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American Journal of  
Environment and Climate (AJEC)

Farmers’ Perception on the Impact of  Deforestation Influencing Climate Change 
Flomo L. Gbawoquiya1*, Makissa P. Cherif 1

Volume 1 Issue 2, Year 2022
ISSN: 2832-403X (Online)

DOI: https://doi.org/10.54536/ajec.v1i2.557
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: September 05, 2022
Accepted: September 18, 2022
Published: September 21, 2022

Farmers’ perceptions on the effect of  deforestation on climate change differ, from 
conceptual, practical, and information barriers all acting as limitations to pro-environmental 
behavior. The study’s overall objective was to assess the farmers’ perception of  deforestation 
and its effect on climatic change. Field questionnaire surveys, focus group discussions 
(FGD), and field observations were considered for this study conducted in Suakoko District. 
Results reviled that 75% of  the respondents believed that deforestation and human activities 
are the main drivers of  climate change while 23% of  the respondents had no idea about the 
drivers of  climate change. About 90 % of  the respondents in the study area indicated that 
deforestation is a cause of  climate change. The respondents were asked if  deforestation 
could be tackled and 73% of  them perceived that it was possible to do something to halt 
it, while 22% of  the respondents didn’t know if  it was possible to stop it. Therefore, it is 
t recommended that the government create more awareness on the effect of  deforestation 
and has to put some measures in place to mitigate or stop it because, deforestation is a driver 
of  climate change. 

Keywords

Deforestation, Climate, Climate 
Change

1 Emmet A. Dennis College of  Natural Sciences, Department of  Environmental Sciences, Cuttington University, Liberia
* Corresponding author’s e-mail: lebekugeh@gmail.com

INTRODUCTION
Forests have a crucial role in the lifestyles of  billions 
of  people around the world (Sidiq, 2018), providing 
wood fuel as energy for everyday cooking and warming, 
hosting diverse types of  wildlife habitats, safeguarding 
biodiversity, and preserving the full functioning of  
ecosystem services (Ochenje et al., 2016). Unfortunately, 
overexploitation and clearance of  forest resources to 
meet the fundamental requirements of  an expanding 
population and sustain economic growth has resulted in 
fast forest loss, particularly in the tropics, which contain 
more than two-thirds of  the world’s biodiversity (Asrat & 
Simane, 2018). Forest removal and degradation result in 
major biodiversity loss and release 10% to 25% of  global 
carbon emissions. In fact, between 2014 and 2018, the 
world lost around 26 million hectares (ha) of  forest every 
year, with the tropics accounting for nearly all of  it. To 
avoid further distraction and its direct effect on human 
lives and economic development, it is crucially important 
to take a swift solution to reduce or divert the trend of  
forest resource loss at every level (Saguye, 2018). Loss 
of  tropical forests (deforestation ) is caused by various 
drivers, which occur at different scales (Falaki et al., 
2013). The main drivers of  deforestation may include 
commercial and subsistence agriculture followed by 
settlement expansion and infrastructure development 
(Woods et al., 2017). Climate change one of  the biggest 
environmental threats to food production, water 
availability, forest biodiversity and livelihoods for many 
countries in the world (Sidiq, 2018). Moreover, it is widely 
believed that developing countries in tropical regions of  
the world, sub-Saharan countries, will be impacted more 
severely than developed ones (Chen et al., 2018). 
Despite climate change being a global issue, developing 
countries need to adapts its effects. It is expected that 

Africa’s agricultural production will be greatly affected 
by climate change (Ken et al., 2020). Considering that 
the agricultural sector is a source of  livelihood for 
many people especially the poor in rural communities, it 
becomes imperative to protect the livelihoods of  farmers 
to sustain food security (Uddin et al., 2019). Surprisingly, a 
system’s ability to adapt is determined by its vulnerability 
to climate change, which is impacted by its level of  
exposure and sensitivity to the effects of  climate change. 
Flood threats, for example, can cause significant output 
losses, raising risk awareness and the need for adaptation 
measures such as increased insurance demand among 
farmers. It appears that assess to insurance and financing 
have been regarded as critical for independent adaptation 
(Ochenje et al., 2016). However, studies have revealed that 
farmers have a variety of  adaption options.
Although there are subtle differences between the two, 
climate change and weather are inextricably linked. 
Weather is a climate-related event that occurs at any one 
time, whereas climate is described as average weather 
conditions over a long period of  time that might affect 
cropping area and intensity. However, climate and weather 
(specific atmospheric conditions) have varied effects on 
cropping area, intensity, and yield (Hyland et al., 2016).
Farmers’ opinions of  climate change differ conceptually, 
practically, and in terms of  information, all of  these 
factors operate as impediments to pro-environmental 
conduct. As a result, understanding farmers’ self-identity, 
their awareness of  an environmental issue, and their views 
of  its danger is critical in personalizing activities aimed 
at improving agriculture’s environmental performance. 
These constructs may influence the likelihood of  farmers’ 
voluntary uptake of  climate change measures, and their 
participation in programs that focus on reducing the 
sector’s GHG emissions (Chen et al., 2018). 

