World Complexity Science Academy

 HAOR AREA PEOPLES’ ADAPTATION STRATEGIES TO CLIMATE CHANGE INDUCED EVENTS IN KISHOREGANJ, BANGLADESH

Authors:

Azizul M. Baten1* and Kabir M. H1

1 Author affiliation: Department of Statistics, Shahjalal University of Science and Technology, Sylhet-
3114, Bangladesh

* Correspondent author: Azizul M. Baten – baten-sta@sust.edu

Received: 07 / 08 / 2021

Accepted: 22 / 02 / 2022

Published: 01 / 01 / 2022

DOI: 10.46473/WCSAJ27240606/04-05-2022-0002//full/html

Category: Research Paper

ABSTRACT

Background: Bangladesh is a vulnerable country with respect to climate change especially in Haor areas because of its geographic location, lateral of flood plains, high population density, overhead level of poverty and irresistible dependency on nature. An extreme weather events due to climate change pose a risk to future food security.
Objectives
: This study analyzed the haor area people’s adaptation ability and significant factors influencing their adaptation towards climate change.
Methods/Approach
: Various descriptive statistical analysis and frequency distribution were used to examine the status of various adaptation strategies and village-wise binary logistic regression analysis was performed to determine the factors that significantly affects haor area people’s adaptation to climate change.
Results: The people of Khoishore, Dalargaon and Hasimpur villages adopted different adaptation strategies like take loan, seasonal migration, job switching, and changing crop calendar etc. taken because of climate change induce events such as flash flood, riverbed fill up and riverbank erosion. Education level, possession of own farming land, yearly family income, perception level of people about climate change impact are found most significant factors that motivated people to take various adaptation strategies.
Conclusions: Government and NGO’s should come forward to arrange alternative income opportunities for the people and to build up sustainable flood control embankments to prevent the damage of flash flood in haor area due to climate change induce events.

Keywords: Climate change, Adaptation, Logit regression, Haor Area, Bangladesh

1. Introduction 

Bangladesh is the most vulnerable area to several natural disasters and calamities related to climate change that are the occurrences of flood, cyclone and storm surge, flash flood, drought, tornado, riverbank erosion and land slide (Disaster Report, 2015). Haors (basin like structure) with the unique hydro-ecological characteristics are located in the north-eastern region of Bangladesh covering about 1.99 million ha of area. There are many haors in Bangladesh because of its unique geographic location, dominance of flood plains, high population density, elevated level of poverty and overwhelming dependency on nature and its resources and services and where water remains either stagnant or in flash flooding condition. Haor in Kishoreganj district is very much important in geo-physical, economic, social and cultural point of view (Kishoreganj Zilla, 1993; District Statistics, 2014). People of the Itna and Mithamoin upazilla of the Kishoreganj district is mainly farmer and is fully dependent on their agricultural land. Haors are important areas for Boro rice cultivation but early flash floods often wash away standing crops and people lose their harvest (Ahmed, 2017). Early flood, hailstorm and drought are the main constraints to grow modern boro rice (Mirza, 1997; Alam et al., 2010). Though flood is the common phenomenon in the haor areas, people have had an experience about the seasonal and flash flood with its frequency and magnitude. But now a days they are unable to predict about the flood due to different development activities in the upstream, embankment and river filling as well as change of river flow. 

Flash floods are a common incident in the Haor region in the pre-monsoon period, but poor management of the rivers and embankments and the decline in the navigability of the rivers have worsened the situation over the years. Early flash floods in Haor areas is the result of climate change which is having a bad impact on agricultural productivity, natural fish breeding, land use practice, lifestyles and livelihoods (Seraj, 2017). In April 2017 flood attack unpredictably and severely in the Haor areas and damage agricultural crop with a large amount. Last several years after liberation in 1974, 1988, 1998, 2004, 2010, and recently 2017 flood attacks severely in this Haor area of Mithamoin Upazilla under Kishoreganj district and damage huge amount of rice production. There are a few empirical works have been conducted to know the land use pattern, farmers’ perception, adaptation strategies of climate change and impact of flood on rice production (Milliman et al., 1989; Haq et al., 1996; Ahmed, 2006; Basak et al., 2009; GoB and UNDP, 2009; Islam et al., 2011; Sarker et al., 2012; 2014; Khan et al., 2012; Asaduzzaman et al., 2010; Monjur-Ul-Haider and Zakaria, 2015; Amin et al., 2015; Uddin, 2012, 2014; Uddin et el., 2017) but no studies are focused yet to examine the adaptation strategies adopted by the people living in the Haor area of Mithamoin Upazilla. Therefore, this study attempts to determine the factors that significantly affect Haor area people’s adaptation towards climate change induced events.

