1. Introduction

2. Materials and Methods

2.1. Description of the Study Area

Sirajganj, located in northern Bangladesh, is a flood-prone district predominantly consisting of char regions. The district is intersected by the Brahmaputra River, commonly known as the Jamuna River, which regularly experiences overflowing during the rainy season, resulting in inundation of various parts of Sirajganj. The Chowhali sub-district within Sirajganj district is particularly susceptible to severe flooding due to its location on the banks of the Jamuna River. Considering the significant impact of floods in the region, the Chowhali sub-district was chosen as the focus area for this investigation.

Figure 1. Map of the study area. Source: Faruk and Maharjan [7].

Figure 1. Map of the study area. Source: Faruk and Maharjan [7].

The Chowhali sub-district is geographically divided into two halves by the Jamuna River. In this region, the most prevalent and severe disasters are frequent floods and riverbank erosion. The land in this sub-district is regularly lost to the river due to riverbank erosion occurring at various intervals.

2.3. Variables and Description

The respondents were asked to rate on a five-point Likert scale how likely and severe they believed floods would impact different aspects of their lives, including valuable assets, crops and livestock, physical health, and income, if they did not implement any flood adaptation measures. The scale ranged from 1 (very unlikely) to 5 (very likely) for perceived probability and from 1 (not bad at all) to 5 (very bad) for perceived severity[14].

Table 1. Description of Variables.

Table 1. Description of Variables.

Variables	Description	Measuring Unit
Explanatory variables
Age	Age of respondents	Age in years
Gender	Gender of farmers	1 if Male, 0 otherwise
Years of schooling	Years of formal schooling received by respondents	No. of years
Family size	Members in the family	No. of family members
Children under 10 years	Children under 10 years old	No. of children
Farm size	Agricultural land under cultivation in decimal	Land size in decimal
Annual income	Total annual income (after cost)	Income in thousand BDT
Livestock ownership	How many livestock farmers owned?	No. of livestock
Distance from river	House distance from river in kilometer	Distance in kilometer
Flood experience	Flood severity experienced in the last 10 years	No. of flood severity
Trust in government actions	Respondents were asked to rate the frequency of showing trust on govt. flood protection measures (4 items)	1-very untrustworthy, 2-untrustworthy, 3-neutral, 4- trustworthy, 5- very trustworthy
Flood risk awareness	Respondents were asked their opinions on five flood related statements	1-strongly disagree, 2-disagree, 3-neutral, 4- agree, 5- strongly agree
Dependent variables
Perceived flood risk	There are a total of eight components in this personal evaluation of the probability of a future occurrence (a) and the consequent damage (b)	(a) 1–very unlikely, 2–rather unlikely, 3–neutral, 4–rather likely, 5–very likely(b) 1–not bad at all; 2–rather not bad; 3–neutral; 4–rather bad; 5–very bad;
Self-efficacy	The respondent thinks that he/she is capable of taking off the described 21 measures.	1-Very unable, 2-Rather unable, 3-Neutral, 4- Rather able, 5-Very able
Response efficacy	Effectiveness of described 21 flood adaptation strategies	1-Very ineffective, 2-Rather Ineffective, 3-Neutral, 4- Rather effective, 5-Very effective
Response cost	To what extent the adaptation measures are costly (21 items)	1–very costly, 2–rathercostly, 3–neutral, 4–rather not costly, 5–very not costly;

To calculate the perceived risks associated with flood hazards for each dimension and the total perceived risk, the formulae suggested by Dowling [23] were employed. These formulas are based on attitude models widely used in marketing and psychology, providing an information-processing view of decision making [23]. The application of these formulas aligns with the concept of "threat assessment" discussed in the introduction part.

Perceived Risk ═ perceived risk probability x perceived risk consequences

$$\mathrm{O}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{l} \mathrm{P}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{d} \mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{d} \mathrm{r}\mathrm{i}\mathrm{s}\mathrm{k} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{b}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{x} \mathrm{p}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{d} \mathrm{r}\mathrm{i}\mathrm{s}\mathrm{k}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{s}$$

where n is the number of dimensions (i.e., household assets, crops and livestock, physical health, and income)

For each risk dimension that is believed to be impacted by flood, the perceived risk was calculated by multiplying the estimated risk probability by the perceived risk consequences. All four risk dimensions were added up to get the overall perceived flood risk. Each risk dimension and the overall perceived flood risk were determined for 359 char-land farmers. Overall perceived flood risk was used as dependent variable for multiple linear regressions in this study.

