Living with drought: climate change perceptions, adaptation, and mitigation among farmers in rural Bangladesh

The paper presents a new behavioural model that collectively focuses on how the smallholder farmers in the drought-prone Barind Tract of Bangladesh perceive, mitigate, and adapt with the effects of climate change. A survey of 385 farm households was examined with the help of linear and ordinal logistic regression models to point out socioeconomic institutional determinants of these behavioural reactions. Findings indicate that 46 per cent of farmers (n = 177) had a moderate perception of climate change, which was related mostly to lowered soil fertility, water shortage and decreased crop production. Age, education, and income had a significant positive effect on climate awareness (R² = 0.692, p < 0.001), whereas agricultural extension, livestock ownership, and peer-to-peer learning had a positive effect on adaptive and mitigative abilities. Conversely, the lack of financial capacity and lack of access to the markets hindered climate action. The main practices by farmers were crop diversification (79%), soil-water conservation (66%), use of organic fertilisers (78%), and management of residues (69%). The novelty of the study is that it summarises perception, adaptation and mitigation into one analytical model, which exposes the behavioural interrelationships that constitute resilience. The results also highlight the effectiveness of enhancing social learning in networks, financial, and mainstream climate-smart agriculture in supporting low-carbon adaptive food systems in semi-arid areas of South Asia.

1 Introduction

One of the characteristic challenges of the 21st century is anthropogenic climate change, which has an extensive pressure on natural ecosystems and human well-being (Kabir et al., 2023). The agricultural industry is one of the most vulnerable sectors to such changes, as temperatures are rising, the distribution of precipitation is becoming unpredictable, and the number of extreme weather patterns increases directly at the cost of the food production systems (Gomez-Zavaglia et al., 2020). These climatic anomalies also compromise agricultural stability and livelihoods in rain-fed agricultural areas (Aich et al., 2022). Evidence-based research shows that stress factors associated with climate are of particular concern in terms of food security and poverty reduction, especially in developing countries where smallholder enterprises are acting on a small institutional scale with restricted resources (Sartorius and Kirsten, 2007; Branca et al., 2022). Climate change, therefore, not only promotes soil erosion and water stress but also endangers agrobiodiversity in the world (Hailu, 2025).

Consequently, the localized effects of climate change and the associated adaptation mechanisms of agricultural communities need to be comprehended to support resilience. This can be specifically applicable to Bangladesh, which is often mentioned among the most climate-prone countries in the world. On the territory of Bangladesh, the Barind Tract in the northwest is also a special and highly endangered agroecological zone (Khan, 2021). The Barind Tract is also semi-arid with terraced red-brown soils and low water retention capacity (as compared to the rest of the floodplain ecosystems in the country) (Alam and Khan, 2022).

This region has agriculture as the key economic activity, and millions of livelihoods are supported by agriculture (Rana and Moniruzzaman, 2021). There is, however, extreme environmental degradation in the area. Rising temperatures and irregular rainfall patterns have increased soil erosion, and excessive use of groundwater in irrigation has resulted in a rapid decline in aquifers (Alao et al., 2024; Panday et al., 2025). The interaction of drought, soil organic matter, and hydrological stress poses the danger of making the traditional agricultural practice untenable (Stavi et al., 2021; Farooqi et al., 2024).

In this regard, the attitude and behavioural reactions of farmers play a key role. The adoption of Climate-Smart Agriculture (CSA) practices, which may be either crop diversification, soil management, or water conservation, depends primarily on the cognitive framing of the climate risks and the socioeconomic capacity cv a farmer (Autio et al., 2021). Although existing literature has been used to understand the physical climatic change in the Barind Tract (Afrin et al., 2024), more specific investigations into socio-institutional factors that facilitate or restrict the practice of Adaptation and Mitigation at the household level are required.

This paper seeks to fill this gap with the objective of analyzing the interaction between climate change perceptions among the farmers, the adaptive strategies they have adopted, and the mitigation activities they are participating in. Through these behavioural dimensions, the study provides evidence-based policy recommendations to augment climate resilience. Precisely, the research questions covered in the study are the following:

RQ1: What is the perception of farmers in Barind Tract about climate change, especially in terms of water supply, soil quality, and the viability of agriculture?

RQ2 What are the adaptation strategies that are presently implemented, and how does that choice depend on the socioeconomic variables, including education, income, and social capital?

RQ3: How frequently are the farmers utilizing Mitigation (emission reduction) practices, and what factors influence the adoption of these practices?

