3.1. Farmers’ Perceptions on Experiencing Climate Change
All of the respondents were asked a dichotomous (“yes/no” response) question about whether or not they had experienced changes to regional climate within the past 20 years. After their initial response, they were asked about their perceived experience in relation to a series of climatic events commonly associated within the literature reviewed for this study with global climate change effects in Bangladesh. To these they could respond that they had experienced decreases, increases, or no change in the occurrence of the event, or to respond that they did not know.
Figure 1 reports the responses to the first question; some 88% of respondents indicated that, within the last 20 years, they have, in their view, experienced climatic change.
Table 2 reports their responses for individual climatic events. Here, all respondents indicated that they had experienced increases in temperature, droughts, flooding, cyclones, and soil salinity. Across all events, at least 80% or more reported having experienced climatic shifts which are likely to have a negative impact on agricultural activity. While it is clear that these are perceptions of farmers, such information provides an important background of the respondent group.
Figure 1.
Proportion of respondents by self-reported experience of climatic change.
Figure 1.
Proportion of respondents by self-reported experience of climatic change.
Table 2.
Distribution of responses to perceived changes in specific climatic events (n = 88).
Table 2.
Distribution of responses to perceived changes in specific climatic events (n = 88).
Climatic Event | % of Respondents Indicating to What Level they Have Experienced the Climatic Event over the Last 20 Years |
---|
Increased | No Change | Decreased | Don’t Know |
---|
Temperature | 100 | | | |
Rainfall | | 3.4 | 96.6 | |
Occurrence of drought | 100 | | | |
Occurrence of flood | 100 | | | |
Occurrence of cyclones | 100 | | | |
Salinity level | 100 | | | |
Short winter season | 85.2 | 4.6 | | 10.2 |
Long summer season | 92 | 2.3 | | 5.7 |
Unpredictable rainfall | 90.9 | 1.1 | | 8 |
Changes of monsoon season | 80.7 | 2.3 | | 17 |
3.3. Adaptation Strategy Index
To identify those adaptive strategies which held relative importance over others an adaptation index procedure was implemented, as measured by the formula presented below (5). Farmers were asked to assess different adaptation strategies by using the same four-point rating scale described in
Section 2 to rate the importance of each strategy to their agricultural enterprises. The relative importance of adaptation strategies to climate change was calculated based on the following index formula [
17]:
where,
ASI = Adaptation Strategy Index
ASn = Frequency of farmers rating adaptation strategy as having no importance
ASl = Frequency of farmers rating adaptation strategy as having low importance
ASm = Frequency of farmers rating adaptation strategy as having moderate importance
ASh = Frequency of farmers rating adaptation strategy as having high importance
Figure 2.
Proportion of respondents by adaptation classification.
Figure 2.
Proportion of respondents by adaptation classification.
The salinity problem and droughts are the most common disasters in the region (south-western Bangladesh) where the study was conducted [
27,
28,
29]. These two climatic disasters tremendously affect the crop production system. The ranking of different adaptation strategies to climate change, as identified by the surveyed farmers, are presented in
Table 3. Out of 14 adaptation strategies, increased use of irrigation was ranked first and thus most important, among farmers’ adaptive strategies to climate change. Irrigation increases the yield of production [
30,
31], improving nutrient availability to the plants but also leading to increased soil salinity [
32,
33]. Practicing crop diversification was identified as the second-ranked adaption strategy. Continuous mono-cropping (for example rice cultivation) has different adverse effects which include pest resurgence, and soil quality deterioration, in addition to the issues of loss risk associated with monocultures. In response to these effects, farmers adopt diversified cropping practices, reducing overall farm risk and expanding opportunities for farm profit, which generally act to boost the farmers’ average incomes. The third most important adaptation strategy was the “integrated farming system” (being engaged in two or more enterprises which act symbiotically with one-another). This farming system is becoming more popular throughout the country because of its economic returns [
34,
35,
36,
37]. Crop insurance was ranked as the least important adaptation strategy. This is most likely due to (1) a significant lack of good management of finance institutions in the country underwriting agriculture and offering farm-based insurance products, (2) poor deployment of technical assistance and low-levels of farmer awareness about the use of agricultural insurance, (3) only a very recent abatement of governmental regulations and policies which placed prohibitive restrictions on insurance provision entities, and (4) an overall lack of capacity (financial, infrastructure, and human) among in-country financial institutions to float insurance programs [
38].
Table 3.
Ranked order of the adaptation strategies to climate change (n = 84).
Table 3.
Ranked order of the adaptation strategies to climate change (n = 84).