2.Methodology

2.1 Survey Area and Sample Size

The structured questionnaire has been used in the interview of survey data on the haor area people’s adaptation strategies to climate change, and factors associated with the adaptation strategies in Khatkhal union, Mithamoin Upazilla in Kishoreganj District, Bangladesh. A total of 230 respondents collected covering 80 individuals from Khoishore village, 60 individuals from Dalargaon village and 90 individuals from Hasimpur village. Mithamoin Upazilla is located 24013/ north to 24031/ north latitude and 90056/ east to 91016/ east longitude with an area of 222.92 sq. km to area 200.52 sq. km, is located in between 24°22′ and 24°32′ north latitudes and in between 90°48′ and 91°01′ east longitudes. It is bounded by Tarail and Itna Upazillas on the north, Nikli, Katiadi and Kishoreganj sadar Upazillas on the south, Austagram Upazilla on the east, Nikli and Karimganj Upazillas on the west. 

2.2 Logistic Regression Analysis

Village wise individual logistic regression analysis was performed to determine the factors that significantly affects haor area people’s adaptation to climate change.

2.3 Data Description and the Variables

Dependent variables: There are in total of 17 adaptation strategies practiced by the people of Khatkhal union, Mithamoin upazilla under Kishoreganj district. Each of the adaptation strategy is created as a dummy variable and it is determined by assigning  a value of ‘1’ for farmers who indicated that they have taken adaptive measures in response to negative effects of climate change and a value of ‘0’ for farmers who indicated they have not been engaged in any adaptive measures at all in response to negative effects of climate change. Each of this dummy variable i.e. adaptation strategy has been considered as a dependent variable in the binary Logit model. After decoding of the adaptation strategies there were in total of 9 dummy variables which were considered for 9 independent binary Logit models. These dependent variables include taking loan, job switching, modern effective seed, changing crop calendar, homestead gardening, preventing and avoiding climate events, duck rearing, fishing and buffalo rearing.

Explanatory Variables: The explanatory variables include possession of own farming land to the farmers, family members (size), age, income, education level, climate change perception, consequence of climate change perception, climate change impact on crop production and climate change impact on fertility of the cultivated land.

3.Results and Discussion

3.1 Results on the Adaptation Strategies to Cope up with the Climate Events

To cope up with the climate events, farmers, fishermen and people in other occupation took different adaptation strategies are shown in (table 1). 

Table 1: Adaptation Strategies to cope up with the Climate Events

Characteristics

Categories

Count

Percentage (%)

Adaptation Practices in Agriculture

Taking loan from Bank

53

23.0

Taking loan from Local Money Lenders

131

57.0

Taking loan from NGO

64

27.8

Migration

42

18.3

Job switching

110

47.8

Submergence / flood tolerant crop varieties

23

10.0

Introducing short duration crop varieties

56

24.3

Changing crop calendar

58

25.2

Upland house

37

16.1

Homestead vegetable gardening

38

16.5

Tree plantation

79

34.5

Follow weather forecast

17

7.4

Aman Rice Cultivation

30

13.1

Adaptation Practices in Livelihoods and Fishing

Duck rearing

110

48.5

Fishing with nets on flooded lands

85

37.6

Fishing from the land and ‘bill’ after the water dried out

32

14.1

Buffalo rearing

53

23.3

Adaptation Practices for Riverbank Erosion

Tree plantation

76

33.6

Distribute Khas lands among eroded people

40

17.7

Introduce alternative income opportunities

122

54.0

Erosion tolerant embankment

39

17.3

Adaptation Practices for Infrastructure

Construct flood friendly infrastructure

100

43.7

Repair or reconstruction of houses

81

35.4

Bamboo and Chailia (raised flood-proof houses)

36

15.7

Disaster endurable house

62

27.2

Institutional grounds (flood/cyclone shelter)

12

5.2

Raising plinths (above the flood level)

24

10.5

Low-height submersible embankments

7

3.1

Adaptation Practices In Health

Long lasting insecticide treated nets

28

12.5

Bed nets

185

82.6

Distributed stickers, poster and installed billboards

9

4.0

Boil water and Potash alum

18

8.0

Source: Author’s Computation

Among them, most of them take loan from local money lenders (57%) or switch their job (47.8%). Other noticeable practices take loan from NGO (27.8%) and Bank (23%), introducing short duration crop varieties (24.3%), tree plantation (34.5%), seasonal migration (18.3%), homestead vegetable gardening (16.5%), upland house (16.1%) and ‘Amon’ rice cultivation (13.1%). Fishermen and people in other occupation practice four different adaptation strategies. Duck rearing (48.5%) is the most important of them. Other practices are- fishing with nets on flooded lands (37.6%), buffalo rearing (23.3%) and fishing from land and ‘bill’ after the water dried out (14.1%). The popular adaptation practices are- alternative income opportunities (54%), tree plantation (33.6%), distributing ‘khas’ lands among eroded people (17.7%) to cope up with the damage of riverbank erosion. Adaptation strategies in terms of infrastructure are: constructing flood friendly infrastructure (43.7%), repairing or reconstruction of houses (35.4%), disaster endurable house (27.2%). Similarly, some adaptation strategies are followed in health. They are bed nets (82.6%), long lasting insecticides treated nets (12.5%), using boil water and potash alum (8%) and distributing stickers, poster and install billboards to aware people (4%).