2 Literature review and conceptual framework

2.1 Climate change and agriculture in Bangladesh

Bangladesh’s agriculture is highly vulnerable to weather variability due to its reliance on monsoon and groundwater irrigation, as well as temperature fluctuations throughout the year (Shahen et al., 2025). Increasing research indicates that climate change poses a significant challenge for crop production, livelihoods in rural areas, and food security (Amoah and Simatele, 2021; Ahmad et al., 2022). For instance, when the temperature rises, the growing season becomes shorter, evaporation and transpiration increase, and crop yields decrease, especially for rice, wheat, and legumes. Climate change has disrupted rainfall patterns and shifted the timing of the monsoon season (Hajek and Knapp, 2022). This has messed up the planting calendar, increased the frequency of droughts and floods, and made agricultural output less stable. The literature examines the relationship between water stress and agricultural sustainability. It has been demonstrated that boro rice production, which relies on water bodies, is threatened by groundwater and aquifer depletion, as well as calcification, which leads to saltwater intrusion (Gaydon et al., 2021). Depending on social and economic disadvantages exacerbated by fragmented land ownership, limited capital, and institutional failures, households face high levels of food insecurity (Ntihinyurwa and De Vries, 2021). Conceptually, climate exposure, sensitivity, and adaptive capability are embedded in either the vulnerability-resilience continuum or the sustainable livelihoods platform. Although none of the publications relate these conceptual frameworks to agroecosystems, the agriculture domain indirectly benefits from the linkages and clarifications reviewed here. It is assured that changing agricultural practices in Bangladesh agroecosystems necessitate a comprehensive and coordinated approach.

2.2 Climate change impacts on agriculture in the Barind Tract of Bangladesh

The Barind Tract, often referred to as the “granary of Bangladesh,” is a climate-stressed agricultural frontier (Parvin, 2020). It is characterized by repeated droughts, a rapidly falling water table, and diminishing soil fertility, all of which interact with climatic precipitation variability in a context of declining food security. Empirical research indicates that increasing mean temperatures and decreasing rainfall have led to reduced yield losses in Boro rice, wheat, and pulses, which are the primary crops in the local cropping system (Wang et al., 2022; Al Mamun et al., 2025). Water is quite scarce right now. Aquifers are being depleted because too much water is being extracted to meet agricultural needs, causing the water table to drop rapidly across the Barind (Hossain et al., 2023). The need for irrigation is likely to continue increasing. Farmers are increasingly finding it challenging to plant on time. Rain has become less predictable, and prolonged dry spells have further lowered soil moisture levels, making plants less able to resist pests (Shukla et al., 2025). Monoculture, loss of organic matter, and pressure have all contributed to the soil becoming less fertile, while climate change has further reduced its productivity (Khan et al., 2024). The expenses of production have increased, and the risks have also risen due to later sales and fertilizer use. These hazards are exacerbated by socioeconomic factors such as small landholdings, limited access to irrigation technologies, and high production costs, which increase the vulnerability of smallholders.

2.3 Adaptation strategies on agriculture in the Barind Tract of Bangladesh

Adaptation strategies for agriculture, tailored to each user, aim to make people less vulnerable and more resilient in the face of climate change, which is becoming increasingly unpredictable (Darjee et al., 2023). Crop diversification, improved irrigation, conservation tillage, and agroforestry are all methods that have worked well around the world in the past (Choden and Ghaley, 2021). It has been thoroughly useful to have these strategies to keep the agricultural system safe in shocks like this. Forget the farmers of old, who all too often have felt compelled to end it all between milkings or at other desperate times. For instance, they grow crops that can withstand heat and tolerate drought, harvest rainwater, and adjust their planting schedules for water-dependent crops based on rainfall amounts, especially in drought-prone regions (Maleki et al., 2025). The farmers in the Barind Tract have begun using some of these methods more frequently, including mixed cropping, additional irrigation, and low-cost ways to protect against soil damage, such as mulching (Kumari et al., 2021). But the farmers do not have the cash to put them to use just yet. It is unlikely that any of these responses could fully protect communities throughout the region. Still, projects run by communities and at farmer field schools to manage water resources offer a way for people to adapt in ways that have everyone learning and getting things done, as dramatic successes in places like Nepal or Africa would suggest (Koirala, 2023). However, the effectiveness of those changes can—at least in part—hinge on factors such as the rules by which they operate, the signals they emit to policymakers, and just how difficult it is to obtain goods and services. Therefore, even if people are working on climate-smart solutions at various levels, it’s essential to implement them in more places to make the Barind Tract more resilient in the long term.