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METHODOLOGY
In this study, survey research was conducted. Both 
qualitative and quantitative data types were collected. 
More specifically, a descriptive research design which 
is theory-based design method was performed to 
collect, analyze, and present the collected data. From an 
approximate population of  2000 which are perceived to 
be farmers, the required sample size was determined as 
follow:
 N/1+N (e2) 
2000/ (1+2000*0.052) = 2000/6 = 333
Where: N is the total number of  populations; e is the 
margin sampling error. Despite the actual determined 
sample size was 333, only 100 individuals were considered 
for the study because of  financial and time constraints. 
Among the four administrative towns, two towns were 
selected randomly. Then a structured questionnaire 
survey was used for collecting the primary data to assess 
the farmers’ perception on the effect of  deforestation on 
climate change. It also included details of  primary data on 
the respondents’ socio-economic characteristics, forest 
products consumption pattern, income-expenditure 
scenario, perceptions of  deforestation, factors 
determining deforestation, strategies to minimize or stop 
deforestation and strategies to cope with climate change 
and its effects. A purposive sampling technique was used 
to obtain a survey population in the study area. Then, 
a random sampling approach was conducted to select 
representative participants to participate in the study. For 
the accuracy and reliability of  the data, participants were 
asked for their willingness to participate in the survey 
before administering the questionnaires to them. Up on 
their agreement, they were given the detailed information 
on how to respond on each of  the provided questions.
After the required sample size was determined, field 
questionnaire surveys, focus group discussions, and field 
observations were conducted in seven communities 
within the selected District and the required data was 
collected. 

Data on Farmers’ perception on the effect of  deforestation 
on climate change was measured using the 5-point Likert 
scale. Statements were selected and each respondent was 
asked to indicate his/her perception of  agreement or 
disagreement against each statement as “strongly agree,” 
“agree,” “neutral,” “disagree,” or “strongly disagree.” 
Weights were assigned to responses as 5, 4, 3, 2, and 1, 
respectively. Total weighted score (TWS) of  a statement 
was determined by summing up the weighted responses 
of  the 5-point scale. The TWS of  a statement was divided 
by the sample size (i.e., 100) to obtain weighted average 
score (WAS). Thus, the possible range of  TWS for each 
statement was determined, and possible range of  WAS 
for a statement could range from 0 to 5. A Likert scale 
for each selected statement was computed using the 
following formula: 
Weighted average score = Total weighted score (5 x SA + 
4 x A + 3 x N + 2 x DA + 1 x SDA) ÷ Total number of  
respondents (Uddin et al., 2019).
Where SA is the total number of  respondents expressing 
the preference “strongly agree” for the statement, A is the 
total number of  respondents expressing the preference 
“agree” for the statement, N is the total number of  
respondents expressing the preference “neutral” for 
the statement, DA is the total number of  respondents 
expressing the preference “disagree” for the statement, 
and SDA is the total number of  respondents expressing 
the preference “strongly disagree” for the statement.
Generally, an analysis with frequency distribution was 
used. It was then presented using a pie/bar chart and 
tables. Inferential analysis was done using Chi-square 
test to finally agree/accept and disagree/reject. All data 
analysis was done using SPSS Version 25 and was tested 
at p<0.05 significance level

RESULTS
Among the 100 interviewed participants, 45 respondents 
were female while 55 of  them were male. The results 
show that farmers in the study area were predominately  

Table 1: Gender and educational background of  the respondents
Education Total
BSc Elementary High school Illiterate

Gender Female 7 2 22 13 45
Male 6 5 40 4 55

Total 13 7 62 17 100

Figure 1: Education level of  the respondents: Source: field data, 2021

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Am. J. Environ. Clim. 1(2) 53-58, 2022

high school graduates (62) while 17 of  the respondents 
were illiterate (Table 1).
The average age of  the farmers interviewed was 31 (±11). 
85% of  the respondents were single while only 12% were 
married. Farmers’ main activity was agriculture, business, 
and services respectively, and 88% of  the respondents 
live in rural area (Table 1). 