3.2 Results on Reasons for Taking Different Adaptation Strategies in Different Sectors

3.2.1 Agriculture and Livelihood Sector

The reasons for adaptation practices in Agriculture and Livelihoods are shown in (table 2a). Flash flood is found the main reason behind choosing most of the adaptation strategies. According to the 69.1% of the people, flash flood is the main reason behind seasonal migration and riverbed fill up (36.5%) is the second main reason behind migration. Flash flood (73.9%) is the main reason behind job switching, riverbed fill up (39.5%) and riverbank erosion (23.9%) are the second and third main reason respectively behind job switching. It could be said that flash flood (86.1%) is the only reason behind changing crop calendar. For community-based seed preservation, 92.1% respondents identified flash flood as the main reason and 21% identified the riverbank fill up. For using flood tolerant rice varieties, 89.1% of the people identified flash flood as the main reason behind it. 97.2% of the people believe that flash flood is the main reason behind introducing short duration crop varieties. The people of 84.3% think that flash flood is the reason to do homestead vegetable gardening and 21.3% think that it’s because of riverbank erosion. For tree plantation adaption strategy, 72.5% believe that flash flood is the main reason and 22.7% think that riverbank erosion is the second main reason.

Table 2a: Reasons for Adaptation Practice in Agriculture and Livelihoods

Adaption Strategies

Categories

Count

Percentage (%)

Migration

Flash Flood

159

69.1

Heavy Rainfall

8

3.5

Seasonal Storm

1

0.4

Riverbank Erosion

63

27.4

Sheela Brishty

8

3.5

Riverbed Fill up

84

36.5

Job Switching

Flash Flood

170

73.9

Heavy Rainfall

2

0.9

Seasonal Storm

Riverbank Erosion

55

23.9

Sheela Brishty

13

5.7

Riverbed Fill up

91

39.5

Changing Crop Calendar

Flash Flood

198

86.1

Heavy Rainfall

5

2.2

Seasonal Storm

1

0.4

Riverbank Erosion

37

16.1

Sheela Brishty

10

4.3

Riverbed Fill up

77

33.4

Community Based Seed Preservation

Flash Flood

211

92.1

Heavy Rainfall

4

1.7

Seasonal Storm

2

0.9

Riverbank Erosion

37

16.2

Sheela Brishty

13

5.7

Riverbed Fill up

48

21.0

Flood Tolerant Rice Varieties

Flash Flood

204

89.1

Heavy Rainfall

5

2.2

Seasonal Storm

3

1.3

Riverbank Erosion

30

13.1

Sheela Brishty

9

3.9

Riverbed Fill up

37

16.2

Introducing Short duration Crop Varieties

Flash Flood

225

97.2

Heavy Rainfall

7

3.0

Seasonal Storm

Riverbank Erosion

25

10.9

Sheela Brishty

11

4.8

Riverbed Fill up

29

12.6

Homestead Vegetable Gardening

Flash Flood

194

84.3

Heavy Rainfall

6

2.6

Seasonal Storm

1

0.4

Riverbank Erosion

49

21.3

Sheela Brishty

15

6.5

Riverbed Fill up

30

13.0

Tree Plantation

Flash Flood

166

72.5

Heavy Rainfall

4

1.7

Seasonal Storm

28

12.2

Riverbank Erosion

52

22.7

Sheela Brishty

10

4.4

Riverbed Fill up

42

18.3

Table 2b: Reasons for Adaptation Practice in Agriculture and Livelihoods   

Adaption Strategies

Categories

Count

Percentage (%)

Following Weather Forecast

Flash Flood

168

73.4

Heavy Rainfall

4

1.7

Seasonal Storm

47

20.5

Riverbank Erosion

18

7.9

Sheela Brishty

9

3.9

Riverbed Fill up

34

14.8

Take Loan

Yes

180

78.3

No

50

21.7

Reason for taking Loan

Flash Flood

160

88.9

Heavy Rainfall

11

6.1

Seasonal Storm

Riverbank Erosion

16

8.9

Sheela Brishty

8

4.4

Riverbed Fill up

60

33.3

 

 Source: Author’s Computation

The people from hoar region are not that much aware to follow weather forecasting to avoid different climate events are shown in (table 2b). The people of 73.4% believe that the occurrence of flash flood in advance is the reason to to follow weather forecasting. The affected people usually take loan from bank, NGO and local money lenders. When asked about whether they take loan or not as adaptation strategy, 78.3% responded yes. For this, 88.9% respondents think that effect of flash flood is the main reason and 33.3% think that riverbed fill up is the second main reason for taking loan.

3.2.2 Fishing and Livelihoods Sector:

The reasons for adaptation practices in Fishing and Livelihoods are shown in (table 3). To take fishing as an adaptation strategy, 66.1% respondents think flash flood is the main reason and 44.8% think riverbed fill up is the second main reason. For duck rearing, 68.7% think flash flood is the main reason and 39.1% think riverbed fill up is the second main reason. For introducing alternative income opportunities, 71.6% respondents believe that flash flood is the main reason and 38.9% believe that riverbed fill up is the second main reason, as well as 28.8% believe that riverbank erosion is the third main reason. For construction and maintenance of infrastructure, 69% think that flash flood is the main reason, 34.1% think that riverbed fill up is the second main reason and 24.5% think that riverbank erosion is the third main reason.