2.4 Mitigation practices on agriculture in the Barind Tract of Bangladesh

Agriculture is a significant source of greenhouse gas emissions, particularly methane from rice cultivation, nitrous oxide from the use of fertilizers, and carbon loss from land-use changes (Sahu and Arya, 2024). Accordingly, pharmaceutical interventions aim to reduce their number; in other words, they minimize the emissions that result rather than the productivity that drives this objective. Elsewhere in the world, conservation tillage, organic Farming, tree crops, and judicious fertilizer application are considered best management practices (De et al., 2021). For example, an innovation known as alternate wetting and drying reduced methane emissions from rice fields by 30% without compromising yield (Dahlgreen and Parr, 2024). Nature-based solutions provide a composite benefit by reducing carbon emissions, restoring soil fertility, and diversifying farmers’ incomes. Mitigation measures are already being mainstreamed into climate-smart agricultural policies in Bangladesh, although their adoption by farmers has been uneven due to high costs and limited awareness (Ali et al., 2024). For instance, in the Barind Tract, a region adjacent to one of the country’s most hydrologically stressed and land-deficient areas, AWD irrigation, organic amendments, and agroforestry are likely to offer significant advantages for Adaptation and Mitigation.

2.5 Existing knowledge limitations and study significance

Despite an abundance of literature on the impacts of climate change and agriculture in Bangladesh, a significant knowledge gap persists regarding the Barind Tract (Daraz et al., 2025). Even the extensions of these studies are primarily concerned with forecasting yield-growth responses rather than long-term soil health dynamics, groundwater sustainability, and ecosystem services (Xing et al., 2024). The same applies to a crop drought-, flood-, or salinity-tolerance threshold, as most research focuses either solely on yield decline or on short-term solutions (Ntanasi et al., 2025). On the other hand, national-scale mitigation studies have primarily focused on rice-based systems, overlooking a significant information gap. At the same time, low-emission intensification measures can be introduced in the Barind, a system with scarce resources (Hossain et al., 2025).

Additionally, there has been little work that pushes the boundaries of climate science, agronomy, and socioeconomics, providing only limited information about how resilient these vulnerable farmers are. While there have been studies utilizing vulnerability frameworks, systemic-based approaches incorporating ecological-social feedback remain heavily underutilized. Therefore, due to the lack of previous studies, this current study has been launched to provide a detailed overview of the impacts of climate change, Adaptation, and mitigation measures on the Barind Tract. This interdisciplinary effort facilitates the crafting of policies and interventions tailored to local agroecological and socioeconomic conditions.

2.6 Conceptual framework

This study’s conceptual framework incorporates two paradigms that are complementary to each other: the Vulnerability-Resilience Framework and the Climate-Smart Agriculture (CSA) methodology. This complex method is aimed at examining the complicated relationship between climate-related stressors and socioeconomic variables and the behavioural responses of the farmers in the drought-prone Barind Tract.

The paradigm is based on the assumption that climate change is a key stressor. This phenomenon comes in the form of increasing temperatures, unpredictable rains, and water deficit, and leads to a cognitive reaction. However, in contrast to standard research, which pays much attention to agronomic consequences (e.g., yield loss) or coping responses in the short run, the framework focuses on the behavioural pathway between perception and action.

As shown in Figure 1, the process starts with perception, which involves how farmers understand the risks of climatic variations depending on how they have experienced them. This thinking is facilitated by socioeconomic aspects, including age, education, income, and social associations among farmers (Figure 1, labelled as the Farmer to Farmer interaction). This farmer-to-farmer interaction is essential, and it helps in the informal spread of the adaptation knowledge among peers.

Figure 1

Figure 1. Conceptual framework of this study.

The model states that high risk perception coupled with sufficient capacity gives rise to two different but complementary results:

Adaptation: Reactive options to deal with short-term threats (e.g., the Adaptation of planting dates, soil preservation).

Mitigation: Preemptive investments to lessen the long-term environmental footprint (e.g., organic manuring, planting trees).

This study goes to a wider scope through the incorporation of Mitigation and Adaptation, as opposed to the traditional emphasis on short-term survival. It states that agroecological resilience should be based not only on stabilizing yields, but on low-emission practices that guarantee long-term agroecological sustainability. Finally, the framework shows that there is a feedback loop whereby effective adaptation and mitigation measures increase the resilience of the Farmer and change the way the Farmer will view risk and capacity in the future.