Table 2: The respondents’ religion distribution and their gender
Religion Total
Christian Muslim

Gender Female 39 4 45
Male 44 5 55

Total 83 9 100

Figure 2: Religion classes of  the Respondents; Source: 
Field Data, 2021

DISCUSSION 
About 90 % of  the respondents in the study area 
indicated that deforestation is a cause for climate change. 
In addition, a greater proportion of  farmers perceived 
that their activities highly depended on the forest and 
forest products. Thus, they pointed out that formerly 
strong covered with forest are now becoming scares. A 
total of   60% of  the respondents in Suakoko between 
the aged between  of  25 and 35 thought deforestation 
was perceived as the main cause of  climate change. This 
indicates that the younger farmers had more awareness 
than the older farmers. This is mainly attributed to the 
availability of  radio and smart phones. However, it 
is usually assumed that older farmers more exposed 
to changes in the climate than the younger farmers 
(Damodar & Nibal, 2020)
Variables such as age, education level, and gender 

Figure 3: Perception of  the respondents towards the 
deforestation as a main cause for Climate Change

are said to be among the main factors significantly 
influencing farmers’ perception of  climate change. 
Among these variables, deforestation in the study area 
was due to a range of  factors, including but not limited 
to expansion of  agricultural and residential lands, fuel 
wood harvest and charcoal production. Even though 
23% of  the respondents had no idea about the drivers 
of  climate change, 75% of  the respondents believed that 
deforestation and human activities as being the main 
drivers of  climate change in Suakoko. In other studies, 
deforestation was associated with the loss of  forest 
climate related indigenous knowledge (Deressa et al., 
2011). 
Gender perception on the effect of  deforestation on 
climate changes was significantly different between male 
and female respondents. Male farmers were more likely 
to perceive the deforestation effects on climate change 
compared to female farmers. This is due to the fact 
that men are the primary actors in rainfed agriculture, 
therefore they are more involved in developing climate 
change and variability adaptation measures than women 
in the agricultural sector. Women, on the other hand, are 
particularly vulnerable because they rely on non-wood 
forest products, such as trees, for food and money. 
The respondents were asked if  deforestation could 
be tackled and 73% of  them perceived that it was 
possible to do something to halt it. While 22% of  the 
respondents didn’t know if  it was possible to stop it. 
According to Ahmad & Afzal, (2020) high temperatures 
are often associated with climate change while increase 
in temperature is expected to reduce crop yields and 

Figure 4: The response of  the respondents after they 
were asked if  it was possible to tackle deforestation

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increase levels of  food insecurity.
Some researchers indicated that aged farmers could 
observe an increase in drought severity, temperature, 
strong wind and dust, and decrease in rainfall pattern 
as a result of  climate change. Furthermore, when the 
farmers’ ages are taken into account, the results suggest 
that older farmers were better able to detect changes 
in meteorological variables than younger farmers. The 
farmer’s educational level increases the likelihood of  
spotting changes in climatic occurrences. As a result, 
farmers with a higher level of  education saw an increase 
in the following meteorological variables (Abubakar et al., 
2020).
Farmers’ Perception on Climate Change
More than 89% of  the respondents in the study area have 
heard about climate change on different occasions and 
have observed that climate is gradually changing. They 
stated that an increase in climate change is making them 
more vulnerable as a result of  crop failure and occurrence 
of  many plant pathogens. Majority of  the respondents 
indicated that the occurrence of  floods was mainly due 
to climate change. Even though floods can cause some 
agricultural damage, farmers prefer floods to drought 
because the latter is more damaging to crop productivity. 
Temperatures and the number of  hot days has increased, 
according to a larger percentage of  farmers in Liberia’s 
central region.
The rainy season, according to the majority of  
respondents, begins late and finishes sooner in the year. 
Rainfall length has decreased from 6 months between May 
and October to 4 months, according to the majority of  
responses. The responders also noticed a decrease in the 
amount of  rainfall. A small proportion of  farmers across 
all sites did not notice any changes in rainfall patterns, but 
they claimed that rainfall distribution has become more 
erratic recent decades.
Farmers are well aware of  climate change and its 
repercussions, such as frequent droughts and floods, 
rising temperatures and the number of  hot days, stronger 
winds, and changing rainfall patterns, according to the 
findings of  this study (late start and early cessation of  
the rainy season). The interviewees ascribed the observed 
rise in temperature to a reduction in plant cover. Indeed, 