Table 3: Reasons for Adaptation Practice in Fishing and Livelihoods

Adaption Strategies

Categories

Count

Percentage (%)

Fishing

Flash Flood

152

66.1

Heavy Rainfall

9

3.9

Seasonal Storm

2

0.9

Riverbank Erosion

51

22.2

Sheela Brishty

10

4.3

Riverbed Fill up

103

44.8

Duck Rearing

Flash Flood

158

68.7

Heavy Rainfall

5

2.2

Seasonal Storm

Riverbank Erosion

50

21.7

Sheela Brishty

11

4.8

Riverbed Fill up

90

39.1

Construction and Maintenance of Infrastructure

Flash Flood

158

69.0

Heavy Rainfall

2

0.9

Seasonal Storm

7

3.1

Riverbank Erosion

56

24.5

Sheela Brishty

11

4.8

Riverbed Fill up

78

34.1

Distribution of Khas Lands among Eroded People

Flash Flood

124

54.1

Heavy Rainfall

9

3.9

Seasonal Storm

4

1.7

Riverbank Erosion

66

28.8

Sheela Brishty

6

2.6

Riverbed Fill up

89

38.9

Introduce alternative Income Opportunities

Flash Flood

164

71.6

Heavy Rainfall

5

2.2

Seasonal Storm

3

1.3

Riverbank Erosion

59

25.8

Sheela Brishty

12

5.2

Riverbed Fill up

85

37.1

 Source: Author’s Computation

For the distribution of khas lands among eroded people, 54.1% respondents consider flash flood as the main reason, 38.9% consider riverbed fill up is the second main reason and 28.8% respondents consider riverbank erosion is the third main reason.

3.2.3 Water and Sanitation Sector:

The reasons for adaptation practice in Water and Sanitation are shown in (table 4). In case of flood proof raised tube wells implantation, 50.2% respondents consider flash flood is the main reason and 49.8% respondents consider flood as the second main reason. For flood proof sanitary latrine implantation, 43.4% respondents consider flash flood is the main reason and 56.6% respondents consider flood as the second main reason. Behind the reason of using boil and potash alum mixed water, 48.9% respondents consider this for potable water crisis, 15.9% consider this for daily needs and 35.2% consider this for drinking purpose.

Table 4: Reason for Adaptation Practice in Water and Sanitation

Adaption Strategies

Categories

Count

Percentage (%)

Flood Proof Raised Tube Wells

Flash Flood

115

50.2

Flood

114

49.8

Heavy Rainfall

Flood Proof Sanitary Latrines

Flash Flood

99

43.4

Flood

129

56.6

Heavy Rainfall

Using  Boil Water and Potash Alum

Potable Water Crisis

111

48.9

Daily Needs

36

15.9

Drinking Purposes

80

35.2

Source: Author’s Computation

3.2.4 Infrastructure Sector

The reasons for adaptation practice in infrastructure are shown in (table 5). The people of 61.3% consider flash flood is the main reason behind construction of flood friendly infrastructure and 41.7% consider flood. Behind the reconstruction of houses, 65.5% people consider flash flood is the main reason and 38.9% consider flood, as well as 10% consider riverbank erosion. In case of bamboo and chailia (raised flood-proof houses), 58.5% people consider flash flood is the main reason and 48.9% consider flood is the reason. For building disaster endurable house, 61.3% people consider flash flood behind the reason, 39.1% consider seasonal flood and 7% consider seasonal storm. Behind taking shelter in institutional grounds, flash flood is the main reason according to 61.1% people and seasonal flood is the reason according to 46.7%. When the people were asked about the reason behind raising plinths (above the flood level), 53.9% people believe that flash flood is the main reason and 47.8% think that seasonal flood is the reason. The people of 63.5% take loan to reconstruct the damage. The people of 27.4% take loan from bank, 39% from NGO and 39.7% from local money lenders.

Table 5: Reasons for Adaptation Practice in Infrastructure

Adaption Strategies

Categories

Count

Percentage (%)

Construction of Flood Friendly Infrastructure

Flash Flood

141

61.3

Flood

96

41.7

Heavy Rainfall

3

1.3

Riverbank Erosion

19

8.3

Seasonal Storm

3

1.3

Repair or Reconstruction of Houses

Flash Flood

150

65.5

Flood

89

38.9

Heavy Rainfall

3

1.3

Riverbank Erosion

23

10.0

Seasonal Storm

6

2.6

Bamboo and Chailia (raised flood-proof houses)

Flash Flood

134

58.5

Flood

112

48.9

Heavy Rainfall

3

1.3

Riverbank Erosion

14

6.1

Seasonal Storm

2

0.9

Disaster Endurable House

Flash Flood

141

61.3

Flood

90

39.1

Heavy Rainfall

3

1.3

Riverbank Erosion

20

8.7

Seasonal Storm

16

7.0

Institutional Grounds 

Flash Flood

140

61.1

Flood

107

46.7

Heavy Rainfall

6

2.6

Riverbank Erosion

7

3.1

Seasonal Storm

7

3.1

Raising Plinths (above the flood level)

