growers’ attitude, 7

perception and adoption of organic tea farming in the Panchagarh district of Bangladesh. Factors affecting the 8

farmers' belief in the adoption of organic farming were measured based on control factors (marketing factors and 9

cost and benefit factors), attitude towards organic farming and social factors (extension factors) through a binary 10

logistic regression model. This study showed that attitudes towards organic farming and perceptions on different 11

dimensions are the major factors that bring about the adoption of organic farming. We also observed that organic 12

growers had more favorable perception and it was significantly difference than non-organic growers reading various 13

factors on organic farming. Our results demonstrated Education and knowledge greatly influence the farmers to 14

form a highly favorable attitude towards organic tea farming. Moreover, attitudes towards organic farming and cost 15

and benefit factors were indicated as the significant contributors to the adoption of organic tea farming practices. 16

Finally, we suggested a participatory extension program by the Bangladesh Tea Board to change the attitudes and 17

knowledge of the growers towards organic tea farming. 18

Keywords Farmers’ belief, organic tea farming, adoption, Bangladesh. 19

1. Introduction 20

Tea cultivation began in Bangladesh in 1854, and it has since grown into an agro-based industry that 21

contributes to the national economy through job creation and export earnings (Ahammed, 2012). Recently, organic 22

tea production has become very popular in Bangladesh, as it is free from the harmful effects of chemical fertilizer 23

(Shabbir and Saיadat 2010). However, organic farming is still in its early stages of adoption, with 0.177 million 24

hectares of land under trial, accounting for just 2% of total cultivable land in Bangladesh (Willer and Yussefi 2005; 25

Sarker and Itohara, 2008). Currently, a total area of 13,903 ha is documented for organic agriculture, accounting for 26

approximately 0.1% of the total agricultural area (Ferdous et al. 2021). Organic tea production, that is, tea produced 27

naturally without using chemical fertilizer, has begun in the Panchagarh district of Bangladesh. The Kazi and Kazi 28

Tea Company has taken the lead in this respect. Bangladesh is a country in South Asia with a population of 159.1 29

million, of which 80% of the population depends primarily on agricultural activities (World Bank, 2014). Given 30

poverty elimination, environmental protection, and the strengthening of global cooperation, the government of 31

Bangladesh focused more on the agriculture sector in its efforts to achieve the Millennium Development Goals 32

(MDGs). As a result, to increase the profitability of agriculture, a special effort is needed, which requires a growth 33

rate of 4 % in agriculture sectors, thus helping in poverty reduction (Poverty Reduction Strategy Paper (PRSP), 34

2005). However, the excessive usage of agrochemicals poses a longer-term threat to sustainable agriculture. 35

2

Adoption of eco-friendly cultivation technologies has a significant effect on long-term agricultural production in this 36

regard (Sarker et. al., 2009). Organic farming (OF) is a highly regarded practice worldwide with economic and 37

ecological benefits (Vogl et al., 2005). In many regions of Bangladesh, adoption of OF has been very slow, though 38

the country has a great opportunity for OF owing to enormous crop diversity and considerable investment by the 39

government and non-government organizations (NGOs) (Sarker and Itohara, 2008). Hence, a significant approach 40

for socio-economic development by the developing countries is organic farming, which required various national 41

and international policy interventions (Twarog, 2010; Kilcher & Echeverria, 2010). However, the expansion and the 42

development of organic production are affected by diverse aspects and fluctuate to a great extent from one country 43

to another (Brodt & Schug, 2008). 44

A number of studies have been found in the literature that explore different factors stimulating farmers’ 45

decisions for organic farming conversion. This kind of research has been done in most cases in developed countries, 46

and the identified factors do only apply to North America or European countries. Subsidies for transitioning to 47

organic farming in EU countries and Switzerland are a key consideration for producers (Dabbert et al., 2004; 48

Daugbjerg et al., 2011). Other major inducements in developing countries include market entry and domestic 49

customer desire (Lamine & Bellon, 2009) as well as better returns associated with organic farming activities 50

(Dabbert et al., 2004; De Cock, 2005). Social, health or environmental aspects considered as non-economic factors 51

act as a momentous for organic farming development but less studies have been found from developed counties in 52

determining these factors as important ones (Cranfield et al., 2010). According to Thamaga-Chitja & Hendriks 53

(2008), the issues with regard to conversion to organic agriculture in developing countries are quite different, with 54

respect to policy and demand for organic products, market access, and training facilities. Padel (2001) identified 55

economic and health considerations as motivations for migration to organic cultivation, despite the fact that others 56

have reported the technological expertise required for organic production as the cause of conversion (Midmore et al. 57

2001). The literature outlines a wide range of other considerations that have led agriculturists to organic agriculture, 58

such as moral and religious convictions (Rigby et al., 2001) coupled with viability and consumer demand (Howlett 59

et al. 2002), as well as food protection and quality (Fairweather, 1999). Environmental issues (Henning et al. 1991), 60

customer intimacy, family, or health and safety concerns (Hall and Mogyorody, 2001) can also serve as motivators 61

in Canada and the United States. Cranfield et al. (2010) concluded that four main questions about conversion to 62

organic farming arose from the need for profits, the climate, better food nutrition/safety/higher quality, and 63