some farmers recall that when they were younger, the 
vegetation cover was heavier and the temperature was 
lower than it is today. Farmers have seen an increase 
in the frequency of  hot days in tandem with the rise in 
temperature, echoing the findings of  earlier workers in 
West Africa (Fahad & Wang, 2020).
Similarly, the perception that deforestation affects 
climate change is significantly impacted by education of  
respondents. Therefore, a farmer who has a higher level 
of  education considers his education in deciding whether 
deforestation affects climate change. For instance, a 
farmer with a high level of  education will have a different 
perception if  he had a low level of  education. Abid et 
al., (2019), studies associated higher education level with 
access to information on improved technologies and 
thus better perception. Similarly with age, farmers can 
better identify   the receding shoreline of  forest and its 
decreasing depth over the years. Because deforestation 
affect this, it may require experience based on age to 
identify that the forest do not return to their previous 
levels and density.
Farmers’ perception on the Impact of  Deforestation on 
Climate Change
Farmers perceived that the amount and intensity of  
the rainfall has been changing from time to time in the 
last two decades. The erratic rainfall and its variable 
distribution negatively impact on ecosystem services. 
Most respondents know that climate change impacts 
negatively on the delivery of  the ecosystem services. 
According to most of  the farmers, forest is directly 
related to water availability. The respondents enumerated 
major climate hazards (rainfall and temperature) which 
could reduce the ecosystem services provided by forest. 
The majority of  farmers stated that total rainfall was 
higher in the past because vegetation was denser, but that 
vegetation has become scarce as a result of  deforestation, 
and that rainfall is decreasing every year. More than 82 
percent of  farmers polled believe that an early end to 
the rainy season, as well as high/low intensity rainfall, 
leads in low agricultural productivity. Furthermore, due 
to decreased rainfall in Suakoko, more than half  of  the 
respondents noted a decline in the delivery of  forest 
ecosystem services. 

Table 3: Summary Chi-square analysis of  the comparison of  female and mare in relation to deforestation as a cause 
for climate change 

Chi-Square Tests
Value df Asymptotic 

Significance (2-sided)
Exact Sig. 
(2-sided)

Exact Sig. 
(1-sided)

Pearson Chi-Square 0.112a 1 0.738
Continuity Correctionb 0.000 1 1.000
Likelihood Ratio 0.112 1 0.738
Fisher's Exact Test 0.750 0.496
N of  Valid Cases 100
a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 4.50.
b. Computed only for a 2x2 table

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Despite the fact that male farmers were more likely to 
perceive the deforestation effects on climate change 
compared to female farmers, a Chi square analysis 
revealed no significant difference in gender of  the farmers’ 
perceptions about climate change due to deforestation 
indicating that their knowledge might be similar (Table 3). 
This agrees with (Sujakhu et al., 2016) who observed no 
significant difference in farmers’ perceptions of  climate 
change. 

CONCLUSIONS 
Farmers’ opinions of  the impact of  deforestation 
on climate change, as well as their concordance with 
temperature and rainfall patterns were investigated in 
this study. The relationship between deforestation and 
climate change was also investigated. Understanding local 
perspectives of  climate change is critical for developing 
successful support mechanisms for implementing 
adaptive measures on farms.
Farmers do not see climate change as a single process, 
according to the findings, and they distinguish between 
the components of  the climate system. In particular, 
farmers’ impressions of  temperature fluctuations are 
highly compatible with the hypothesized data. Farmers’ 
views on climate change were consistent independent 
of  their educational level, religious affiliation, or gender. 
Both personal and environmental factors influence 
perceptions. According to the respondents, there is a 
direct link between deforestation and climate change.