Flash Flood

123

53.9

Flood

109

47.8

Heavy Rainfall

17

7.5

Riverbank Erosion

11

4.8

Seasonal Storm

1

0.4

Taking Loan to Reconstruct the damage

Yes

146

63.5

No

84

36.5

Taken Loan from Where

Bank

40

27.4

NGO

57

39.0

Local Money Lenders

58

39.7

Source: Author’s Computation

3.2.5 Health Sector

The reasons for adaptation practice in health are shown in (table 6). According to 32.9% people use long lasting insecticide treated nets for dengue, 38.7% think it’s for malaria, 28.4% think it’s for chikunguniya. 55.1% consider using bed nets for malaria, 31.6% for chikunguniya and 13.3% for dengue. When people were asked about if there were any awareness building strategy taken prior to the disasters, only 32.8% responded yes. The people of 68% believe that distribution of stickers, poster and installed billboards to raise awareness for preventing diarrhea and another 20% think that it had been for preventing malaria.

Table 6: Reasons for Adaptation Practice in Health

Adaption Strategies

Categories

Count

Percentage (%)

Long lasting Insecticide Treated Nets

Dengue

51

32.9

Malaria

60

38.7

Chikunguniya

44

28.4

Snake Bite

Bed Nets

Dengue

21

13.3

Malaria

87

55.1

Chikunguniya

50

31.6

Snake Bite

Poster, Sticker or Installed billboards Strategy

Yes

75

32.8

No

154

67.2

Distribution of stickers, poster and installed billboards to Raise Awareness

Allergy

1

1.3

Malaria

15

20.0

Dengue

5

6.7

Diarrhea

51

68.0

Dysentery

1

1.3

Chikunguniya

2

2.7

Source: Author’s Computation

4.Results on Village-Wise Logistic Regression Analysis

The output of Khoishore village for 9 individual binary Logit models with 9 adaptation strategies are shown in (table 7). Possession of own farming land is statistically significant for practicing homestead gardening and preventing and avoiding climate events. It’s because- people who have own farming land can do homestead vegetable gardening when other crops are affected by climate change as well as they try to prevent climate change by planting more trees in their own land. In education level, who are class VIII pass or more, significantly practice the adaptation strategy of changing crop calendar than others. It’s because- people with higher education are more aware about the consequences of climate change. The people who believe that there is severe negative impact of climate change on crop production, are significantly motivated to duck rearing, tree plantation and following weather forecast. It’s because- crop production is severely affected by climate change; they are driven to adopt new income opportunities and take steps to prevent climate change induced events. 

The output of Dalargaon village for 9 individual binary Logit models with 9 adaptation strategies are shown in (table 8). Possession of own farming land is statistically significant for practicing job switching, introducing modern and effective seed and changing crop calendar. Possession of own farming land is important for practicing modern and effective seed in land and changing crop calendar. In case of lease or sublease, people cannot take their decision independently which are found significant. Family members (size) significantly influences to taking loan from bank, NGO or local money lenders. Yearly income significantly influences for fishing. Adaptation practice of modern and effective seed is significantly lower among the people who have only signatory education level. Fishing adaptation is found significantly lower among the people who are class VIII pass or higher. The people who have higher level of perception about climate change, significantly change their crop calendar. People who aware about the consequences of climate change significantly adopt to duck rearing. The people who are aware about the impact of climate change on fertility of land, significantly motivated to homestead vegetable gardening. 

The output of Haismpur village for 9 individual binary Logit models with 9 adaptation strategies are shown in (table 9). Yearly income significantly influences practicing modern and effective seed. The people with lower education level (signatory or less) migrate or switch their job more significantly than the people with higher education. The people with class 5 pass or higher education level significantly adopt modern and effective seed practice more than the people with lower education. The higher education level people are aware of using modern and effective seed like submergence/ flood tolerant rice variety and short duration crop variety.  

Table 7: Binary Logistic Regression Output of Khoishore Village

 

 

Adaptive Strategies
Explanatory Variable

Taking Loan

Job Switching

Modern Effective Seed

Changing Crop Calendar

Homestead Gardening

Preventing & Avoiding Climate Events

Duck Rearing

Fishing

Buffalo Rearing

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Own Farming Land

1.024

.978

.915

.910

2.518

.318

.816

.792

11.86

.024

5.984

.049

3.784

.140

1.827

.443

.597

.571

Family Members

1.253

.097

1.048

.688

1.170

.268

.870

.207

.956

.812

1.024

.849

1.046

.748

1.113

.358

.843

.173

Age

1.015

.503

.977

.255

.965

.183

1.018

.382

.989

.721

1.014

.568

1.018

.422

.981

.354

1.023

.327

Income

1.000

.119

1.000

.197

1.000

.934

1.000

.491

1.000

.416

1.000

.063

1.000

.543

1.000

.584

1.000

.890

Education (Base- Illiterate)

                                   

Education (Signatory)

1.523

.540

.841

.772

2.234

.245

2.969

.075

2.209

.412

1.240

.753

3.497

.087

.448

.190

.414

.226

Education (Primary)

.914

.954

.235

.325

1.150

.931

13.09

.091

5.819

.309

7.266

.202

1.414

.825

.000

.999

.834

.910

Education (Class VIII)

1.3e9

.999

3.102

.500

23.20

.064

20.13

.050

.000

.999

4.8e09

.999

.908

.962

1.075

.963

.589

.779

Education (S.S.C.)

