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American Journal of   
Environment and Climate (AJEC)

Adoption of  Climate Smart Agricultural Practices among Coastal Farmers: Insights 
from a Selected Southern Area of  Bangladesh

Md. Golam Rabbani Akanda1, Probir Kumar Mittra2*, Avijit Biswas3

Volume 4 Issue 3, Year 2025
ISSN: 2832-403X (Online) 

DOI: https://doi.org/10.54536/ajec.v4i3.5318
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: June 22, 2025

Accepted: July 25, 2025

Published: September 05, 2025

Climate-smart agriculture (CSA) methods must be implemented to protect both ecological 
stability and sustainable livelihoods, as climate change poses significant dangers to coastal 
agriculture. Understanding the motivations behind farmers adoption of  these methods is 
crucial for creating focused and successful policies, especially in climate-vulnerable areas 
like coastal zones of  Bangladesh. The purpose of  this study is to evaluate coastal farmers’ 
adoption of  climate-smart agriculture (CSA) methods and investigate the associations 
between a few chosen farmer attributes and adoption levels. In the Kalapara Upazila of  
Patuakhali district, 310 coastal farmers were chosen using a multi-stage random selection 
approach, and data was collected from them using a pre-tested interview schedule. The 
results showed that coastal farmers’ adoption of  CSA techniques varied from 40.25 to 
75.29, with a possible range of  0 to 100. The standard deviation was 8.50 and the mean 
adoption level was 56.53. Among farmers, the majority (60%) showed a medium degree 
of  adoption, followed by low adoption (21.29%) and high adoption (18.71%). CSA 
adoption was significantly positively correlated with agricultural expertise, training exposure, 
communication exposure, innovativeness, risk orientation, and economic motive, according 
to correlation analysis. In contrast, farm size exhibited a significant negative relationship 
with adoption levels. Regression analysis indicated that 31.8% of  the variation in CSA 
adoption was explained by farmers’ communication exposure (30.4%) and innovativeness 
(1.3%). One methodological limitation of  this study is its reliance on self-reported data from 
a single region, which may limit the generalizability of  findings to broader coastal contexts. 
However, the study provides a strong theoretical basis for policy formulation by linking 
farmer-level attributes with CSA adoption, emphasizing the importance of  behavioral and 
socio-economic factors. To enhance CSA adoption, stakeholders- including policymakers, 
extension agents, and local NGOs should prioritize targeted training, improve access to 
climate information, and promote farmer-led innovation and communication networks. 
Future study is necessary to examine impediments to greater adoption and evaluate the 
long-term effects on farmers’ livelihoods and environmental sustainability, as these findings 
offer important insights into the factors driving coastal farmers’ adoption of  CSA methods.

Keywords
Adoption, Bangladesh, Climate-
Smart Agriculture Practices, 
Coastal Farmers

1 Department of  Agricultural Extension and Rural Development, Patuakhali Science and Technology University, Dumki, Patuakhali, 
  Bangladesh
2 Department of  Basic Science, Patuakhali Science and Technology University, Khanpura, Babugonj, Barishal, Bangladesh
3 Department of  Agriculture, Gopalganj Science and Technology University, Bangladesh
* Corresponding author’s e-mail: probir.pstu@gmail.com

INTRODUCTION
Agriculture is the backbone of  Bangladesh’s economy, 
contributing significantly to employment, food security, 
and rural development (BBS, 2022). However, the 
country is highly vulnerable to climate change due to 
its geographical location, low-lying coastal regions, and 
frequent natural disasters such as cyclones, floods, and 
saline water intrusion (Chowdhury et al., 2022). Millions 
of  smallholder farmers in coastal regions depend on 
these climate-related issues for their livelihoods, as they 
affect agricultural productivity (Islam & Wadud, 2020). A 
viable solution to these issues is climate-smart agriculture 
(CSA), which aims to improve agricultural resilience 
while maintaining environmental sustainability and food 
security (FAO, 2018).
A variety of  methods and tools are included in CSA 
with the goals of  increasing productivity in a sustainable 
manner, enhancing climate change resistance, and 
lowering greenhouse gas emissions whenever feasible 