REFERENCES
Abid, M., Scheffran, J., Schneider, U. A., & Elahi, E. 

(2019). Farmer Perceptions of  Climate Change, 
Observed Trends and Adaptation of  Agriculture in 
Pakistan. Environmental Management, 63(1), 110–123. 

Abubakar, A. I., Haruna, Y., Garba, A., & Babuga, 
U. (2020). Farmers’ Perception on the Effect of  
Climate Change on Crop Production in Bauchi Local 
Government Area, Bauchi State, Nigeria. 

Afshar, N. R., & Fahmi, H. (2012). Rainfall forecasting 
using Fourier series. Journal of  Civil Engineering and 
Architecture, 6(9), 1258.

Ahmad, D., & Afzal, M. (2020). Climate change adaptation 
impact on cash crop productivity and income in 
Punjab province of  Pakistan. Environmental Science and 
Pollution Research, 27(24), 30767–30777. 

Asrat, P., & Simane, B. (2018). Farmers’ perception of  
climate change and adaptation strategies in the Dabus 
watershed, North-West Ethiopia. Ecological Processes, 
7(1), 1-13.

Chen, P., Niu, A., Liu, D., Jiang, W., & Ma, B. (2018). Time 
Series Forecasting of  Temperatures using SARIMA: 
An Example from Nanjing. IOP Conf. Series: Materials 
Science and Engineering, 394(5), 052024

Damodar, J., & Nibal, D. (2020). Farmers’ perception on 
climate change and its measurement. Disaster Advances, 
13(9), 59–66.

Deressa, T. T., Hassan, R. M., & Ringler, C. (2011). 

Perception of  and adaptation to climate change 
by farmers in the Nile basin of  Ethiopia. Journal of  
Agricultural Science, 149(1), 23–31. 

Dwivedi, D. (2019). Forecasting Mean Temperature 
using Sarima Model for Junagadh City of  Gujarat. 
International Journal of  Agricultural Science and Research 
(IJASR), 7(4), 183–193.

Elum, Z. A., Modise, D. M., & Marr, A. (2017). Farmer’s 
perception of  climate change and responsive 
strategies in three selected provinces of  South Africa. 
Climate Risk Management, 16, 246–257.

Espinola, B. (2014). The Frederick City Watershed : 
Forecasting Climate Change Impacts.

Fagariba, C. J., Song, S., & Baoro, S. K. G. S. (2018). 
Climate change adaptation strategies and constraints 
in Northern Ghana: Evidence of  farmers in Sissala 
West District. Sustainability (Switzerland), 10(5), 1–18.

Fahad, S., & Wang, J. (2020). Climate change, vulnerability, 
and its impacts in rural Pakistan: a review. Environmental 
Science and Pollution Research, 27(2), 1334–1338. 

Falaki, A. A., Akangbe, J. A., & Ayinde, O. E. (2013). 
Analysis of  Climate Change and Rural Farmers’ 
Perception in North Central Nigeria. Journal of  Human 
Ecology, 43(2), 133–140.

Fierros-González, I., & López-Feldman, A. (2021). 
Farmers’ Perception of  Climate Change: A Review 
of  the Literature for Latin America. Frontiers in 
Environmental Science, 9, 1–7. 

Hollowed, A., A’mar, T., Barbeaux, S., Bond, N., Ianelli, 
J., Spencer, P., & Wilderbuer, T. (2011). Integrating 
ecosystem aspects and climate change forecasting into stock 
assessments. ASFC Quarterly Report Research Feature, 
July–August–September, NOAA Alaska Fisheries 
Science Center.

Hyland, J. J., Jones, D. L., Parkhill, K. A., Barnes, A. P., 
& Williams, A. P. (2016). Farmers’ perceptions of  
climate change: identifying types. Agriculture and 
Human Values, 33(2), 323–339.

Ken, S., Sasaki, N., Entani, T., Ma, H. O., Thuch, P., 
& Tsusaka, T. W. (2020). Assessment of  the local 
perceptions on the drivers of  deforestation and 
forest degradation, agents of  drivers, and appropriate 
activities in cambodia. Sustainability (Switzerland), 
12(23), 1–26.