Initially, a list of 21 individual flood adaptation strategies was developed using the literature [10,14,22]. To check the validity of these flood adaptation measures, they were brought up for debate in FGDs during the preliminary survey. The adaptive measures were pretested before the final survey. Farmers were asked to assess their own ability to execute each adaptive measure, to rate the effectiveness, and to rate the perceived cost of implementing each adaptation strategy. Descriptive statistics and multiple regressions were used to explore how farmers perceived their risk (threat appraisals), appraised each flood adaptive measure (coping appraisals), and identified significant factors influencing those appraisals.

3. Results

3.1. Descriptive Statistics of the Study

The study's descriptive statistics are presented in Table 2. The results indicate that the average age of the farmers is 48.68, suggesting that older individuals are more likely to be engaged in agriculture. Males constituted over 70% of the survey respondents in the Char-lands. In the char-lands, the average number of years of schooling was relatively low (2.82), reflecting primary-level education, which is typical in the context of Bangladesh. This is consistent with similar findings in the Padma floodplain, where the average schooling years were 1.9 years [24]. Additionally, another study [25] found that 45% of the floodplain population had only primary education. On average, there are around six people per household, with approximately one child under the age of 10. The average land under cultivation is 131.79 decimal, indicating that agriculture is the primary livelihood option for char-dwellers. However, despite their reliance on agriculture, the farmers' average annual income was relatively low at 44,140 BDT.

Table 2. Descriptive statistics.

Table 2. Descriptive statistics.

Variables	Mean	SD	Minimum	Maximum
Age	48.68	12.78	25	85
Gender	0.70	0.46	0	1
Years of schooling	2.82	3.05	0	12
Family size	5.61	1.77	0	12
Children under 10 years	1.33	0.87	0	4
Farm size (dm)	131.79	59.43	33	363
Annual income (000’ BDT)	44.14	22.10	6	150
Livestock ownership	0.86	0.37	0	1
House distance from river (km)	1.68	0.79	0.25	4
Flood experience	2.50	0.74	1	4
Trust in government actions	2.78	0.49	1.6	4.6
Flood risk awareness	3.87	0.55	2.6	5

In the char-lands, most farmers (86%) owned livestock. Due to their location on riverine islands, farmers lived near the river, with an average distance of 1.68 kilometers. On average, farmers in the char-lands experienced 2.50 flood severity events in the last decades. Trust in government action was relatively low, with an average rating of 2.78, emphasizing the significance of private flood adaptation measures. The average flood risk awareness among farmers in the char-lands was 3.87.

3.2. Farmers’ Adoption of Flood Adaptation Strategies

Table 3 presents the percentage of adoption for twenty-one flood adaptation strategies utilized by farmers in the char-lands. According to the preliminary survey, farmers tended to implement most of these strategies, both related to farming and non-farming practices. Each adaptation measure was scored 1 if adopted and 0 if not. Given that agriculture is the primary livelihood in the char-lands, local farmers have traditionally employed various agricultural and livelihood adaptation measures. For instance, 81.89% of farmers arranged fodder for their livestock, and about three-fourths (77.44%) raised their livestock to a safer place before the onset of floods, reflecting their concern for livestock safety. To minimize crop losses, more than half of the respondents (56.27%) adopted mixed cropping practices, and 52.92% adjusted planting and harvesting times. Regarding financial strategies, around 61.84% of farmers utilized informal sources for financing, while 52.09% used official sources to continue their farming livelihood.

Additionally, more than half of the respondents saved money for emergency requirements in anticipation of the impending flood disaster which may hamper their agricultural production. These findings highlight the proactive approach of char-land farmers in implementing various adaptation strategies to cope with flood risks.

Table 3. Farmers’ adoption of flood adaptation strategies.

Table 3. Farmers’ adoption of flood adaptation strategies.