3

ideological/philosophical beliefs. In recent years, a few number of academic research on critical aspects of the 64

transition to organic agriculture in developed countries has been reported in the international literature (e.g. Kisaka-65

Lwayo, 2008; Pastor et al., 2011; Sarker et al., 2010). The majority of studies have focused on farmers' personal 66

characteristics and farm characteristics as determinants of conversion to organic farming. For example, Djokoto et 67

al. (2016), Tiffin and Balcombe (2011), Mzoughi (2011), Jayawardana and Sherief (2012), Thapa and 68

Rattanasuteerakul (2011) explored socioeconomic factors of the respondents affecting the adoption of organic 69

farming for diverse crops in different geographic locations. However, Sarker et al. (2010) and Pornputrasombat et al. 70

(2011) have published the only studies where they look at farmers' motives and attitudes concerning organic farming 71

among an extended range of Bangladeshi and Thai populations. Recently, Sumi and Kabir (2018) investigated the 72

factors affecting consumers' buying intentions for organic tea in Bangladesh. However, the need for an in-depth 73

analysis of the factors affecting organic tea farming adoption in Bangladesh is obvious in this circumstance. 74

Therefore, our study aims to understand the factors influencing conversion to organic production, 75

considering not only the personal characteristics of the farmers but also the control factors (marketing factors and 76

cost and benefit factors) and social factors (extension factors or influence of the extension officer). The current 77

research is concentrating on organic tea cultivation in Bangladesh's Panchagarh district, where it offers tremendous 78

opportunity for jobs and export revenue. The aim of this study is to broaden and deepen our understanding of the 79

factors affecting organic tea farming adoption in order to address the following questions: What are the socio-80

economic characteristics of organic tea growers? What are the important factors driving farmers to adopt organic tea 81

farming practices? 82

2. Methodology 83

2.1.Conceptual frame work of the study 84

According to Ajzen and Fishbein (1980), two significant factors that influence an individual's behavior are 85

the individual's personality and perceived social stress. The motive of the person to conduct an action positively or 86

negatively is the individual component. This element is connected to personal emotions and is described as the 87

'attitude toward behavior' (Ajzen and Fishbein 1980). Another aspect that influences an individual's decision to 88

commit or abstain from a behavior is his or her sense of social pressure. A positive attitude is developed based on an 89

individual's view of the result of performing a behavior, regardless of whether the behavior is thought to be positive. 90

4

Alternatively, if the behavior is perceived to be pessimistic, a negative attitude may also arise. The assumed conduct 91

against constructive or harmful behavior is referred to as a subjective norm. People can generally get ahead with 92

doing actions because they have a favorable assessment of the behavior and believe that a large number of other 93

people will want them to do so. Ajzen and Fishbein established the Theory of Reasoned Action (TRA) in 1980 in 94

response to these objections. Although the TRA has been effective in imagining and recognizing behaviour that is 95

entirely within an individual's volitional control, it has failed to anticipate behavior that is not entirely within an 96

individual's volitional control. As a result, the Theory of Planned Behaviour (TPB) was established to enhance the 97

TRA's predictive ability for activities involving people with little volitional power. TPB now includes a third 98

influencing element of behavioral purpose, perceived behavioral management, to account for any building or 99

inspiring influences that may influence an endeavored action being carried out (Beedell and Rehman, 2000). We 100

used TPB to develop the theoretical framework for this study, as shown in Figure 1. First, the farmers' behavioral 101

beliefs about organic farming were assessed by looking into their personal characteristics, knowledge of organic 102

farming, and environmental factors. Then, the social factors that form the normative beliefs and the control factors 103

that help to support the formation of control beliefs toward organic farming were used to evaluate the adoption 104

behaviour of the farmers' toward organic tea farming. 105

106

Figure 1.The conceptual framework of different factors persuading tea growers’ belief in the adoption of organic 107

farming 108

2.2.Study area 109

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The study was performed in northern Bangladesh, specifically in Tentulia upazila of Panchagarh district, 110

the country's most northern upazila, where organic tea is grown by the Kazi and Kazi tea state company as well as 111

by small growers. Tentulia covers a region of 189.12 square kilometers and is located between latitudes 26°24' and 112

26°38' north and longitudes 88°21' and 88°33' east. This upazila is bounded on the north, south, and west by West 113

Bengal, India, and on the east by Panchagarh Sadar upazila (Banglapedia, 2014). The study area is located in the 114

Himalayan piedmont, where the weather and rainfall conditions are ideal for tea growth. Furthermore, the area's soil 115

is loamy and acidic with relatively well-drained conditions, making it more appropriate for tea cultivation. Tentulia 116

has been designated as the district's most significant economic zone in recent years owing to the development of the 117

tea industry and the Banglabandha landport. 118

119

Figure 1. Bangladesh map indicating the research region 120

121

2.3. Sample size and data collection 122

The tea famers living around the ‘Kazi and Kazi Tea State’ company at Rowshanpur union, Tentulia 123

upazila in Panchagarh district of Bangladesh were the population of the present study. There were 89 farmers 124

who were directly involved with organic tea farming under the cooperative named Kazi Shahed Foundation of 125