3 Methodology

3.1 Research design

This article utilizes a quantitative study approach to analyze the effects of climate change on agriculture in the Barind Tract. Structured surveys, field observations, and secondary sources are used to gather information about crop yields, irrigation techniques, soil characteristics, and adjustment methods. Stratified random sampling is used to minimize bias from farmers when questionnaires are given to a representative group of smallholder and medium-scale farmers.

3.2 Study area

The study took place in the Rajshahi District of the Barind Tract, shown in Figure 2, in a semi-arid area due to its unique vulnerability to the effects of climate change. It was chosen because this particular district represents the worst in terms of the environmental issues that the region has to encounter: the soil is mostly red-brown clay, whose qualities are not the best in terms of holding water (Rana and Moniruzzaman, 2021), and there is often an uneven distribution of rain and harsh droughts (Bharambe et al., 2023). Nevertheless, Rajshahi is an important agricultural center, and the farmers there engage in intensive Farming of Boro rice, wheat, maize, and pulses. This high vulnerability (water shortage, soil erosion) and high agricultural reliance combination turns out to be an ideal site for studying the perceptions and adaptation strategies of the farmers in a wide range of farm sizes.

Figure 2

Figure 2. Map showing the research area.

3.3 Sampling and data collection

This study employed a cross-sectional design and utilized a stratified random sample of farm households, drawn from groups with distinct characteristics, including farm size, crop type, and socioeconomic status. The Population of the district of Rajshahi is 291,509 out of the total Population. A sample of 385 farmers was selected using a formula to ensure statistical reliability and representativeness. The sample size (n) is calculated according to Daniel (2009), Equation 1:

$\begin{array}{ll}\mathrm{n}=[\mathrm{z}2\ast \mathrm{p}\ast (1-\mathrm{p})/\mathrm{e}2]/[1+(\mathrm{z}2\ast \mathrm{p}\ast (1-\mathrm{p})/(\mathrm{e}2\ast \mathrm{N})\left)\right]& (1)\end{array}$

Where: z = 1.645 for a confidence level (α) of 95%, p = proportion (expressed as a decimal), N = population size, and e = 5% margin of error.

According to this estimation, a sample size of 385 households was arrived at. To adopt the stratification, the farmers were divided into three specific strata of the classification, according to land ownership, according to the classification rules of the Department of Agricultural Extension (DAE): Small Farmers (0.05–2.49 acres), Medium Farmers (2.50–7.49 acres), Large or Commercial Farmers (7.50 and above) (Dhruw, 2020). The sampling frame (list of farmers) was taken out of the local Upazila Agriculture Office. The respondents were then randomly chosen per stratum in proportion to the size of the stratum to the total Population. This method reduces the selection bias and makes sure that the opinion of both the marginal and commercial farmers is properly reflected.

3.4 Measurement of dependent variables

This study analyzed three primary dependent variables, including: (i) Farmers’ perception of Climate Change Impact; (ii) Adaptation to Climate Change; and (iii) Mitigation of Climate Change. Definitions and measurements of each variable are as follows:

3.4.1 Farmers’ perception of climate change impact

We sought the agreement or disagreement of farmers on seven critical outcomes of climate change: reduction in crop yields, increase in pests and diseases, decrease in water availability, deterioration in soil quality, depletion of infrastructure, livestock welfare, and decline in farm income. Response effects in each dimension were characterized using a 4-point Likert scale as follows: No Impact (1), Low Impact (2), Moderate Impact (3), and High Impact (4). For each Farmer, a composite score across the seven parts was computed by adding all items (ranging from 0 to 21). Farmers were divided into three categories: Low (7–12), Medium (13–16), and High (17–21).

3.4.2 Adaptation to climate change

Adaptation involves employing methods to ensure that farmers are not overly vulnerable to the threats posed by climate change to the system. Farmers were presented with a list of 10 prevalent adaptation methods and asked to indicate whether they had adopted any one of them. Implementation received a code of 1, and non-implementation received a NSDI custom taxonomy code of 0. We constructed a total adaptation index by summing the number of conditions of use, which can range from 0 to 10. The index consists of three levels: Tavares 1, Low (0–5); Tavares 2, Medium (6–7); and High risk (8–10).