Adaptation Strategies | Importance of Your Farm | ASI | Rank |
---|
High | Medium | Low | No |
---|
Increased use of irrigation | 75 | 9 | - | - | 243 | 1 |
Practicing crop diversification | 51 | 32 | 1 | - | 218 | 2 |
Integrated farming system | 47 | 36 | 1 | - | 214 | 3 |
Use of drought tolerant varieties | 47 | 35 | 1 | 1 | 212 | 4 |
Use of salinity tolerant varieties | 48 | 29 | 2 | 5 | 204 | 5 |
Practicing crop rotation | 38 | 40 | 4 | 2 | 198 | 6 |
Cultivating short duration crops | 31 | 50 | 3 | - | 196 | 7 |
Practicing intercropping | 13 | 50 | 20 | 1 | 159 | 8 |
Find off-farm job | 20 | 35 | 27 | 2 | 157 | 9 |
Moved to Non-farm activities | 11 | 49 | 22 | 2 | 153 | 10 |
Agro forestry | 1 | 25 | 47 | 11 | 100 | 11 |
Soil conservations techniques | 2 | 20 | 52 | 10 | 98 | 12 |
Zero tillage | 5 | 21 | 48 | 10 | 63 | 13 |
Crop insurance | - | 5 | 12 | 67 | 22 | 14 |
3.4. Econometric Estimation of Factors Affecting the Farmers’ Adaptation Strategies to Climate Change Effects
Before the data analysis, the contingency coefficient test was applied to diagnose colinearity and omit independent variables that are highly dependent and strongly correlated to each other (
Table 4).
Multi-colinearity was observed between farming experience and age, extension contact and education, number of plots and farm size, family income and farm size, family income and number of plots, extension contact and cooperative involvement. Generally, it is predicted that there should be a positive relationship between family income and farm size, very important variables that might also affect the decision to adopt adaptive strategies to climate change. Therefore, both were considered in the logit model reported here instead of excluding them from the analysis. The model was run with these items omitted and the econometric estimates in those simulations were found to not have significantly changed from the model which maintains these two variables reported here. Only farming experiences, extension contact, and the number of plots are omitted from the logistic regression model in determining factors affecting the farmers’ adaptation strategies to climate change effects, as shown in
Table 5.
Table 4.
Contingency coefficient test for co-linearity between independent variables.
Table 4.
Contingency coefficient test for co-linearity between independent variables.
Variables | AG | EDU | FAMSZ | FARSZ | NUMP | FAREX | FAMIN | CRRE | TRRE | COPIN | MARAC |
---|
AG | 1 | | | | | | | | | | |
EDU | −0.046 | 1 | | | | | | | | | |
FAMSZ | 0.368 | 0.114 | 1 | | | | | | | | |
FARSZ | 0.213 | 0.393 * | 0.354 * | 1 | | | | | | | |
NUMP | 0.181 | 0.35 * | 0.382 * | 0.853 ** | 1 | | | | | | |
FAREX | 0.887 ** | −0.114 | 0.360 * | 0.163 | 0.162 | 1 | | | | | |
FAMIN | 0.195 | 0.344 * | 0.229 | 0.893 ** | 0.639 ** | 0.131 | 1 | | | | |
CRRE | 0.223 | 0.113 | 0.117 | 0.202 | 0.254 | 0.178 | 0.139 | 1 | | | |
TRRE | 0.095 | 0.331 | 0.143 | 0.344 * | 0.341 * | 0.009 | 0.245 | 0.363 * | 1 | | |
COPIN | 0.048 | 0.506 * | 0.158 | 0.347 * | 0.36 * | −0.040 | 0.237 | 0.242 | 0.343 * | 1 | |
MARAC | 0.054 | 0.495 * | 0.171 | 0.236 | 0.288 | 0.034 | 0.166 | 0.270 | 0.188 | 0.410 * | 1 |
EXCONT | 0.032 | 0.756 ** | 0.164 | 0.457 * | 0.446 * | 0.018 | 0.391 * | 0.155 | 0.355 * | 0.526 ** | 0.398 * |
Table 5.
Estimates of binary logit regression model based on farmers’ adaptation strategies to climate change effects.
Table 5.
Estimates of binary logit regression model based on farmers’ adaptation strategies to climate change effects.
Variables | Adaptation |
---|
Coefficient | Robust Std. Error | p Value |
---|
CONS | 0.905 | 3.12000 | 0.772 |
AGE | −0.110 * | 0.06550 | 0.092 |
EDU | 1.215 ** | 0.59820 | 0.042 |
FAMSZ | −2.174 ** | 0.77470 | 0.005 |
FARSZ | −10.105 ** | 3.3480 | 0.003 |
FAMIN | 0.0002 ** | 0.00008 | 0.002 |
CRRE | −1.685 | 2.31900 | 0.467 |
TRRE | 7.231 | 4.50200 | 0.108 |
COIN | 7.029 * | 3.76700 | 0.062 |
MARACC | 0.003 | 1.49100 | 0.998 |
Pseudo R 2 | 0.875 |
The findings of the regression model (
Table 5) indicate that age is negative and significantly (at 10% level) related to farmers’ adaptive strategies to climate change effects. This implies that the probability of adaptation significantly decreases the older a respondent farmer. It can be predicted that such farmers have less interest or less incentives in taking climate change adaptation measures. Perhaps older farmers do not see the necessity to adapt to climate change effects. Moreover, these older farmers may be more “set in their ways”, interested in following traditional methods familiar to them rather than adopting modern farming techniques. The similar outcome have found and explained in the articles written by Acquah and Quayum
et al. [
39,
40].