Kazi and Kazi Tea State's company were purposely selected as the sample of the study. Another 89 small tea 126

farmers whose tea gardens were within very close proximity of the cooperative farmers’ field but had not 127

adopted organic farming techniques were also randomly selected as samples of the study to compare the 128

6

perception of the growers towards organic farming. Therefore, the total sample size was 178. We developed an 129

extensive interview schedule with the specific aim of gathering relevant information. The schedule's questions 130

and comments were straightforward, direct, and readily understood by the respondents. Both open and closed-131

style questions were part of the interview schedule. Appropriate scale and calculation methods were used to 132

ensure the right reactions of the components involved. Until proceeding to final data collection, it was pre-tested 133

in the research field and any required modifications, alterations, and changes were made in view of the tangible 134

and realistic experiences and results of the pre-test. In accordance with the aims of this report, the data obtained 135

from participants is coded, compiled, tabulated, and analysed. Where necessary, qualitative data is converted 136

into quantitative type by conveying appropriate scores. Finally, a Focus Group Discussion (FGD) was held with 137

the selected farmers to cross-check the information gathered. As a consequence, if the data enumerator had any 138

doubts about the information supplied by the particular respondent during the discussion, he or she might 139

review the acquired data. A descriptive study design was used in this study for factual observations that needed 140

ample interpretation. It aids in establishing the characteristics of a specific circumstance, organization, or 141

person. Alternatively, diagnostic or analytical designs are concerned with hypothesis testing and the 142

specification and interpretation of relationships between variables (Ray and Mondal, 1999). 143

2.4. Model specification for attitude towards organic farming and adoption of organic farming 144

Farmers' attitudes towards organic farming are influenced by a number of variables. These influential factors include 145

personal characteristics of the farmers, environmental factors, and the farmers’ knowledge of organic farming (Issa 146

and Hamm, 2017). To analyze the interaction between influential factors and attitudes toward organic farming, this 147

study used the multiple linear regression approach to determine how these contributing factors relate to the 148

development of attitudes toward organic farming and the relationship between them. Regression analysis is one of 149

the most significant instruments in statistical analysis. Its purpose is to illuminate the response variables (dependent 150

variables) using known explanatory variables (independent variables) (Hron et al., 2012). Regression analysis is 151

used to evaluate the correlations between two or more factors that have cause and effect relationships and to create 152

forecasts for the subject utilizing the relationship (Uyanık et al, 2013). Multiple linear regression is an excellent 153

method for determining the association between these variables and the actual problems when there is more than one 154

influential factor (Draper and Smith, 1998; Tamhane and Dunlop, 2000; Shakil 2008; Montgomery et al., 2013; 155

McClave and Sincich, 2014). It is crucial to determine the degree to which the independent variables, individually 156

7

or in combination, will predict or relate to the dependent variable. The specification of the linear model that was 157

used in this study is given below:𝑦𝑦 = 𝛼𝛼 + 𝛽𝛽1𝑥𝑥1 + 𝛽𝛽2𝑥𝑥2 +⋯+ 𝛽𝛽𝑛𝑛𝑥𝑥𝑛𝑛 + 𝜀𝜀 ………………… (1) 158

Where, 159

y = dependent variable 160 𝛽𝛽1= slops or coefficient 161 𝑥𝑥1= independent variable 162 𝜀𝜀= error term 163 𝛼𝛼= intercept 164

The empirical model to determine the attitude towards organic farming specified as 165

𝑦𝑦 = 𝛼𝛼 + 𝛽𝛽1𝐴𝐴𝐴𝐴𝐴𝐴 + 𝛽𝛽2𝐴𝐴𝐸𝐸𝐸𝐸 + 𝛽𝛽3𝐹𝐹𝐴𝐴𝐹𝐹 + 𝛽𝛽4𝐹𝐹𝐹𝐹 + 𝛽𝛽5𝐴𝐴𝐹𝐹 + 𝛽𝛽6𝐾𝐾𝐾𝐾𝐹𝐹 + 𝜀𝜀 ………………. (2) 166

Where, 167 𝐴𝐴𝐴𝐴𝐴𝐴= Age of the respondents 168 𝐴𝐴𝐸𝐸𝐸𝐸= Education of the respondents 169 𝐹𝐹𝐴𝐴𝐹𝐹= Farming experience 170 𝐹𝐹𝐹𝐹=Farm size 171 𝐴𝐴𝐹𝐹= Environmental factors 172

KOF= Knowledge on organic farming 173

In this analysis, we used logistic regression to ascertain the acceptance of organic farming and the contribution of 174

other variables to organic farming adoption. Since logistic regression is a dichotomous procedure, it is often used in 175

adoption studies (Contech at al., 2015). Farmers' adoption activity was classified as 'adopters' or 'non-adopters' 176

depending on a dichotomous result that identifies response variables (Y). As a result, adopter farmers are classified 177

as Yi=1, whereas non-adopter farmers are defined as Yi=0. The Logit model is used in this case as a methodology to 178

analyze a decision-making process in which there are two competing options (Greene, 2003). Thus, the dependent 179

variables in the following binomial logistic model are the value of organic adoption (which is equal to one) and the 180

probability of non-adoption (which is 0). The logistic regression model is a kind of generalized linear model that 181

incorporates the linear regression model by comparing the set of real numbers to the range of 0 – 1 (Ullah et al., 182