Variables	Frequency and percentage of adoption
	Frequency	Percentage
Farming flood adaptation strategies
Growing seedling in pot or sandbag	137	38.16
Mixed cropping	202	56.27
Changing crop variety	145	40.39
Adjustment of planting and harvesting time	190	52.92
Fodder arrangement	294	81.89
Raising of livestock place	278	77.44
Relocating livestock	166	46.24
Money savings	180	50.14
Informal credit	222	61.84
Formal credit	187	52.09
Alternative occupation during flood	160	44.57
Non-farming flood adaptation strategies
Construction or raising the plinth of the house	150	41.78
Fencing house	137	38.16
Raising tube wells	164	45.68
Flood-proof sanitation	162	45.13
Portable stoves	303	84.40
Arrangement of boat	135	37.60
Macha preparation	260	72.42
Dry food collection	207	57.60
Shifting family	209	58.22
Shifting valuable goods	212	59.05

Among the non-farming flood adaptation measures, a significant proportion of farmers (84.4%) installed portable cooking stoves before floods in the char-lands, as most homes with permanent cooking stoves have yards that become submerged during floods. Another crucial non-farming flood adaptation strategy is the construction of "macha," with 72.42% of farmers adopting this measure. A macha is a high stage or bed made primarily of low-cost material such as bamboo. During flooding, farmers take refuge on top of the macha and safeguard their essential belongings.

However, other structural improvements had lower adoption rates, including building or elevating the house plinth (41.78%), raising tube wells (45.68%), and implementing flood-proof sanitation measures (45.13%). These findings suggest that while some non-farming strategies are widely adopted, there is scope for further improvements in other structural measures to enhance flood resilience in the char-lands.

3.3. Farmers’ Perceived Flood Risk

The overall perceived risk associated with floods is derived by summing up the four distinct perceived risks, calculated by multiplying perceived risk probability by perceived risk consequences for each risk dimension. The minimum and maximum values for each specific perceived risk are assumed to be 1 and 25, respectively, resulting in a range of 4 to 100 for the total perceived risk. In our sample, the overall perceived flood risk is regularly distributed, ranging from 28 to 100.

Table 4. Means, and standard deviations (SD) of perceived risk probability, perceived risk severity, and specific perceived risks.

Table 4. Means, and standard deviations (SD) of perceived risk probability, perceived risk severity, and specific perceived risks.

Risk Dimensions	Perceived Risk Probability		Perceived Risk Consequences		Specific Perceived Risk
	Mean	SD	Mean	SD	Mean	SD
Household assets	3.91	0.74	3.76	0.72	15.09	4.99
Crops and livestock	4.23	0.60	4.16	0.57	17.75	4.19
Income	4.13	0.72	4.05	0.69	17.04	5.19
Physical health and diseases	3.52	0.98	3.39	0.91	12.64	6.10

The average overall perceived flood risk is 62.52, which is above half of the maximum level of perceived flood risk. This suggests that among the surveyed farmers, there is a relatively high level of perceived flood risk. Farmers perceive flood hazards differently concerning various aspects of their lives. They tend to place higher importance on the probability of floods affecting their crops, livestock, and income, with average perceived risks at 17.75 and 17.04, respectively, compared to household assets and physical health, which have average perceived risks of 15.09 and 12.64, respectively.

In the context of perceived risk probability and perceived consequences, crops and livestock are considered to have the highest mean values (4.23 and 4.16, respectively) compared to the other dimensions when assessing the potential impact of floods. On the other hand, flood is perceived to have a lesser impact on farmers' physical health and diseases, as evidenced by the lowest mean values of perceived risk probability (3.52) and perceived severity (3.59) in Table 2. These findings suggest that farmers prioritize and perceive greater vulnerability to their crops and livestock compared to other aspects of their lives when it comes to the risks posed by floods.

Figure 2. Proportion of specific risk dimensions to overall perceived risk.

Figure 2. Proportion of specific risk dimensions to overall perceived risk.

Furthermore, we assessed the contributions of each perceived risk dimension to the overall perceived risk and compared their percentages. Among the risk dimensions, perceived risk on crops and livestock had the highest contribution, accounting for 28.39% of the overall perceived risk. It was followed by farmers' income at 27.25%, household assets at 24.14%, and physical health and diseases at 20.22%. These results indicate that farmers attribute the highest importance to the risks posed to their crops and livestock, followed closely by their income and household assets, while considering physical health and diseases to have a relatively lower impact on the overall perceived flood risk.