Kim, B. T., & Kim, T. (2006). Monthly Precipitation 
Forecasting Using Rescaling Errors. Water Engineering, 
10(2), 137–143.

Lee, S., Lee, Y., & Son, Y. (2020). Forecasting Daily 
Temperatures With Different Time Interval Data 
Using Deep Neural Networks. Applied Science. 

Limantol, A. M., Keith, B. E., Azabre, B. A., & Lennartz, 
B. (2016). Farmers’ perception and adaptation 
practice to climate variability and change: a case study 
of  the Vea catchment in Ghana. In Springer Plus., 5(1). 
Springer International Publishing.

Manikin, G. S. (2019). An overview of  precipitation type 
forecasting using NAM and SREF data. Camp Spring.

Mohammed, M., Kolapalli, R., Golla, N., & Maturi, S. 

 https://journals.e-palli.com/home/index.php/ajec


Pa
ge

 
58

 https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 1(2) 53-58, 2022

S. (2020). Prediction Of  Rainfall Using Machine 
Learning Techniques. International Journal of  Scientific 
& Technology Research, 9(1), 3236–3240.

Naz, S. (2015). Forecasting Daily Maximum Temperature 
of  Umeå. Department of  Mathematics and 
Mathematical Statistics, Umeå University Supervisor.

Ochenje, I. M., Ritho, C. N., Guthiga, P. M., & Mbatia, O. 
L. E. (2016). Assessment of  Farmers’ Perception to 
the Effects of  Climate Change on Water Resources at 
Farm Level: The Case of  Kakamega County, Kenya. 
5th International Conference of  Th African Association of  
Agricultural Economists.

Parmar, A., Mistree, K., & Sompura, M. (2017). Machine 
Learning Techniques For Rainfall Prediction : 
A Review. International Conference on Innovations in 
Information Embedded and Communication Systems, 
September.

Qiao, L. X., Zhang, Y., MA, W. Y., Yang, X. H., & Li, J. 
Q. (2013). Analysis Model For Forecasting Extreme 
Temperature. Thermal Science, 17(5), 1369–1374.

Saguye, T. S. (2018). Analysis of  Farmers’ Perception 
on the Impact of  Land Degradation Hazard on 
Agricultural Land Productivity in Jeldu District in 
West Shewa Zone, Oromia, Ethiopia. Energy and 
Environment Research, 8(2), 20. 

Sanogo, K., Binam, J., Bayala, J., Villamor, G. B., 
Kalinganire, A., & Dodiomon, S. (2017). Farmers’ 
perceptions of  climate change impacts on ecosystem 
services delivery of  parklands in southern Mali. 

Agroforestry Systems, 91(2), 345–361.
Sidiq, M. (2018). Forecasting Rainfall with Time Series 

Model. IOP Conf. Series: Materials Science and Engineering 
407, 407, 1–7.

Solgi, A., Nourani, V., & Pourhaghi, A. (2014). Forecasting 
Daily Precipitation Using Hybrid Model of  Wavelet-
Artificial Neural Network and Comparison with 
Adaptive Neurofuzzy Inference System (Case Study : 
Verayneh Station , Nahavand ). Advances in Civil 
Engineering, 2014.

Stone, R. C., & Meinke, H. (2006). Weather , climate , and 
farmers : an overview. Meteorol. Appl. (Supplement), 20, 
7–20.

Sujakhu, N. M., Ranjitkar, S., Niraula, R. R., Pokharel, 
B. K., Schmidt-Vogt, D., & Xu, J. (2016). Farmers’ 
perceptions of  and adaptations to changing climate in 
the Melamchi Valley of  Nepal. Mountain Research and 
Development, 36(1), 15–30.

Uddin, M. T., Rasel, M. H., Dhar, A. R., Badiuzzaman, 
M., & Hoque, M. S. (2019). Factors Determining 
Consumer Preferences for Pangas and Tilapia 
Fish in Bangladesh: Consumers’ Perception and 
Consumption Habit Perspective. Journal of  Aquatic 
Food Product Technology, 28(4), 438–449.

Woods, B. A., Nielsen, H. Ø., Pedersen, A. B., & 
Kristofersson, D. (2017). Farmers’ perceptions of  
climate change and their likely responses in Danish 
agriculture. Land Use Policy, 65, 109–120.

 https://journals.e-palli.com/home/index.php/ajec