2015). 183

8

𝜋𝜋𝑖𝑖 = 11 + 𝑒𝑒−𝑧𝑧𝑖𝑖 � …………………….. (3) 184

where 𝜋𝜋𝑖𝑖 denotes the likelihood that the ith farmer would follow organic farming and 𝑧𝑧𝑖𝑖 denotes the magnitude of 185

the ith unobserved continuous variable. Additionally, the model incorporates that z is a feature of the n-explanatory 186

variables, which is also described as: 187

𝑧𝑧𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝑥𝑥1 + 𝛽𝛽2𝑥𝑥2 +⋯+ 𝛽𝛽𝑛𝑛𝑥𝑥𝑛𝑛 + 𝑒𝑒1……………… (4) 188

The term 𝑧𝑧𝑖𝑖 refers to the ith meaning of the dependent variable (adoption probability), the 𝛼𝛼 term is the regression 189

constant, and 𝑒𝑒𝑖𝑖 denotes to the error term. Where 𝑥𝑥1, 𝑥𝑥2 are the independent variables (tested farmer characteristics) 190

for 𝑧𝑧𝑖𝑖, with the first 𝑥𝑥1 corresponding to the nth variable. Assuming that farmers (respondents) would or would not 191

adopt organic farming, the following empirical model was used to determine organic farming adoption in the study 192

region. 193

log (𝑧𝑧)𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝑀𝑀𝐹𝐹 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐹𝐹 + 𝛽𝛽3𝐴𝐴𝐴𝐴𝐴𝐴 + 𝛽𝛽4𝐴𝐴𝐹𝐹𝐴𝐴𝐹𝐹 + 𝑒𝑒1……………… (5) 194

Where, 195

MF=Marketing factors 196

CBF= Cost and benefit factors 197

ATT= Attitude of the farmers towards organic farming 198

EXTF= Extension factors (Influence of the extension officer to the farmers for adopting organic tea farming) 199

2.5.Description and measurement of variables used in the model 200

The study's dependent variable was adoption of organic farming, while the study's independent variables were 201

divided into three categories: farmers' personal attributes, farmers' attitude toward organic farming, and perception-202

related variables. Factors that could impact the personal traits of farmers include age, education, experience in 203

farming, and farm size. Alternatively, variables associated with perception included environmental factors, control 204

factors (marketing, cost, and benefit), and social aspects (influence of the extension officer viz. extension factors). 205

Table 1 contains a description of the variables and the technique of measurement. 206

Table 1. Variables used in the multiple linear regression and binary logistic regression models 207

Variables Type Measurement

9

Dependent variable (y)

Adoption Categorical

variable

1 for adoption of OF, 0 for non-adoption

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)

3. Results and discussion 208

3.1.The selected characteristics of the respondents 209

Six features of the farmers have been chosen for analysis in the current research. Age, education, farm size, 210

farming experience, knowledge of organic farming, and attitude toward organic farming were all considered. The 211

characteristics of the respondents’ farmers and their descriptive statistics are presented in Table 2. An individual's 212

age is a major social component of existence. It is one of the most essential aspects of a person's personality. The 213

age of a person is the stage of development at which the individual is expected to make decisions and analyze events 214

(Bayei and Nache, 2014). 215

Table 2. Salient features of the selected characteristics of the respondents 216

Characteristic

s (unit) Category

No. of

responden

ts

Percen

t

Mean

SD

t-

statistic

s All

farmers

Adopter

s

Non-

adopters

Age (yrs) Young aged (up 36 20.22 46.04 44.18 47.91 10.39 -1.802ns

10

to35)

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 1.952ns 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

The respondents varied in age from 26 to 66 years of age, with an average of 46.04 years and a standard 217

deviation of 10.93. According to Khalil et al. (2013), we divided respondents into the following three groups, which 218

are summarized in Table 2. Data exhibited in Table 2 indicates that the middle aged constituted the highest 219

proportion (43.82%) of the respondents, followed by the old aged category (35.96%) and the young aged category 220

(20.22%). The collective percentage of the middle and old aged category was 79.78%, which constituted the huge 221

majority of the respondents. Because of their age, young and middle-aged respondents have a wider outlook and are 222

significantly more exposed to both social and mass media influences than elderly respondents. It helps people 223

become more aware of and sensitive to organic farming challenges, as well as develop a positive perception of 224

organic agriculture. However, the findings show that the majority of respondents ranged in age from middle-aged to 225

elderly, implying a less favorable attitude toward organic agriculture. The most critical socio-demographic attribute 226

of an individual is education, which has an effect on the individual's views and manner of perceiving and 227

comprehending certain social occurrences (Badiger, 2015).In the process of improving knowledge, comprehension, 228

and character, education is also regarded as a need for the mind. This is an imperative concern for the adoption of 229

better agriculture technology skills, methods, and procedures (Zahid et al., 2013). The average education score of the 230

respondents under the study ranged from 0 to 14, with an average education score of 6.52 and a standard deviation 231