The regression model results explain that education is positive and significantly (at 5% level) related to adaptation strategies to climate change effects. This implies that the probability of adaptation to climate change is greater for those who have higher educational attainment compared to less-educated or illiterate farmers. It is obvious that educated farmers have more knowledge, a greater ability to understand and respond to anticipated changes, are better able to forecast future scenarios and, overall, have greater access to information and opportunities than others, which might encourage adaptation to climate change. Several studies found that education also positively and significantly affects the adoption of technology [
40,
41,
42,
43,
44,
45].
Family size is negative and significantly (at 5% level) related to farmers’ adaptation strategies to climate change effects. However, the negative sign on this relationship is contradictory to our initial hypothesis. This negative sign indicates that with increasing size of the family, the probability of farmers’ adoption of an adaptive strategy decreases. Prior to this study it was expected that the sign of the variable family size would have a positive sign, the logic being that large family size makes available more labor which can actively engage in work, better facilitating the adoption of adaptive measures against climate change effects,
ceteris paribus. This assumption was in line with the results of similar work on climate change adaptation strategies done by Deressa
et al. (2008, 2009), as well as the large body of literature on technology adoption such as Mignouna
et al. (2011), Tiamiyu
et al. (2009) and many others [
20,
46,
47,
48].
Given our negative results, we turned towards a deeper look at the literature on labor availability within the household and its impact on propensity to adopt new technologies or farming strategies. From this review we theorize a variety of potential explanations for the negative sign found in our work: (1) if there are sufficient opportunities for off-farm labor, which would increase household liquidity at a greater rate than on-farm activities, then there will be a flight to quality of a household’s labor endowments and a reduction in actual internal labor availability; (2) if subsistence farming is predominant among the households in the sample, then the same labor shortages assumed to be hindering adoption in income-generating agricultural activity may not be present; (3) there may be a timing issue with the increased labor demands needed to implement adaptive strategies; (4) labor markets are seen to be intertwined with credit markets, and thus if there is insufficient access to credit which can offset the lag in income between switching labor from off-farm income generation to on-farm adaptive strategies, then a larger household will choose not to reallocate labor and adopt new, more labor-intensive strategies; (5) it may be that households with an abundance of labor face risk premiums and opportunity costs different than those with less labor endowments, thus leading them to supplement labor to cope with climate change as the less expensive allocation decision compared to other investments that would be required to adopt coping strategies; or (6) a majority of the additional family members are children and/or the elderly, therefore we may assume an overestimate of labor availability using household size, and, in fact, the distribution of household members and their endowments may be a contributing factor to the risk acceptance-aversion factor of a farm household, leading them to view adaptive strategy adoption as “too risky” given their circumstances [
49,
50,
51,
52,
53]. These areas provide rife opportunity for further empirical study to provide evidence as to which, if any, is connected with the observation of a negative sign of the coefficient and its statistical significance. Other studies, such as that of Quayum and Ali (2012) [
40] have shown that family size was negatively and significantly related to adoption of technologies, but there is no definitive causation shown in the literature reviewed for preparing this work.
There is a negative and significant (at 5% level) relationship between farm size and adaptation to climate change effects. Specifically, results show that increasing size of a farm operation decreases the probability of farmers’ adoption of adaptive strategies to climate change. The reason behind this result may be the large farmers were deployed traditional technologies rather modern technologies to climate change adaptation. Moreover, large farms require greater levels of investment to implement adaptive strategies to climate change, therefore, farmers of the study failed to do that compared to small farm. Acquah explained the similar result while farm size showed negatively significant with adaptation to climate change effects [
39]. Moreover, larger farms require inputs such as seeds, fertilizer, pesticides, irrigation facilities, and more at rates which are stressors on farm budgets. For adaptation behaviors it may be that these inputs were not available or are too expensive in the study area at sufficiently large quantities. Another potential explanation, may be that all inputs were available but, due to a lack of proper management capacity in relation to farm size, large farms fail to adapt efficiently. Scarcity of labor may also be and additional motive not to engage in adaptive strategy adoption.