(Fuad et al., 2025; Lipper et al., 2014). Utilizing crop varieties 
that can withstand stress, conservation agriculture, enhanced 
water management, agroforestry, and integrated soil fertility 
management are some of  these methods (Debnath et al., 
2019). Because coastal farmers in Bangladesh are more 
vulnerable to harsh weather events, increasing sea levels, and 
salinized soil, which have a detrimental effect on agricultural 
productivity and household incomes, it is especially 
important that they adopt CSA methods (Islam et al., 2022).
Farmers in coastal Bangladesh continue to implement 
CSA at unequal rates despite its demonstrated benefits 
because of  a number of  institutional, socioeconomic, and 
environmental variables (Hoque et al., 2023). According 
to earlier research, farmers’ desire to use climate-smart 
practices is significantly influenced by a number of  
factors, including risk perception, financial resources, 
training, and information availability (Saha et al., 2019). 
Adoption is further hampered in many coastal regions 
by poor infrastructure, a lack of  extension services, and 



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problems with land tenure (Biswas et al., 2024).
Given the significance of  CSA in promoting climate 
resilience and guaranteeing sustainable agricultural 
development, it is critical to comprehend the adoption 
patterns and variables affecting its uptake among 
Bangladeshi coastal farmers. Assessing the degree of  CSA 
adoption in the southern coastal belt and investigating 
the connection between important farmers attributes and 
adoption behavior are the objectives of  this study. The 
results will help development organizations, extension 
services, and policymakers create focused interventions 
to encourage the broad adoption of  CSA practices, 
improving agricultural resilience and sustainability in the 
coastal regions of  Bangladesh.

MATERIALS AND METHODS
The study was conducted at Kalapara upazila of  
Patuakhali district where a scope of  adoption of  climate-
smart agricultural practices exists to be pursued as the 
study was concerned with the farmers’ adoption of  
CSA practices. The study’s population consisted of  
all agricultural household heads, with the exception of  
Kalapara Upazila absentees. Instead of  collecting data 
from the entire population, a sample was used. The 
sample was chosen using a multi-stage random sampling 
technique (Upazila, unions, villages, and households). Two 
of  the twelve unions that make up Kalapara Upazila were 
chosen at random during the initial phase. Eight villages 
were chosen at random from the communities that were 
part of  these two unions. With the assistance of  Sub-

Assistant Agricultural Officers, local union parishad staff, 
and local leaders of  the affected villages, a list of  all the 
farm household heads in these eight villages was created 
in the third stage. The sampling population for this study 
was made up of  1601 of  these farm household heads. 
Using a sample size calculator (www.surveymonkey.com), 
310 farmers (household heads) were chosen as the final 
sample, dispersed proportionately among chosen villages, 
taking into account a 50% response distribution, a 5% 
margin of  error, and a 95% confidence level. The sample 
size was 310 as a result. Standard procedures were used to 
measure the study’s independent variables, such as age in 
years, education in school years, farming experience was 
measured in years, agricultural knowledge was measured in 
scores, training experience was measured in day, farm size 
was measured in hectare, annual income was measured in 
taka, communication exposure was measured in scores, 
cosmopoliteness was measured in scores, innovativeness 
was measured in scores, risk orientation was measured 
in scores, economic motivation was measured in scores, 
attitude towards modern technology was measured in 
scores. The study’s dependent variable was the adoption 
of  climate-smart agriculture techniques. With a mean of  
56.53 and a standard deviation of  8.5, the respondents’ 
observed scores on the implementation of  climate-
smart farming practices varied from 40.25 to 75.29. 
Low adoption (40-48), medium adoption (49-65), and 
high adoption (66 and above) were the three categories 
into which farmers were divided (mean=/- standard 
deviation).