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of 3.71 (Table 1). The results presented in Table 3 show that more than two-fifth (43.26%) of the respondents had no 232

education, while 41.57% of the respondents belonged to the primary level, followed by 10.11% at the secondary 233

level, and only 5.06% above the secondary level. A considerable number of respondents (51.68%) fell into the 234

primary to secondary education category, while only a small number passed above secondary education. From the 235

above findings, it can be said that respondents were progressive, but they are still far away from higher education. 236

Whereas, the average education of the organic farming adopter farmer was higher than that of the non-adopter 237

farmer, and the difference between them was significant. That means adopter farmers were more aware of organic 238

farming, which subsequently helped them form a positive attitude towards organic farming. According to the farm 239

holding category, the respondents were classified into three categories following Shakib and Afrad, 2014. It was 240

found that the utmost quantity of respondents belonged to medium farm holding category (73.03%) followed by the 241

large (21.35%) and small (5.62%) farm size categories. (Table 1). However, the overall average farm size of the 242

farmers was 2.28 hectares, which was higher than the national average of 0.59 hectares (BBS, 2015). That means 243

farmers in the study area possess a higher average farm size than the national average farm size. Conversely, the 244

organic farming adopter farmers had a bit higher average farm size compared to non-adopter farmers, but the 245

difference was non-significant. Larger farms have a better chance of transforming a portion of their land to organic 246

farming (Oluwasus, 2014). This might be due to the fact that large farm size farmers had an option to trial organic 247

practice on small parts of their farms before they eventually adopted organic practice on the entire farm as a 248

sustainable decision. Similarly, it can be assumed that large farmers might not have as much financial stress to look 249

for substitute means to increase their income by replacing traditional farming technology with organic farming 250

technology (Genius et al., 2006). This can allow a farmer to escalate the benefits and shortcomings associated with 251

this activity. Agriculture is a complex undertaking, and some information can only be gained through years of 252

experience. Respondents' farming experience ratings varied from 3 to 29, with an average of 15.31. (Table 1). On 253

the basis of experience, respondents were divided into three groups, according to Prodhan and Afrad, 2014. 254

According to the findings in Table 1, the proportion of those who fell into the medium farming experience category 255

was 39.89%, while the proportion of those who fell into the low farming experience category was 34.27%, while 256

only 25.84% of them fell into the high farming experience category. The cumulative percentage of respondents in 257

the low and medium farming experience categories was 74.16 percent, which constituted the huge majority of 258

respondents. We also observed that adopter farmers had less farming experience than non-adopter farmers, and the 259

12

difference was significant. It can be said that farmers with less farming experience are more likely to form a positive 260

attitude towards the adoption of organic farming, as those who have been farming for a very long time are usually 261

old and resistant to change (Adesope et al., 2012). It is one of the most important human behaviors that increases his 262

/her consciousness, and makes him/her conversant with facts that ultimately affect the covert and overt behavior of 263

human beings. The knowledge scores on organic farming of the respondents ranged from 9 to 20, with an average of 264

15.67 and a standard deviation of 3.05 (Table 1). Results in Table 1 indicate that the largest percentage (61.80%) of 265

respondents were in the medium knowledge class, compared to 21.35% in the highest and 16.85% in the lowest 266

knowledge category. However, it was shown that the vast majority of respondents in the research region (83.17 267

percent) had a medium-to-high understanding of organic farming. It was also found that there was a significant 268

difference between adopter and non-adopter knowledge of organic farming. That indicates organic farmers are more 269

likely to show a favorable attitude towards organic farming. This could be due to the fact that farmers' knowledge 270

gained over time in an agricultural farming system may aid in assessing farm practices and influence their adoption 271

decisions (Sall et al., 2000). Attitude is a very influential aspect in moving towards sustainable agriculture for the 272

farming community. The computed attitude scores of the respondents ranged from 17-48, the mean being 34.31 with 273

a standard deviation of 8.62 (Table 1). The data revealed that over half (53.37%) of respondents were in favor of 274

organic farming and 26.97% were very positive, while just about two-fifths (19.66%) of respondents had 275

unfavorable attitudes toward organic farming. However, an overwhelming majority of survey respondents (80.34 276

percent) were positive about unfavorable behavior in organic farming. We observed a substantial difference in 277

attitudes toward adopters and non-adopters, with adopters having a more positive view. This is because the adopters 278

had a higher educational level, which increased their knowledge and beliefs, thus determining their positive attitude. 279