The result of the logistic regression shows that positive and significant (at 5% level) relationship between family income and adoption of adaptive strategies to climate change effects. This implies that farmers with high income are more likely to adopt adaptive strategies than farmers with lower incomes. The findings support projects undertaken by Government Organizations (GOs) and Non-Government Organizations (NGOs) designed to create off-farm livelihoods activities so that farmers can diversify and supplement their income and continue their agricultural operations in the face of climatic uncertainty. On the other hand, remittances and off-farm jobs might also be another source of annual family income of the farmers. Kim
et al. found that household income positively and significantly influences the adoption of adaptive to climate change [
54] while Gbeibouo (2009) explained that wealthier farmers are more interested to adapt by changing planting practices, using irrigation, and altering the amount of land farmed [
55]. Further, Nhemachena and Hassan indicate that per capita income has a positive influence on farmers’ decisions to take-up adaptation measures [
56].
Involvement in cooperatives is positive and significantly (at 10% level) related to adoption of adaptation strategies, implying that the probability of adaptive strategy adoption in higher for those farmers who have connections with different cooperatives enterprises compared to farmers not participating in such coordinated actions and groups. We interpret this observation as an indication that membership and engagement in a cooperative encourages farmers to engage in a united strategies orientation; farmers involved in cooperatives share knowledge and innovation ideas, discuss problems and challenges with others, and engage in collaborative decision-making.
Credit received, training received, and market access coefficients were not statistically significant in the model. In certain ways these results are surprising in light of the rhetoric and theory surrounding their use as development instruments in the general case and as climate change adaptation strategies in general. While there is much that could be said about the sign of the coefficients reported here, we forego such discussion given the statistical insignificance of the terms in the calculated model. Further research with larger data sets may present different results than those here, but the lack of significance in this model creates a clarion call for attention to the influence these stalwarts of rural development policy play in terms of promoting adaptive strategy adoption by farmers.
3.5. Constraints to Adopting Coping Strategies Faced by Farmers
Table 6 summarizes the problems identified by farmers which can hinder or constrain adoption of the climate change coping strategies identified and investigated in this report and this sections discusses the related results. Similar to previous sections, a ranking of the problems was completed using a Problem Confrontation Index (PCI) value as estimated by using the formula (6) below [
57,
58]. Survey respondents were asked to rate their perception of each constraint on a four-point Likert scale ranging from “not encountered” to “high”.
where,
PCI = Problem Confrontation Index
Pn = Frequency of the farmers who rated the problem as not encountered
Pl = Frequency of the farmers who rated the problem as low
Pm = Frequency of the farmers who rate the problem as moderate
Ph = Frequency of the farmers who rated the problem as high
Based on the results of the formula, the problems were listed in rank-order, also presented in
Table 6. The results indicate that “lack of available water (both irrigation and drinking)” ranked first and seems to be the most severe problem of the farmers in the region studied in terms of adoption of climate change adaptation strategies. Agricultural production systems have been highly reliant on water, especially for irrigation, while rivers, rainfall, groundwater, canal water,
etc., have been used as a source of this irrigation water. Available water is hard to manage-either for crop production or drinking water- in the coastal region of Bangladesh due to frequent natural disasters such as floods, droughts, and cyclones. Different GOs, NGOs and even farmers have been working on the water issues.
Table 6.
Rank of the problems faced by the farmers.
Table 6.
Rank of the problems faced by the farmers.
Problems | Degree of Problems | PCI | Rank |
---|
High | Medium | Low | Not at All |
---|
Lack of available water (both irrigation and drinking) | 99 | 1 | 0 | 0 | 299 | 1 |
Shortage of land | 88 | 12 | 0 | 0 | 288 | 2 |
Unpredicted weather | 77 | 20 | 3 | 0 | 274 | 3 |
Lack of credit/money | 55 | 44 | 1 | 0 | 253 | 4 |
Lack of market access | 22 | 56 | 22 | 0 | 200 | 5 |
Lack of farm animals | 13 | 56 | 30 | 1 | 181 | 6 |
Shortage of farm inputs | 11 | 55 | 34 | 0 | 177 | 7 |
Lack of information | 18 | 39 | 41 | 2 | 173 | 8 |
Poor soil fertility | 5 | 23 | 71 | 1 | 132 | 9 |
Insecure property rights | 1 | 29 | 62 | 8 | 123 | 10 |
“Shortage of cultivable land” was ranked as the second most severe problem. With the growing population, cultivable land is increasingly being utilized for non-farm sectors such as housing, construction facilities, and other industrial activities in Bangladesh [
59]. In 1971 (after Independence), the cultivable land was 14.4 million hectares but this has since decreased to 8.44 million hectare [
60]. This may be a result of the increased pressures of population growth, but is also heavily influenced by river bank erosion and landslides because of catastrophic weather events. Land use policy is also one of the most important issues enabling this decrease in cultivable land.