.000

.999

3.509

.525

1.4e10

.999

6.2e09

.999

1.1e09

.998

1.9e09

.999

.000

.999

.000

.999

2.9e07

.999

Climate Change Perception

.977

.961

.984

.968

.482

.136

.804

.588

.742

.648

1.503

.384

.464

.144

.787

.579

2.593

.059

Consequence Perception

.882

.817

1.264

.632

1.132

.829

1.181

.731

.539

.439

.891

.844

1.006

.992

.989

.983

.683

.491

Impact on Crop Production

.271

.332

1.457

.680

5.514

.232

1.230

.812

5.3e08

.999

.139

.049

.112

.028

1.697

.543

1.2e09

.999

Impact on Fertility

.000

.998

14.47

.056

.184

.088

.468

.407

5.9e16

.998

.402

.358

3.07e08

.999

.000

.998

1.4e15

.998

Model Summary

 

Base Outcome

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No. of Observation

79

79

79

79

79

79

78

79

78

Prob>H-L value

0.465

0.852

0.183

0.738

0.775

0.648

0.372

0.734

0.683

 

N.B.: Significant coefficients are marked in bold letters

Table 8: Binary Logistic Regression Output of Dalargaon Village

 

 

Adaptive Strategies
Explanatory Variable

Taking Loan

Job Switching

Modern and Effective Seed

Changing Crop Calendar

Homestead Gardening

Preventing & Avoiding Climate Events

Duck Rearing

Fishing

Buffalo Rearing

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Own Farming Land

.901

.891

.052

.013

36.00

.009

12.74

.030

.844

.838

.823

.834

.636

.555

1.629

.656

3.182

.181

Family Members

1.357

.047

1.075

.713

1.595

.095

.928

.760

.906

.501

.999

.995

1.017

.905

1.127

.604

1.033

.852

Age

.991

.698

.976

.414

.957

.233

.978

.523

.969

.221

1.002

.949

.962

.106

1.049

.172

1.023

.390

Income

1.000

.747

1.000

.909

1.000

.100

1.000

.606

1.00

.766

1.000

.218

1.000

.771

1.000

.043

1.000

.523

Education (Base- Illiterate)

                                   

Education (Signatory)

.524

.409

.249

.249

.027

.044

.520

.634

.491

1.723

1.874

.546

1.413

.673

.126

.116

1.151

.897

Education (Primary)

.247

.235

.816

.816

.550

.674

.727

.804

.343

.274

6.377

.142

.562

.626

.020

.029

5.081

.221

Education (Class VIII)

.195

.316

.855

.855

.255

.531

1.607

.830

.869

.756

3.0e9

.998

2.106

.647

.001

.006

.645

.805

Education (S.S.C.)

9.9e07

.999

.964

.964

7.8e06

1.00

5.5e07

.999

.999

5.5e9

1.5e9

.999

7.4e09

.999

.000

.999

1.356

.905

Climate Change Perception

1.900

.314

.842

.842

1.344

.648

6.350

.022

.567

1.410

1.061

.927

1.463

.505

3.397

.096

1.089

.892

Consequence Perception

.917

.868

.673

.673

2.007

.300

.869

.847

.660

.781

1.869

.300

.284

.050

1.965

.370

1.524

.474

Impact on Crop Production

                                   

Impact on Fertility

.000

1.00

.000

1.00

9.8e10

.999

2.5e08

1.00

1.00

.000

3.2e8

1.00

1.7e09

1.000

0.000

1.000

4.1e08

1.000

Model Summary

 

Base Outcome

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No. of Observation

59

59

59

59

59

59

58

59

58

Prob>H-L value

0.084

0.390

0.863

0.723

0.395

0.074

0.439

0.668

0.579

 

N.B.: Significant coefficients are marked in bold letters

Table 9: Binary Logistic Regression Output of Haismpur Village

 

 

Adaptive Strategies
Explanatory Variable

Taking Loan

Job Switching

Modern and Effective Seed

Changing Crop Calendar

Homestead Gardening

Preventing & Avoiding Climate Events

Duck Rearing

Fishing

Buffalo Rearing

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

Odds Ratio

P

value

 

Own Farming Land

.598

.694

.744

.654

.602

.538

1.189

.794

2.714

.101

2.161

.232

.807

.704

1.214

.733

.394

.225

 

Family Members

2.166

.027

1.000

.999

.722

.064

1.049

.705

1.075

.541

1.082

.561

.962

.715

1.171

.182

.846

.250

 

Age

.884

.040

1.024

.361

.945

.125

1.038

.140

1.002

.934

.970

.276

.967

.126

1.001

.957

1.046

.128

 