3.2. The perceptions of the growers towards organic farming on different factors 280

Perception empowers an individual to recognize his attitudes concerning the objects and circumstances in 281

his environment and to act accordingly. Farmers' perceptions regarding organic tea growing were examined across 282

four aspects in this study. These include environmental concerns, marketing aspects, cost and benefit considerations, 283

and social/extension factors. 284

3.2.1. Environmental factor 285

13

The organic farming system is based on the uniformities of landscape production and non-production 286

functions, where the importance is placed on environmental aspects (Birkhofer et al., 2017). Environmental 287

perception is a very important factor in the motivation for the adoption of organic tea farming. Concerning all 288

environmental claims, there was a substantial difference in perception between organic tea farmers and non-organic 289

tea growers (Table 2). Organic growers have a more favorable perception than non-organic growers because most of 290

the organic growers have a better understanding of the real effects and long-term impact of chemical fertilizers. 291

Alternatively, non-organic farmers were unaware of natural resource conservation since most of them concentrated 292

more on high yields and increasing short-term production. This may mean that the two groups of farmers have 293

divergent perceptions and points of view on various environmental factors, implying that they also have divergent 294

levels of environmental consciousness and care for organic farming. As a result, there was a substantial difference in 295

environmental awareness between organic and non-organic tea farmers (Table 3). Information presented in Table 4 296

revealed that education and knowledge of organic farming were significantly correlated with environmental factors, 297

i.e., farmers who had more education and more knowledge of organic farming were concerned about environmental 298

conservation. 299

3.2.2. Marketing factor 300

Agricultural marketing covers the wide range of activities encompassed in moving agricultural products 301

from the farmhouse to the customer. When it comes to organic farming, it is critical for farmers to be aware of not 302

just the nature of organic agriculture, but also the market for organic goods (Khaledi et al., 2010). The marketing 303

factor is another important motivational factor for the adoption of organic farming. In all statements except one, 304

organic tea farmers had a more positive image than non-organic tea growers in terms of marketing considerations, 305

i.e., consumers tend to buy more organic agricultural products than products grown using chemicals, because both 306

organic and non-organic growers realize that the customers are very much concerned about their health and the 307

nutritional value of the product. That’s why there was no significant difference (Table 2). The overall perception of 308

marketing factors was higher in the case of organic growers than in non-organic growers (Table 3) because the 309

majority of non-organic tea farmers believed that there was no difference in farm-gate prices between organic and 310

conventional tea. Further, non-organic tea growers believed that there were no adequate buyers for organic tea. On 311

the contrary, the organic growers had a favorable attitude towards marketing because the Kazi and Kazi tea states 312

14

purchase organic tea directly from the organic tea growers. Table 4 shows that older farmers with greater 313

agricultural experience and an understanding of organic agriculture had a more positive attitude towards marketing. 314

3.2.3. Cost and benefit factor 315

Economics provides a rational basis for making decisions to use a technology among various options 316

(Caswell et al., 2001). At the moment, most organic farmers are motivated by economic rather than non-economic 317

considerations (Flaten et al., 2005; Padel, 2001), whereas cost and benefit are important factors in organic farming 318

conversion in Bangladesh (Sarker & Itohara, 2008). Regarding the farmers' perceptions on cost and benefit factors, 319

five statements were analyzed, and only two statements were significant between organic and non-organic tea 320

growers (Table 2). There was no significant change in cost-benefit perception for the other three statements, i.e., 321

‘total OF cost is higher than chemical farming cost’, ‘cost of production can be reduced because family labor can be 322

utilized in OF’ and ‘cost of labor in OF is less than chemical farming." This is because both organic growers and 323

non-organic growers thought that organic farming involves only organic inputs and processing of organic inputs like 324

organic fertilizer, organic pesticide, etc. and for intercultural operations require more labor. In addition, due to the 325

decreasing cattle population, all organic inputs are not sufficiently available. That’s why they have to buy organic 326

inputs at a higher cost. Because of this, the ultimate cost of production of organic farming gets higher. But the 327

overall mean average of organic tea growers is higher than non-organic tea growers (Table 3), as organic growers 328

are aware that the demand for organic tea is increasing day by day and organic tea fetches higher prices in the 329

market places. In addition, organic tea remains sold by targeting both the top and international classes, ultimately 330

contributing to more profit. Conversely, non-organic tea growers assumed that organic products were only for 331

upper-class people due to their higher price. In addition, lack of proper marketing channels means they can’t sell it 332

in urban areas, and local people are not very much interested in buying organic products at the maximum price from 333

the local market. As a result, the organic product remains unsold, resulting in lower profit. For these reasons, non-334

organic growers tend to have a lower perception of the cost and benefit aspect. According to the data in Table 4, the 335

farming experience and knowledge of organic farmers have a positive, significant connection with their cost and 336

benefit perspective. This implies that more farming experience and more knowledge of organic farming among the 337

growers leads to a tendency towards a more favorable perception of the cost and benefit factor. 338

3.2.4. Social/Extension factors 339

15

In this study, we considered five statements to analyze the farmers' perceptions related to the extension 340

factors (Table 2). The results presented in Table 2 reveal that only one statement, i.e., ‘Training progressive farmers 341

and early adopter farmers to accept and develop organic farming" was insignificant among organic and non-organic 342

tea farmers. This might be due to the fact that both organic and non-organic tea farmers perceived that the training 343

program could help them learn more organic farming practices, which motivated them to learn organic farming. 344