Figure 1: Map illustrating the data collection location in Kalapara upazila, situated in the Patuakhali District of  Bangladesh

RESULTS AND DISCUSSION
The profile of  the respondent’s sociodemographic traits 
was established, and the findings are shown in Table 1. 
The findings showed that the majority of  farmers (42.6%) 
were middle-aged (42.6 percent), no formal schooling 

(68.1 percent), medium farming experience (49.4 
percent), medium agricultural knowledge (56.1 percent), 
no training experience (72.3 percent), small farm size 
(46.1 percent), low annual income (65.8 percent), medium 
communication exposure (61.0 percent), medium 



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Am. J. Environ. Clim. 4(3) 28-34, 2025

cosmopoliteness (76.1 percent), medium innovativeness 
(57.8 percent), medium risk orientation (75.8 percent), 

medium economic motivation (61.3 percent), medium 
attitude towards modern technology (70.7 percent).

Table 1: Demographic and socio-economic characteristics of  the respondents
Characteristics 
(Measuring units)

Range Farmers Mean SD
Possi-ble Obse-rved Categories No. Perc-ent

Age (Year) Unknown 23-61 Young (up to 35) 79 25.5

43
.4

5

10
.8

6

Middle (36-50) 132 42.6
Old (>50) 99 31.9

Education 
(Year of  Schooling) 

Unknown 0-14 No formal schooling 211 68.1

1.
39 2.
5

Primary level 70 22.5
Secondary level 28 9.1
Above secondary 1 0.3

Farming experience Unknown 5-45 Low farming experience 129 41.6

21
.5

9

7.
94

1

Medium farming 
experience

153 49.4

High farming experience 28 9
Agricultural 
knowledge

Unknown 10-29 Low (Upto 12) 61 19.7

17
.7

3

5.
63

Medium (13 to 22) 184 56.1
High (23 and above) 65 24.2

Training experience 
(day)

Unknown 0-6 No training 224 72.3

1.
09 1.
9

With training 86 27.3
Farm size (Ha) Unknown 0.15-3.20 Marginal (between 0.02 

and 0.2 ha)
19 6.1

1.
12

0.
75

Small (between 0.2 and 
1 ha)

143 46.1

Medium (between 1 and 
3 ha)

138 44.5

Large farmers (above 3 
ha)

10 3.2

Annual income
(TK))

Unknown 64.10-
427.22

Low income (Upto Tk 
116)

204 65.8

11
6.

05

55
.0

6
Medium income (Tk. 
116.01 to 232.0)

86 27.7

High income (Tk. 232.01 
– 427.22 and above)

20 6.5

Communication 
exposure

Unknown 14-39 Low (14-17) 55 17.7

23
.7

8

6.
69

Medium (18-29) 189 61
High (30-39) 66 21.3

Cosmopoliteness Unknown 3-14 Low (3) 32 10.3

5.
98

2.
27

Medium (4-8) 236 76.1
High (9 and above) 42 13.5

Innovativeness Unknown 11-20 Low innovativeness 
(11-14)

33 10.6

15
.9

8

1.
49

Medium innovativeness 
(15-16)

179 57.8

High innovativeness 
(17-20)

98 31.6

Risk orientation Unknown 16-38 Low (16-26) 30 9.7

29
.9

4

3.
23

Medium (27-32) 225 75.8
High (33 and above) 45 14.5



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Table 2: Characteristics of  waste management in poultry farms
Categories (Scores) Frequency Percentage Mean SD
Low adoption (40-48) 66 21.29 56.53 8.5
Medium adoption (49-65) 186 60.00
High adoption (66 and above) 58 18.71
Total 310 100

Table 3: Relationship with selected independent variables with the adoption of  CSA practices of  the respondents
Variables Selected Independent Variables Value of  correlation coefficient (r)
Adoption of  climate 
smart agricultural 
practices

Age .010
Education .041
Farming experience -.008
Agricultural knowledge .228**
Training experience .276**
Farm size -135*
Annual income .016
Communication exposure .552**
Cosmopoliteness -.066

Economic 
motivation

Unknown 13-32 Low economic 
motivation (13-18)

56 18.1

22
.5

1

4.
51

Medium economic 
motivation (19-27)

210 61.3

High economic 
motivation (28-32)