Income

1.000

.390

1.000

.128

1.000

.018

1.000

.112

1.000

.188

1.000

.698

1.000

.910

1.000

.158

1.000

.681

 

Education (Base- Illiterate)

                                     

Education (Signatory)

.092

.100

9.850

.003

.537

.501

1.303

.695

.944

.928

.440

.226

.836

.763

.924

.895

4.568

.056

 

Education (Primary)

5.8e7

.998

4.647

.119

19.503

.006

.899

.915

3.315

.156

26.210

.007

.923

.923

.658

.624

.899

.932

 

Education (Class VIII)

9.5e5

.999

4.441

.265

1.575

.747

11.662

.067

7.1e17

.998

4.680

.276

2.227

.568

1.561

.762

.000

.999

 

Education (S.S.C.)

9.2e6

.999

.611

.752

7e09

.999

5.900

.244

1.1e10

.999

2e09

.999

.297

.399

.547

.680

.000

.999

 

Education (H.S.C.)

8.6e8

1.00

.000

1.00

3.7e08

1.00

3.5e09

1.00

1.2e10

1.00

1.2e09

1.00

9.6e08

1.00

.000

1.000

.000

1.000

 

Education (Graduate)

2.1e8

1.00

.000

1.00

.000

1.00

1.3e09

1.00

6.8e09

1.00

.000

1.00

5.2e09

1.00

.000

1.000

.000

1.000

 

Climate Change Perception

.435

.309

1.719

.235

2.181

.165

1.121

.773

.532

.125

2.353

.077

1.224

.578

1.192

.631

.899

.819

 

Consequence Perception

1.610

.559

1.938

.200

.977

.970

1.905

.154

1.030

.945

.371

.066

.478

.071

2.150

.068

.778

.602

 

Impact on Crop Production

.000

.999

11.56

.057

1.867

.710

.280

.229

2.1e08

.999

.194

.142

.312

.350

.864

.899

3.6e08

.999

 

Impact on Fertility

                                     

Model Summary

 

Base Outcome

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No Adaptation

No. of Observation

86

86

86

86

86

86

85

86

85

Prob>H-L value

0.000

0.940

0.812

0.149

0.870

0.533

0.683

0.356

0.552

 

N.B.: Significant coefficients are marked in bold letters

5.Conclusion

This study examined the various adaptation strategies taken to cope up with the climate change induce events in Khatkhal union of Mithamoin Upazilla, Kishoreganj and performed village-wise binary logistic regression analysis to determine the factors that significantly affect haor area people’s adaptation to climate change.

The people living in haor area practice different adaptation strategies. Seasonal migration, job switching, changing crop calendar, seed preservation, flood tolerant rice varieties, homestead vegetable gardening, tree plantation, following weather forecasting, take loan, duck rearing, alternative income opportunities, flood friendly infrastructure, flood proof raise tube wells,fishing with nets on flooded lands, buffalo rearing, fishing from land and ‘bill’ after the water dried out are found the adaptation strategies adopted by the majority of the people where flash flood is the main reason; riverbed fill up and riverbank erosion are second and third reason. 

For Khoishore village, possession of own farming land was found statistically significant for practicing homestead gardening and preventing and avoiding climate events i.e. for practicing tree plantation and following weather forecast; Education level was statistically significant for people’s adaptation to changing crop calendar and people who believe that there is severe negative impact of climate change on crop production, were significantly motivated to duck rearing, tree plantation and following weather forecast. For Dalargaon village, possession of own farming land was found statistically significant for practicing job switching, introducing modern and effective seed, and changing crop calendar; family size significantly influences taking loan; yearly income significantly influences for fishing; signatory education level was found significantly lower for practicing modern and effective seed; people who have higher level of perception about climate change, significantly change their crop calendar; awareness about the consequences of climate change perception significantly influence duck rearing; and climate change negative impact on fertility significantly motivate people to homestead vegetable gardening. For Hasimpur Village, yearly income significantly influences to use modern and effective seed; people with lower education level (signatory or less) migrate or switch their job more significantly than the people with higher education; people with class 5 pass or higher education level significantly adopt modern and effective seed practice more than the people with lower education.

Climate change phenomenon has been an increasing concern around the world especially for the developing countries, like Bangladesh and  Haor area is the most vulnerable area to several natural disasters especially flood and flash flood and every year this natural calamities upset people’s lives in that region. During and after the disasters, people are used to live a miserable life both socially and economically in there. Failure to do satisfactory studies on the impact of climate change on rice yield and the adaptation ability of the rice farmers to climate change may affect to investigate possible planning strategies to reduce vulnerabilities. So, this type of study should be given national priorities. Otherwise, it may create obstacle in poverty eradication and sustainable development. And government should take immediate steps to build up sustainable flood control embankments to prevent the damage of flash flood in haor area. During and after the natural disasters in haor region, government should come forward to arrange alternative income opportunities for the people and others who are severely affected by the climate change induce events.

References

Ahmed, F. H. 2017. “Managing flash floods”, The Financial Express, 12 April. 