However, in the case of the other four statements, the organic tea farmers had a more favorable perception in terms 345

of extension factors than the non-organic farmers. Organic tea farmers had a higher perception of extension factors 346

than non-organic tea farmers (Table 3). A significant difference in the perception of extension factors was observed. 347

This is because organic farmers feel that the extension worker's influence will increase the adoption of organic 348

farming by providing them with additional organic farming information to help them improve their farms. 349

Moreover, education of the farmers had a positive, significant correlation with extension factors, which implies that 350

farmers with more education were more concerned about extension programs to enhance the adoption of organic 351

farming. 352

Table 2. Perception of the respondents towards organic tea farming 353

Statements Mean score

t-value 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 0.081ns

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

16

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 354

Table 3. Comparing views of organic and non-organic farmers on a broad scale 355

Sl.

No Factors

Mean score t-value Sig.

OF NOF

1. Environmental factor 19.21 13.00 12.18 0.000

2. Marketing factor 18.81 12.97 9.46 0.000

3. Benefit and cost factor 17.61 14.47 6.40 0.000

4. Extension factor 17.53 13.51 9.538 0.000

Table 4. Relationship of the growers’ personal characteristics and their perception 356

Variables Age Education Farm size Farming Experience Knowledge on organic

farming

Environmental

factor 0.185ns 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 0.203ns 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 357

3.3.Factors influencing the growers’ confidence for the formation of attitude toward organic farming 358

The individual's attitude towards organic farming is defined by his or her evaluation of its conduct. 359

Regression analyses were used to examine the influence of factors on attitude development. Table 5 summarizes the 360

findings. The regression coefficient of only two factors, namely education and knowledge, made a major 361

contribution to organic farming out of six variables (Table 5). The remaining five factors had no meaningful impact 362

on the outcome. The R2 value is 0.248, and the F value is 6.51, both of which are significant at the 0.000 level. The 363

R2 result indicates that the two variables included in the regression analysis explained 24.8 percent of the overall 364

variance in respondents' attitudes about organic farming. 365

366

17

Table 5. Regression coefficients between respondents' attitudes about organic farming and their chosen attributes in 367

a general linear model method 368

Selected characteristics

of the respondents

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

(Constant) 32.635 8.420 3.876 .000

Age .133 .127 .424 1.052 .300

Education .397 .152 .431 2.610 .013

Farming experience -.278 .215 -.512 -1.289 .205

Farm size .047 .540 .013 .087 .931

Knowledge .689 .331 .309 2.080 .044

Environment -.120 .239 -.079 -.500 .620

R2 0.248

Adjusted R2 0.126

F 6.51

However, due to the internal relations between the variables, the correct contribution of the elements could 369

not be represented. As a result, it was decided to conduct a stepwise multiple regression analysis, the results of 370

which are shown in Table 6.The regression model, which together represents 20.6 percent of the overall change in 371

attitude towards organic farming, was only included in two variables out of 6, namely education and knowledge. 372

The F value was 5.32, which is statistically significant at the 0.000 level. Given the substantial contributions of the 373

two variables stated above to the variance in farmers' attitudes about organic farming, the researchers rejected the 374

null hypotheses and concluded that each of the two factors had a significant impact on the respondents' "attitude." 375

Additionally, the unique contribution of each of the two variables was identified by examining the changes in the R2 376

value that happened when a specific variable was included in the step-wise regression model. Table 7 displays the 377

findings. The two factors may explain 20.6 percent of the overall variance in respondents' attitudes, leaving the 378

remaining 80.4 percent unexplained. Knowledge alone accounted for 11.1 percent of the variance in attitude toward 379

organic farming, whereas education accounted for 9.5% of the variation. 380

Table 6. Regression coefficients of respondents' attitudes toward organic farming with their chosen attributes 381

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

R2 0.206

Adjusted R2 0.168

18

F 5.328

Table 7. Changes in multiple R2 for enter of a variable into the step-wise multiple regressions for respondents’ 382

attitude towards organic farming 383

Model Independent variables R2 value R2 change

Variance

Explaining

(percent)

1 Knowledge 0.111 0.111 11.1

2 Education 0.206 0.095 9.5

3.4. Factors boosting tea growers’ belief for adoption of organic farming 384

To identify the factors triggering the adoption of organic tea farming, we employed a binary logistic 385

regression model. This study identified some of the factors that have a significant influence on farmers' adoption of 386

organic tea farming. Table 8 shows the results of the binary logistic regression that identified two important factors, 387

i.e., attitude and CBF, significantly contributed regarding organic tea farming adoption. The results also showed that 388

EXT and MF had no statistically significant contribution to organic farming adoption by the farmers. However, 389

attitude and CBF were significantly positively related to farmers' belief in organic farming adoption at 0.5% and 390

0.01% significant levels. 391

Table 8. Results of binary logit model 392

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 R2: Cox and Snell 0.672; Nagelkerke 0.896; Log likelihood: 48.295.