3.4.3 Mitigation of climate change

Mitigation involves taking actions to reduce greenhouse gas emissions or increase the amount of carbon that plants and trees can absorb. The farmers were given a list of 10 possible ways to reduce their impact, such as ecological agricultural methods and new technologies. For each practice that was done, a code of 1 was given, and for each practice that wasn’t done, a code of 0 was given. Then, a composite mitigation index was generated by summing up each Farmer’s positive replies, which could range from 0 to 10. It was further split into three groups: Low (0–5), Medium (6–7), and High (8–10).

3.5 Statistical analysis

The choice of the regression models was based on the measurement scale and statistical character of the dependent variables. The perception of farmers regarding climate change was measured as a composite index based on several Likert-scale items and yielded a pseudo-continuous variable that has a normal distribution. In line with previous perception-based climate research, linear regression was then utilized in order to approximate the impact of socioeconomic and institutional variables on perception so that the coefficients and overall model fit can be directly interpreted.

Conversely, the adaptation and mitigation results of the farmers were determined as ordered categorical indices (low, medium, high) depending on the total number of practices adopted. These variables are ordinal results, and linear regression cannot be applied there. To address this, the ordered logistic regression models were estimated to determine the likelihood of farmers shifting among categories of responses ordered, in addition to maintaining the ordinal nature of the data. Such a modelling approach guarantees the rigour of methods and is consistent with the established methodologies in climate adaptation and the adoption of agricultural technologies research.

3.6 Regression diagnostics

Regression diagnostics were conducted to test the assumptions of the model. The Shapiro–Wilk test was used to test the normality of the residuals, and QQ plots were used to determine the normality of the residuals visually. Multicollinearity was investigated using the Variance Inflation Factor (VIF) values, whereby all predictors had VIF values that were below 5, which implies that there is no multicollinearity. The Breusch-Pagan test was used to test heteroscedasticity. Heteroscedasticity-consistent covariance matrices were also calculated when a robust standard error was required.

4 Result

4.1 Demographic, economic, and farming profiles of respondents

As shown in Table 1, a summary of the demographic, economic, and Farming characteristics of the respondents in Barind Tract, Bangladesh, and their linkage to domestic farming methods. Specific comprehension measures indicate that most of the respondents were within the age range of 36 to 50: 60.52% are young farmers aged 17–35, 28.31% are senior farmers aged 50+, and only 11.17% are senior farmers. This points to the mix of an enthusiastic and knowledgeable agricultural population. The majority of men (91.2%) and women (8.8%) indicate a traditional gender pattern among farms in the area. Regarding education, only 48.57 and 51.43% were present at the primary and secondary levels, respectively. Since the continued use of advanced agricultural methods and modern agricultural practices relies considerably on the level of knowledge, this is necessary. The financial status of 64.68% of families is considered insufficient for adaptation efforts to low-income households. Indeed, 2.08 percent of those folk may be characterized as high revenues, which might limit access to inputs, machinery, adaptation resources, and technology. The farm size is 57.66% for medium farms (1.01–3 acres) and 42.34% for large-scale farms (more than 3 acres). More extensive farms can be successful, but undoubtedly have a lower ability to withstand climate variability. Livestock keeping is pursued by 59% of respondents as an additional revenue stream, and 41% for sales activities. 80.3% use agricultural extension dictations; this has the potential to enhance adaptation capacity. 47.8% of them have been allowed in agronomic form; they play a vital role in cultivation, since more than 50% o50% are permitted. More progress is accomplished, and hence professions are achieved than in 20 years of experience, with 41.56 and 45.71% of farmers having 11–19 years of preparation. More knowledgeable agents may make long-term decisions. 93.5 percent of farmers engage in farmer-to-farmer interaction. 18.5 expresses rare, and 25.9 shows market directives.

Table 1

Table 1. Demographic, economic, and farming profiles of respondents in the Barind Tract of Bangladesh.

4.2 Construction of indices for perception, mitigation, and adaptation

A systematic procedure of synthesising qualitative knowledge and qualifying it by quantitative validation was put at the basis of constructing the indices of perception, mitigation, and adaptation. To begin with, ten perception constructs and 15 constructs in the case of mitigation and adaptation were developed based on Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs) with local farmers, agricultural officers, and community leaders. The pre-test survey was used to make sure that the survey was clear and contextually appropriate, and it allowed narrowing down the items. After the pre-test, seven constructs were finalised in the perception, and ten constructs in mitigation and adaptation. This focus made the constructs capture the conceptual framework of the study in a meaningful way and capture locally appropriate dimensions of climate change awareness and response.