3.4. Farmers’ Assessment of Flood Adaptation

Farmers' flood adaptation assessments encompass perceived self-efficacy, response efficacy, and response cost. Table 4 presents the means and standard deviations of these three variables. Regarding farming-related flood adaptation measures, farmers exhibited higher self-efficacy in activities like arranging fodder (3.78), raising livestock places (3.48), and practicing mixed cropping (3.29). Notably, farmers demonstrated greater self-efficacy in obtaining credit from formal sources (3.26) compared to informal sources (3.21).

For non-farming flood adaptation measures, higher levels of self-efficacy were observed among farmers for using portable stoves (4.42), preparing macha (3.95), and collecting dry food (3.70). In contrast, self-efficacy was relatively lower for structural measures such as house construction or elevation (2.78), raising tube wells (2.92), implementing flood-proof sanitation (2.92), and preparing boats (2.81). These findings highlight farmers' varying levels of confidence in different flood adaptation strategies, reflecting their perceptions of efficacy and potential outcomes associated with these measures.

In terms of response efficacy, farmers considered activities such as arranging fodder (4.07), saving money (4.00), and raising livestock places (3.83) as more effective in the context of farming-related flood adaptation measures. For non-farming adaptation measures, higher levels of response efficacy were reported by farmers for activities like house construction or elevation (4.21), flood-proof sanitation (4.13), using portable stoves (4.11), and preparing boats (3.99). These responses indicate that farmers perceive certain measures as more efficacious in mitigating flood-related challenges, highlighting their beliefs in the effectiveness of these strategies to adapt with flood risks.

Table 5. Farmers’ assessments of flood adaptation strategies.

Table 5. Farmers’ assessments of flood adaptation strategies.

Flood Adaptation Measures	Self-Efficacy		Response Efficacy		Response Cost
	Mean	SD	Mean	SD	Mean	SD
Farming flood adaptation measures
Growing seedling in pot or sandbag	3.13	0.99	2.89	0.87	3.60	0.69
Mixed cropping	3.29	1.09	3.09	0.93	3.68	0.71
Changing crop variety	2.70	0.95	2.74	0.86	2.97	0.84
Adjustment of planting and harvesting time	3.03	0.95	3.06	0.84	3.37	0.73
Fodder arrangement	3.78	0.78	4.07	0.64	2.82	0.98
Raising of livestock place	3.48	0.98	3.83	0.67	2.96	1.05
Relocating livestock	3.18	0.87	3.30	0.79	3.15	0.75
Money savings	2.77	0.97	4.00	0.75	3.22	0.52
Informal credit	3.21	0.90	2.99	0.82	2.67	0.86
Formal credit	3.26	0.88	3.45	0.77	3.32	0.80
Alternative occupation during flood	2.95	1.02	3.74	0.73	3.23	0.72
Non-farming flood adaptation measures
Construction or raising the plinth of the house	2.78	1.16	4.21	0.78	1.45	0.56
Fencing house	3.20	1.05	3.06	0.87	2.77	1.01
Raising tube wells	2.92	1.03	3.90	0.59	2.20	0.73
Flood-proof sanitation	2.92	1.06	4.13	0.61	1.97	0.57
Portable stoves	4.42	0.68	4.11	0.61	4.34	0.80
Arrangement of boat	2.81	1.05	3.99	0.70	2.18	0.74
Macha preparation	3.95	0.74	3.48	0.76	3.86	0.69
Dry food collection	3.70	0.90	3.40	0.80	3.60	1.00
Shifting family	3.35	0.90	3.54	0.86	3.48	0.77
Shifting valuable goods	3.37	0.82	3.54	0.66	3.25	0.80

Farmers perceived mixed cropping (3.68), growing seedlings in pots or sandbags (3.60), and obtaining formal credit (3.32) as more cost-effective in comparison to other farming-related adaptation measures. Among non-farming flood adaptation measures, portable stoves (4.34), macha preparation (3.86), and dry food collection (3.60) were considered more financially feasible by farmers, in contrast to other non-farming measures such as house construction or elevation (1.45), flood-proof sanitation (1.97), and boat arrangement (2.18). This indicates that farmers consider the affordability of different adaptation strategies when making decisions to address flood risks, considering the associated costs and benefits.

3.5. Factors Influencin Farmers’ Perceived Flood Risk and Flood Adaptation Assessments

This section presents the results of multiple regression models that analyze the effects of socio-demographic and cognitive characteristics on farmers' perceived flood risk across four dimensions and their overall perceived flood risk. Additionally, multiple regression analysis was also conducted to identify factors influencing farmers' flood adaptation assessments in the categories of self-efficacy, response efficacy, and response cost.