Signif. codes: 0 0.01 ‘**’; 0.05 ‘*’ 393

Our model also predicted adoption probability at different attitudes and CBF levels (Figure 2). The 394

adoption probability of the farmers shows a discrepancy with different attitudes and CBF levels. Results suggested 395

that predicted adoption probability was found to be higher with those farmers having a highly favorable attitude and 396

a high perceived CBF level towards organic farming. 397

19

398

399

Figure 2. Predicted adoption probability of organic farming according to attitude and CBF (cost and benefit factor) 400

level 401

We also plot partial dependency and the ICE-curve (Individual Conditional Expectation-curve) to observe 402

the marginal effects of each significant variable (i.e., attitude and CBF level) individually and jointly towards 403

adoption probability (Figure 3). Partial dependency is very helpful for visualization of the partial association 404

between the predictors and response variables. In addition, the ICE-curve helps to visualize the functional 405

relationship of individuals’ observations and the predicted response when a strong and heterogeneous relationship 406

exists. Figure 3 (a) suggests that adoption probability is linearly associated with the predictors for both cases of 407

20

attitude and CBF. Form the ICE-curve we can see that the effect of both attitude and CBF is highly linear for some 408

individuals and somewhat less linear for others (Figure 3(b)). We also plotted the joint effect of attitude and CBF on 409

predicted adoption probability and demonstrated that adoption increases with the increase of both attitude and CBF 410

level. 411

412

413

21

414

Figure 3. Marginal effect of attitude and CBF on adoption probability: a) partial dependency plot, b) ICE-curve and 415

c) Interaction effect of attitude and CBF on adoption probability: x-axis represents attitude; y-axis represents cost 416

and benefit factors (CBF); and corresponding grid represents adoption probability. 417

Finally, we evaluated model performance by using a Receiver Operating Characteristics (ROC) curve, 418

which demonstrates the analytical ability of a binary classification system (Figure 4). The ROC curve explains the 419

true positive rate (sensitivity) against the false positive rate (specificity) at various threshold settings. In general, a 420

model indicates better performance when it gives curves closer to the top-left corner. In our case, we also observed 421

the curve closer to the top left corner indicates high sensitivity, which means better performance. In addition, we 422

also calculated the Area Under the Curve (AUC), whose value lies between 0 and 1. In particular, a model is 423

considered to be best when the AUC value is greater than 0.5, and we found an AUC value of 0.965 for our model, 424

which confirms a high level of accuracy (Figure 4). 425

426

22

Figure 4. ROC- curve of binary logit model 427

3.5.Theoretical contribution and policy implication 428

First our findings provide evidence of adopters and non-adopters tea growers perception on various 429

dimension related to organic farming. Secondly we sketched out that growers' belief towards organic tea 430

farming adoption is regulated by attitude and control factors (cost and benefit factors). The results of our study 431

are very useful for policy implications to enhance organic tea farming adoption. Most of the farmers in our 432

country are resource poor and require financial incentives to carry out farming activities. In this regard, 433

government subsidy programs on agricultural inputs and various agricultural technologies can boost organic tea 434

farming production. Our findings show that education and knowledge are two important factors triggering the 435

tea growers' formation of a favourable attitude towards organic farming. So it is necessary to provide training 436

and awareness programs to the farmers to make them aware of marketing, cost and benefit, and environmental 437

factors in relation to organic farming. Participatory extension programs and better extension approaches by the 438

Bangladesh tea board, for example, different motivational program is highly recommended for building grower 439

confidence towards adoption of organic tea farming. 440

4. Conclusion 441

Our study addressed individual’s personal characteristics and control factors that regulated an individual’s 442

adoption behavior. Results of our study suggested a significant difference in perception levels for adopter and non-443

adopter tea growers on various aspects related to organic farming. We observed organic farmers had better insight 444

on environmental aspects and negative impact of chemical fertilizer. At the same time, non-adopters were found 445

more sincere on high yields and profitability rather focusing conservation of natural resources using organic farming 446

techniques. Regarding forming a positive attitude towards organic farming, knowledge and education significantly 447

influence the belief of an individual. Farmers with a positive perception are more likely to adopt organic farming 448

technology than those who have a low perception. Our findings demonstrate that the adoption behavior of an 449

individual is regulated by their positive attitude. In addition, farmers' choice behavior is driven by the perceived cost 450

and benefit of organic farming. This result suggests that profitability is another determinant also affecting the 451

decision. In this regard, it would be necessary to investigate the profitability of organic tea farming for further 452

research. Finally, to enhance the knowledge and upgrading the farmers’ attitude towards organic farming adoption, 453

23

better extension contact between the Bangladesh Tea Board and farmers is needed. Intensive research on organic 454

tea by the researchers and a holistic approach by various sectors of government regarding organic tea cultivation 455

would be beneficial to construct targeted policy for sustainable development. 456

Acknowledgment 457

This work was financially supported by RMC research grant from BSMRAU. The authors would like to 458

thank the Department of Agricultural Extension and Rural Development, BSMRAU, for logistical help. 459

Data Availability 460

The datasets generated during and/or analysed during the current study are available from the 461

corresponding author on reasonable request. 462

Declarations 463

Conflict of interest: The authors declare that they have no known competing financial interests or personal 464

relationships that could have appeared to influence the work reported in this paper. 465

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