44 14.2

Attitude towards 
modern technology 

Unknown 13-29 Low (13-22) 55 17.7

24
.0

6

2.
20

Medium (23-26) 219 70.7
High (27 and above) 36 11.6

Adoption of  
climate smart 
agricultural 
practices

Unknown 40.25-75.29 Low adoption (40-48) 66 21.29

56
.5

3

8.
5

Medium adoption (49-65) 186 60
High adoption (66 and 
above)

58 18.71

The respondents’ observed scores for the adoption of  
climate-smart agricultural practices ranged from 40.25 
to 75.29, with a mean score of  56.53 and a standard 
deviation of  8.5. Based on the mean and standard 
deviation, farmers were classified into three categories: 

low adoption (scores between 40 and 48), medium 
adoption (scores between 49 and 65), and high adoption 
(scores of  66 and above). The distribution of  farmers 
across these categories is presented in the following 
table. Table 2 indicates that the majority of  respondents 

(60.00 percent) had a medium level of  adoption of  CSA 
practices, followed by 21.29 percent with low adoption 
and 18.71 percent with high adoption. These findings 
suggest that most farmers fall within the medium to low 
adoption categories. This may be attributed to limited 
access to available CSA technologies and practices. 
Similarly, previous study reported that 56.3 percent of  
farmers had adopted CSA practices, which aligns with the 
results of  this study (Ewulo et al., 2025).
Relationship between selected farmer characteristics and 
their influence on the dependent variable
The study determined the relationship between the 
adoption of  CSA practices of  the coastal farmers on 
climate smart agricultural practices as dependent variables 

and 13 selected characteristics of  the coastal farmers as 
independent variables discussed below under following 
sections:

Relationships between Selected Characteristics 
(Independent Variables) and the Adoption of  CSA 
Practices (Dependent Variable)
This objective sought to examine the relationships 
between selected characteristics of  coastal farmers 
(independent variables) and their adoption of  CSA 
practices (dependent variable). The correlation 
coefficients between the independent variables and the 
dependent variable are presented in Table 3.
Table 3 indicates that from 13 selected characteristics, 7 



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characteristic out of  which 6 characteristics viz. agricultural 
knowledge, training experience, communication 
exposure, innovativeness, risk orientation, economic 
motivation showed positive significant relationships 
with the adoption of  CSA practices (dependent variable) 
and only 1 characteristic viz. farm size showed negative 
significant relationships with the adoption of  CSA 
practices (dependent variable).

Contribution of  Independent Variables to Adoption 
of  Dependent Variable
The correlation coefficients indicate the linear 
relationships between pairs of  variables but do not show 
the extent to which the independent variables-i.e., the 
farmers’ characteristics- contribute to the adoption of  
climate-smart agricultural practices. Therefore, a linear 
multiple regression analysis was conducted to determine 
the individual contributions of  various characteristics 
of  coastal farmers to their adoption of  these practices. 
Only those variables that showed significant correlations 
with the adoption of  climate-smart agricultural practices 
were included in the regression model. As a result, 
seven characteristics- agricultural knowledge, training 
experience, farm size, communication exposure, 
innovativeness, risk orientation, and economic motivation 
were included in the analysis. The results of  the regression 

analysis are presented in Table 4.
Among the seven variables, the regression coefficients 
for only four- farm size, communication exposure, 
innovativeness, and economic motivation were 
statistically significant, indicating that these factors made 
a meaningful contribution to variations in the adoption 
of  CSA practices (Aryal et al., 2018). The remaining three 
variables- agricultural knowledge, training experience, and 
risk orientation did not show a significant contribution.
The results of  the multiple regression analysis are 
presented in Table 4. It was found that four variables- 
farm size, communication exposure, innovativeness, and 
economic motivation were included in the regression 
model, collectively explaining 34.8 percent of  the 
total variation in adoption. The F-value of  23.075 was 
statistically significant at the 0.000 level of  probability.
Therefore, the relevant null hypotheses were rejected, and 
it can be concluded that each of  these factors made a 
significant contribution to the adoption of  CSA practices 
by coastal farmers. In other words, the coastal farmers 
who had high communication exposure, innovativeness 
and economic motivation had high adoption and 
coastal farmers who had high farm size had low level 
of  adoption. The contributions of  the respondents with 
their adoption for climate smart agricultural practices are 
discussed below:

Innovativeness .169*
Risk orientation .163*
Attitude towards modern agricultural technology -.049
Economic motivation .211**

** Significant at 0.01 level of  significance Significant at 0.05 level of  significance

Table 4: Regression coefficients of  the selected farmer characteristics in relation to their adoption CSA practices
Variable entered Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta
(Constant) 21.123 5.700 3.706 .000
Agril. knowledge .113 .075 .075 1.516 .131
Training exposure -.295 .242 -.069 -1.223 .222
Farm size -1.276 .620 -.096 -2.058 .040
Communication 
exposure

.673 .076 .522 8.840 .000

Innovativeness .578 .270 .102 2.141 .033
Risk orientation .190 .125 .072 1.522 .129
Economic motivation .186 .091 .099 2.038 .042
R2= 34.8 F, 23.075             P= .000

The unique contribution of  each of  the three variables 
was determined by observing the change in the R² value 
resulting from the entry of  each variable into the stepwise 
regression model. The results are presented in Table 5. 
Together, the two variables accounted for 31.8 percent 

of  the total variation in adoption among coastal farmers. 
Communication exposure alone contributed 30.4 percent, 
followed by innovativeness, which accounted for 1.3 
percent of  the variation. 



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Communication Exposure
Adoption would be higher with the increase of  
communication exposure i.e. communication media used 
by the farmers. The farmers who have more contact 
with communication media can easily gain various help 
in adoption of  CSA practices. So it can be inferred that 
the higher the communication exposure, the higher is the 
adoption level (r= .552**). A previous study also suggests 
that communication exposure, such as the communication 
access and their alignment with existing farming activities, 
strongly motivate farmers to enhance farmers’ capacity 
to adopt and sustain CSA practices (Ajwang et al., 2024).

Innovativeness
Adoption would be higher within the innovative 
farmers. The farmers who are more innovative can 
search for suitable climate smart agricultural practices 
in adoption. Therefore, it can be inferred that a higher 
level of  innovativeness is associated with a greater level 
of  adoption (r = .169*). A previous study also found 
that agricultural innovativeness has a significant direct 
influence on the intention to adopt CSA practices 
(Mondal & Hasan, 2025).

CONCLUSIONS
This study explores how coastal farmers adopt climate-
smart agriculture by looking at their socioeconomic 
characteristics, how much they use CSA practices, the 
challenges they face, and the information they need. 
The findings provided valuable insights into the factors 
influencing CSA adoption, revealing both enabling 
conditions and significant barriers that hinder widespread 
implementation. Key challenges included limited access 
to resources, inadequate knowledge, and infrastructural 
constraints, which necessitate well-structured 
interventions. Additionally, the study identified the 
critical need for improved access to relevant information 
and support systems to enhance farmers’ capacity to 
adopt and sustain CSA practices. These insights can guide 
policymakers and agricultural stakeholders in designing 
targeted policies, extension services, and capacity-
building programs that facilitate the seamless integration 
of  CSA practices into coastal farming systems. The study 
advances the larger objective of  guaranteeing sustainable 
agricultural development, boosting climate resilience, and 
strengthening the livelihoods of  coastal farmers in the 
face of  growing climate variability by addressing adoption 
barriers and knowledge gaps.

ACKNOWLEDGEMENTS
Research and Training Centre, Patuakhali Science 
and Technology University, Dumki, Patuakhali-8602, 
Bangladesh has been duly acknowledged for funding a 
project in the year 2018- 2019 in favor of  first author as 
Principal Investigator. 

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CSA practices
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model
R2 Adjusted 

R2
Change 
in R2

Variation explained 
(percent)

Level of  
significance

Constant+X8 Communication exposure .304 .302 .304 30.4 .000
Constant+ 
X8+X10

Communication exposure+ 
innovativeness

.318 .313 .013 .013 .015



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