Prior to conducting the regression analysis, a multicollinearity test was performed to assess variance inflation factors (VIFs). Result showed that most of the explanatory variables exhibited VIFs of less than 2, with a maximum VIF of 3.7 (Table 6). This suggests the absence of significant multicollinearity. A standard VIF cutoff value of 10 is often used to identify substantial multicollinearity [26]. Kehinde and Ogundeji [27] further suggests that variables with VIF exceeding 10 should be regarded as highly collinear and can potentially be excluded from the model.

Table 6. Multicollinearity test: variance inflation factor.

Table 6. Multicollinearity test: variance inflation factor.

Variables	VIF	1/VIF
Age	1.21	0.83
Gender	1.09	0.92
Years of schooling	1.19	0.84
Family size	1.47	0.68
Children under 10 years	1.27	0.79
Farm size (dm)	2.12	0.47
Annual income	1.65	0.61
Livestock ownership	1.03	0.97
House distance from river	1.59	0.63
Flood experience	1.92	0.52
Flood risk awareness	2.24	0.45
Trust in government actions	1.25	0.80

Even when VIF is far below the cut-off criterion [26] or more than 5, the degree of multicollinearity should be carefully evaluated [28]. It is justified, nevertheless, to consider the multicollinearity problem beyond the cut-off point. In this situation, removing or combining variables might result in more severe issues.

3.5.1. Factors Influencing Farmers’ Perceived Flood Risk

Table 7 displays the outcomes of the multiple linear regression analyses. The dependent variable is perceived flood risk, divided into four dimensions (perceived risk in physical health and diseases, household assets, crops and livestock, and income), as well as the overall perceived risk. The explanatory factors remain consistent across all five models (as presented in Table 2). The regression coefficients for the five regression models are presented in Table 7 those who indicate how much the perceived risk for each dimension and the overall perceived flood risk are expected to vary because of a change of one unit in each significant explanatory variable.

The R squared, adjusted R squared, and F-test are also present in Table 7. The explanatory factors in our models may account for between 36.6 and 63.6% of the variation in perceived flood risks, according to the R squares for the five models. All models are highly significant according to the F-test (P-value <0.01).

Among the socio-economic factors, age is negatively associated with perceived risk in all models except crops and livestock. The perceived risk in physical health and diseases, household assets, income, and overall risk perception increased with the decrease in farmers’ age, which indicates that younger farmers were likely to have a higher perceived flood risk compared to older ones. Female farmers had significantly higher risk concerns for their health and valuable assets, whereas male farmers were found to have significantly higher perceived risks for crops and livestock than female farmers.

Table 7. Factors influencing farmers’ perceived flood risk.

Table 7. Factors influencing farmers’ perceived flood risk.

Variables	Physical Health and Diseases	Household Assets	Crops and Livestock	Income	Overall Perceived Flood Risk
Age	-0.111** (0.022)	-0.081* (0.018)	0.065 (0.015)	-0.009 (0.018)	-0.056 (0.042)
Gender	-0.144*** (0.597)	-0.117** (0.476)	0.079* (0.410)	0.028 (0.489)	-0.065* (1.133)
Years of schooling	0.032 (0.093)	0.060 (0.074)	-0.006 (0.064)	0.034 (0.077)	0.043 (0.177)
Family size	-0.028 (0.178)	-0.026 (0.142)	0.023 (0.123)	0.067 (0.146)	0.009 (0.339)
Children under 10 years	-0.005 (0.339)	-0.008 (0.270)	0.015 (0.233)	-0.062 (0.278)	-0.021 (0.643)
Farm size (dm)	0.206*** (0.006)	0.244*** (0.005)	0.049 (0.004)	0.064 (0.005)	0.197*** (0.012)
Annual income	0.119** (0.015)	0.052 (0.012)	0.096* (0.010)	-0.095* (0.012)	0.059 (0.029)
Livestock ownership	-0.069 (0.763)	-0.003 (0.608)	0.030 (0.525)	0.031 (0.626)	-0.010 (1.450)
House distance from river	-0.057 (0.416)	-0.021 (0.331)	-0.164*** (0.286)	-0.174*** (0.341)	-0.134*** (0.790)
Flood experience	0.174*** (0.486)	0.196*** (0.387)	0.204*** (0.334)	0.175*** (0.398)	0.249*** (0.923)
Flood risk awareness	0.190*** (0.712)	0.232*** (0.568)	0.129** (0.490)	0.336*** (584)	0.301*** (1.353)
Trust in government actions	-0.069 (0.593)	-0.057 (0.472)	-0.118** (0.407)	-0.070 (0.486)	-0.102*** (1.126)
R squared	0.368	0.399	0.366	0.411	0.636
Adjusted R squared	0.346	0.378	0.344	0.390	0.626
F test (P value)	0.000	0.000	0.000	0.000	0.000