To verify the dependability of these scores, the alpha of Cronbach (α\alpha) of each of the constructs was calculated. The alpha of Cronbach is generally known as a check of internal consistency, which shows how far objects in a construct are linked to each other, and to which they measure the same construct (Cronbach, 1951). The indices had reasonable reliability, and alpha coefficients exceeded the generally required 0.70, meaning that social science research has enough internal consistency (Tavakol and Dennick, 2011). These findings confirm that perception, mitigation and adaptation constructs were statistically sound and could be used in further analysis.

4.3 Farmers’ perception of climate change

As shown in Table 2, the farmers’ perceived level of climate change varied significantly. Specifically, 45.98% of respondents had a medium level of perception, 38.44% had a low level, and 15.58% had a high level. In particular, the overall perception score among farmers was 13.46, with a standard deviation of 2.81. These results imply that, on average, farmers were moderately familiar with various climate change phenomena. However, a significant portion of farmers have some understanding that the climate is changing. There is room for improvement in their knowledge of climate change and their adaptive awareness through extension services and information dissemination programs.

Table 2

Table 2. Farmers’ perceived levels of climate change (N = 385).

4.4 Farmers’ perceptions of specific agricultural impacts of climate change

Table 3 shows what farmers in Bangladesh’s Barind Tract think about how climate change would affect Farming. It highlights where climate change has the most significant effects. Regarding the rated impacts, the water supply was the primary concern, with 156 respondents indicating it has a considerable effect and assigning it a substantially higher score than any other (Risk Perception Score [RPS] 849). This highlights how hard it is to manage with rain that comes and goes, long periods of drought, and groundwater levels that drop. All of these things have a direct influence on irrigation, crop development, and Farming. The second most crucial thing is yearly farm income, which 58 people indicated had a significant effect (the RPS was 739). This means that climate change has a substantial impact on the economy, including the loss of animals and crops, as well as reduced productivity. Buildings on farms came in third (RPS = 736). Additionally, 61 people reported that it made a huge difference.

Table 3

Table 3. Perceptions of specific agricultural impacts of climate change (N = 385).

4.5 Levels of adaptation response to climate change among farmers

Table 4 illustrates how farmers in the Barind Tract of Bangladesh manage fluctuations in the weather. It demonstrates how they are adapting their farming practices by planting crops that benefit the environment. 58.4% of people reported being in the “medium adaptability” group. This means that most farmers are adapting their farming practices because the weather is changing. For example, they might change how they cultivate their crops, conserve soil, or use water more efficiently. Only 27.8% said they were not very flexible. They might not have had the right tools, information, or help from their organization to put adequate safeguards in place. Only 13.8% of the respondents demonstrated a high level of adaptability. Simply put, most of the proactive and comprehensive solutions can be utilized by only a small number of farmers. These could include new methods of animal care, improved irrigation systems, integrated pest control, or even growing different crops. The average level of Adaptation is 6.34, and the standard deviation is 1.03, indicating that people are not adopting superficially, but rather with some individual differences. This is likely due to differences in education, farm size, financial resources, access to extension services, or experience. These results suggest that most farmers are making only minor adjustments in response to climate change. But many others remain vulnerable because they cannot adjust. This would seem to indicate that the farming systems here require more assistance, training, and funding to improve and last longer.

Table 4

Table 4. Levels of adaptation response to climate change among farmers (N = 385).

Table 5 highlights the various ways that farmers in Bangladesh’s Barind Tract are adapting to climate change. It illustrates how people deal with changes in their environment. The most popular strategy was cropping variety, which 305 individuals employed and got the highest Adoption Score (AS). This demonstrates that it helps lower the chance of crop failure and ensures there is adequate food. 264 and 262 respondents, respectively, answered that getting help from other farmers and being able to get loans were also highly significant. This illustrates the importance of exchanging money and knowledge to effect change. Soil management and income diversification were used by the fourth and fifth most responders, who were 254 and 251, respectively. This shows that keeping the soil fertile and improving family income are two important ways to keep Farming profitable, even when the weather changes. Changing the timing of crops and preserving water were also moderately popular, with 244 and 241 individuals stating they would do it, respectively. This suggests that certain steps were taken to mitigate the consequences of rain that comes and goes, as well as water scarcity. Access to agroforestry and climate information had lower adoption rates, with 222 and 203 respondents, who placed them eighth and ninth, respectively. This implies that tree-based systems and information services aren’t widely employed in Farming in the region. The tenth most popular job was taking care of animals, which was held by only 196 individuals. This suggests that humans still do not utilize adaptive therapies on animals as frequently as they should, even when they could be effective.