Ahmed, A.U. 2006. “Bangladesh climate change impacts and vulnerability. A synthesis; climate change cell”, Department of Environment, CDMP, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh.

Alam M. S., Quayum, M. A. and Islam, M. A. 2010. “Crop production in the haor areas of Bangladesh: Insights from farm level survey”, The Agricultriests, Vo. 8 No. 2, pp. 88-97.

Amin, M. R. Zhang, J. and Yang, M. 2015. “Effects of climate change on the yield and cropping Area of major food crops: A case of Bangladesh”, Sustainability, Vol, 7, pp. 898-915.

Asaduzzaman, M. Ringler, C. Thurlow, J. and Alam, S. 2010. “Investing in crop agriculture in Bangladesh for higher growth and productivity, and adaptation to climate change”, Paper presented at Bangladesh Food Security Investment Forum, 26-27 May, Dhaka, Bangladesh.

Basak, J. K. Ali, M. A. Islam, M. N. and Alam, M. J. B. 2009. “Assessment of the effect of climate change on Boro rice production in Bangladesh using CERES-rice model”, Proceedings of the International Conference on Climate Change Impacts and Adaptation Strategies for Bangladesh, 18-20 February, pp. 103-113, Bangladesh.

DAE 2003. “Annual Report: 2002-2003”, Department of Agricultural Extension, Kishoregonj.

DAE 2010. “Annual Report: 2009-2010”, Department of Agricultural Extension, Kishoregonj.

Disaster Report 2015. “Reliefweb.int report Bangladesh disaster-report”. 

District Statistics 2014. “History of Kishoreganj”, Kishoreganj Zilla, Itihas Pronayon Committee/Prokalpa, Kishoreganj.

GoB and UNDP 2009. “Policy study on the probable impacts of climate change on poverty and economic growth and the options of coping with adverse effect of climate change in Bangladesh”, Bangladesh: General Economic Division, Planning Commission, Government of the People’s Republic of Bangladesh and UNDP Bangladesh.

Huq, S. Ahmed, A. U. and Koudstaal, R. 1996. “Vulnerability of Bangladesh to climate change and sea level rise”, in Downing, T.E. (eds), Climate Change and World Food Security. NATO ASI Series, 137. Springer-Verlag, Berlin, Hiedelberg.

Islam, M. B. Ali, M. Y. Amin, M. and Zaman, S. M. 2011. “Climate Variations: Farming systems and livelihoods in the high barind tract and coastal areas of Bangladesh”, in Lal, R., Sivakumar, M.V.K., Rahman, A.H.M.M. and Islam, K.R. (eds), Climate Change and Food Security in South Asia. Springer Science+Business Media B.V.

Khan, M. N. H. Mia, M.Y. and Hossain, M. R. 2012. “Impacts of flood on crop production in haor areas of two Upazillas in Kishoreganj”, Journal of Environmental Science and Natural Resources, Vol. 5, No. 1, pp.193-198.

Kishoreganj Zilla 1993. “History of Kishoreganj”, Kishoreganj Zilla, Itihas Pronayon Committee/ Prokalpa, Kishoreganj.

Milliman, J. D. Broadus, J. M. and Gable, F. 1989. “Environmental and economic implications of rising sea level and subsiding deltas: the Nile and Bengal examples”, Ambio, pp. 340-345.

Mirza, M. M. Q. 1997. “Modeling the effects of climate change on flooding in Bangladesh”, Ph.D. Thesis, International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand. 

Monjur-Ul-Haider, M. and Zakaria, A. F. M. 2015. “Everyday Struggles and Adaptive Strategies: A Snapshot on the Impact of Climate Change Over the Livelihoods in Hail Haor”, Moulovibazar, Bangladesh. JWHSDC, Vol. 1, No. 3, pp. 4-14. http://wwhsdc.org/jwhsd/articles/

Sarker, M. A. R. Alam, K. and Gow, J. 2012. “Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data”, Agric. Sys., Vol. 112, pp. 11-16.

Sarker, M. A. R. Khorshed, A. and Gow, J. 2014. “Assessing the effects of climate change on rice yields: An econometric investigation using Bangladeshi panel data”, Econ. Anal. Policy, Vol. 44, pp. 405-416.

Seraj, S. 2017. “Nature turns her back on Haor people”, The Daily Star, 27 April.

Uddin, M. N. 2012. “An analysis of farmers’ perception and adaptation strategies of climate change in Bangladesh”, Master’s Thesis, Humboldt University of Berlin, Berlin, Germany.

Uddin, M. N. Bokelmann, W. and Dunn, E. S. 2017. “Determinants of farmers’ perception of climate change: A case study from the coastal region of Bangladesh”, American Journal of Climate Change, Vol. 6, No. 1, pp.151.

Uddin, M. N. Bokelmann, W. and Entsminger, J. S. 2014. “Factors affecting farmers’ adaptation strategies to environmental degradation and climate change effects:  A farm level study in Bangladesh”, Climate, Vol. 2, pp. 223-241.

UNEP 2001. “Working for a sustainable future”, pp. 1-48.

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