Note. Significance at *p<0.1, **p<0.05 and ***p<0.001. Standard error in parenthesis. Standardized coefficients are used in regression results.

However, the overall perceived risk was significantly higher for women compared to men. Farmers with higher amounts of farmland were found to have significantly higher overall perceived flood risk compared to farmers with a low amount of farmland, with significantly higher concerns about health, diseases, and valuable assets. Farmers who had higher annual income possessed more risk in their health and agriculture (crops and livestock) and exhibited lower risk in income compared to poor farmers, while the overall perceived risk was insignificant. Flood experience and flood risk awareness were found to be vital drivers affecting all specific flood risk dimensions and overall risk perception, indicating that farmers with higher flood experience and risk awareness were likely to perceive more risk compared to farmers with less experience and awareness. Farmers’ overall perceived risk decreased with the increase in house distance from the river and trust in government actions.

3.5.2. Factors Influencing Farmers’ Flood Adaptation Assessments

This section shows how important elements have an influence on how farmers evaluate their adaptation behavior. In the multiple regression models, the three dependent variables, namely, perceived self-efficacy, perceived response efficacy, and perceived response cost, were considered with the explanatory variables listed in Table 1. The estimated coefficients for farm size, flood experience, and flood risk awareness of char-land farmers were statistically significant for all three models. Farmers who owned or cultivated more land were more likely to have a higher ability to adopt flood adaptation, considered the adaptation measures more effective, and perceived less adaptation cost compared to farmers with fewer farmlands.

Table 8. Factors influencing farmers’ flood adaptation assessments.

Table 8. Factors influencing farmers’ flood adaptation assessments.

Variables	Farmers’ Flood Adaptation Assessments
	Self-Efficacy	Response Efficacy	RESPONSE COST
Age	-0.017 (0.001)	0.019 (0.001)	0.043 (0.001)
Gender	0.013 (0.032)	0.000 (0.027)	0.012 (0.029)
Years of schooling	0.028 (0.005)	-0.033 (0.004)	0.096** (0.005)
Family size	0.069 (0.010)	-0.005 (0.008)	0.070 (0.009)
Children under 10 years	0.019 (0.018)	0.017 (0.015)	0.018 (0.017)
Farm size (dm)	0.259*** (0.000)	0.255*** (0.000)	0.098* (0.000)
Annual income	0.118** (0.001)	0.037 (0.001)	-0.045 (0.001)
Livestock ownership	0.047 (0.041)	0.050 (0.035)	0.046 (0.037)
House distance from river	0.022 (0.022)	-0.079 (0.019)	0.085* (0.020)
Flood experience	0.112** (0.026)	0.099* (0.022)	0.128** (0.024)
Flood risk awareness	0.414*** (0.038)	0.331*** (0.032)	0.571*** (0.035)
Trust in government actions	-0.017 (0.031)	-0.027 (0.027)	-0.005 (0.029)

Note. Significance at *p<0.1, **p<0.05 and ***p<0.001. Standard error in parenthesis. Standardized coefficients are used in regression results.

Similarly, farmers who experienced more flood severity and had a higher level of risk awareness tended to possess higher perceived self-efficacy, response efficacy, and response cost. The year of schooling was significant for perceived response cost, which indicates that farmers who had a higher education level would have perceived less response cost. Farmers with higher annual income were found to have higher self-efficacy, indicating that they have more ability to implement the adaptation measures. House distance from the river was positively correlated with the perceived response cost, explaining that farmers who were far from the river perceived a higher response cost for adopting flood adaptation measures.

4. Discussion

5. Conclusions

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