Table 5

Table 5. Adoption frequencies and rankings of climate change adaptation strategies among sampled farmers (N = 385).

4.6 Levels of climate change mitigation responses among farmers

Table 6 illustrates how farmers in the Barind Tract of Bangladesh are addressing climate change by mitigating the environmental impacts of their farming practices. Among the participants responding, 59.22% were in the “medium mitigation” group. However, this means that most farmers are voluntarily trying to reduce greenhouse gas emissions and lessen harm to the environment by using water more efficiently, preserving soil health, controlling how they incorporate crop residue into their soil, and applying fewer herbicides. A significant 27.01% indicated low Mitigation, signifying an absence of skills, knowledge, and capacity to execute effective action, resulting in a vulnerable condition. That makes people and their Farming much more susceptible to climate change. Only 13.77% of those who answered said they were making substantial changes, which suggests that relatively few farmers are adopting environmentally friendly approaches. Some of these methods include better soil and water management, organic fertilization, agroforestry, and the use of sustainable energy. The average mitigation score was 6.35 (1.03 SD), which means that people are somewhat involved in mitigation efforts, although not everyone is. It depends on factors such as the size of the farm, its financial performance, the level of education of the owner, the availability of extension services, and the owner’s own experiences with climate-related events in the past. These results suggest that most farmers are genuinely striving to minimize their environmental impact. But many people are still at risk since the steps being taken to lessen the consequences are not strong enough. This means that the area’s farming methods require additional training, funding, and support from institutions to adapt to climate change and remain sustainable.

Table 6

Table 6. Levels of mitigation response to climate change among farmers (N = 385).

4.7 Adoption of climate change mitigation strategies among farmers

Table 7 illustrates how farmers in Bangladesh’s Barind Tract address climate change. These are the environmental remedies that will help the world last longer and do less damage in the long run. The most common method (n = 301) with the highest Mitigation Score (MS) was the use of organic fertilizer. This also means that farmers would rather use natural inputs and fertilization methods that make the land more fertile, which in turn reduces their need for chemical fertilizers. Two hundred sixty-four participants indicated that managing crop residue was the second-best option. This illustrates the importance of maintaining healthy soil to reduce greenhouse gas emissions. Using nitrogenous fertilizers, carbon sequestration methods, and renewable energy sources were also common. The MS values for these were 255, 254, and 253, which put them in third, fourth, and fifth place. This indicates that farmers are striving to strike a balance between productivity and environmental stewardship. People employed manure management, tillage methods, and adjustments to animal feed to some extent, indicating that practices aimed at nutrient cycling, soil conservation, and livestock efficiency were only partially adopted.

Table 7

Table 7. Adoption of different climate change mitigation strategies by farmers in the Barind Tract of Bangladesh (N = 385).

4.8 Regression diagnostics and model validation

To guarantee the validity and robustness of both the linear and ordinal logistic regressions, a set of diagnostic tests was performed on each of the two regressions. In the case of the linear regression model to evaluate the opinion of farmers towards climate change, the Shapiro–Wilk test (W = 0.9897, p = 0.0086) and visual analysis of the Q-Q plot showed that residual values have an approximate normality, as suggested by Shapiro and Wilk (1965) when testing the normality of data. The Variance Inflation Factor (VIF) was used to test multicollinearity, and all the values were under 5, which is an assurance that there was no collinearity issue among the predictors (Kutner, 2005). Homoscedasticity of residuals was also achieved (Breusch and Pagan, 1979) by the Breusch Pagan test (p > 0.05), which is fundamental in the homoscedasticity of linear regression.

In the ordered logistic regression models of Adaptation and Mitigation, model diagnostics were also done to determine whether the model met the proportional odds assumption. There was some weak evidence to the assumption as indicated by the result of the omnibus test on Adaptation (χ ² = 22.92, 11, p = 0.02) and Mitigation (χ ² = 19.87, 11, p = 0.05). The violations, however, were limited to the Loan variable (p < 0.001), and all other predictors did not violate the assumption (p > 0.05). Financial access is theoretically significant, and therefore, the Loan was kept in the models. In order to check the robustness, generalized ordered logit models were estimated, and findings were identical to the primary ordinal models (see Supplementary Table). The model fit is measured by the pseudo-R² of McFadden (1974) was satisfactory in explaining the power. All of these diagnostics prove that the models are statistically sound and can be interpreted, and they are used in accordance with the econometric criteria (Agresti, 2010; Wolf and Best, 2013).

4.9 Relationship between socioeconomic and farming variables and perceived climate change impact

Table 8 presents the results of the linear regression analysis examining the relationship between independent socioeconomic and Farming variables and the perceived impact of climate change among farmers in the Barind Tract of Bangladesh. The model demonstrates strong explanatory power, with an R² of 0.692 and an adjusted R² of 0.683, indicating that approximately 68% of the variability in climate change impact perceptions can be accounted for by the selected predictors. Age, education, family income, livestock ownership, farmer-to-farmer extension, and farming experience were found to have significant relationships with the impact of climate change. Age (β = 0.254, p < 0.001) and education (β = 0.118, p = 0.018) positively influenced farmers’ opinions, indicating that older and more educated farmers are more cognisant of climate-related changes and their impacts. A higher family income (β = 0.542, p < 0.001) and owning livestock (β = 0.190, p < 0.001) also led people to believe the effects were worse. This suggests that farmers with more resources or a broader range of assets are more aware of climate-related threats. Likewise, participation in farmer-to-farmer extension (β = 0.132, p < 0.001) and extended farming experience (β = −0.266, p < 0.001) significantly affected perceptions, underscoring the importance of social learning and experiential knowledge. In contrast, gender, farm size, and access to agricultural extension services were not statistically significant, while access to loans (β = −0.127, p = 0.002) and market access (β = −0.183, p = 0.001) had negative associations, suggesting that financial support and market integration may buffer the perceived severity of climate impacts.

Table 8

Table 8. Relationship between independent variables and climate change perception in the Barind Tract of Bangladesh (N = 385).

4.10 Relationship between socioeconomic and farming variables and farmers’ adaptation to climate change

Table 9 summarizes the outcomes of the ordinal logistic regression on factors affecting the characteristics of farmers’ average Adaptation in the Barind Tract of Bangladesh. According to the table, it is evident that multiple socioeconomic and institutional factors result in significant shifts in the dependent variable. The regression model fits the data correctly, with a Residual Deviance of 484.91 and an AIC of 512.91, indicating that the incorporated independent variables explain a significant proportion of the variation in Adaptation among farmers. Age, education, and family income were significant socioeconomic factors. Age and its coefficient of 0.1042 with a p-value of 0.0066 were positively associated with Adaptation. Similarly, education, with a coefficient of 0.2110 and a p-value of 0.0098, was also significant. Finally, family income, with a coefficient of 0.4066 and a p-value of 0.0001, was a significant socioeconomic factor. Age and family income reflect greater accumulated experience and a higher potential for investment in adaptive measures, which can contribute to a positive relationship. Younger people with less experience and those from poorer backgrounds with limited resources are hence less likely to adapt. The negative relationship between education and awareness of the existence and potential of adaptive technologies among farmers in the study area clearly indicates that their understanding of these technologies is insufficient. Among institutional and social support factors, access to agricultural extension and its coefficient 1.9799 with p-value 0.0006, Farmer to Farmer and its coefficient 3.4572 with p-value 0.0004, and livestock income and its coefficient 1.7897 with p-value < 0.001, were positively related to Adaptation. These findings highlight the higher demand for information, the ability to learn from one another in a non-direct teaching environment, and the adverse conditions observed in cases. It had a significant positive effect on the ability to adapt. In reality, a better life means one is more self-sufficient and more able and willing to invest in such measures.

Table 9

Table 9. Relationship between independent variables and climate change adaptation in the Barind Tract of Bangladesh (N = 385).

On the other hand, farming experience and its coefficient (−0.1019, p-value 0.036), access to loans and its coefficient (−0.9043, p-value 0.0062), and market access were negatively related. In contrast, market access, with a coefficient of −1.8872 (p-value < 0.001), was significantly associated with factors attributed to Adaptation. Family experiences may result in adaptation strategies that are insufficient to address the new weather conditions, as they may lack information about the essential human responsibility of farmers to adjust to climate change or may establish other barriers. With access to loans, farmers may become indebted and are forced to proceed slowly, and cannot afford to invest in modern, adaptive technologies, including irrigation systems, drought-tolerant plants, soil conservation, and other best practices. The fact that cash-generating animals and the ability to breed will allow agriculturists to start irrigation systems, drought-tolerant plants, and other initiatives will enable smallholder farmers to do so easily during the dry season, which is typically the agricultural period. Market access is the most apparent limiting factor. Lack of infrastructure and commuting are frequently seen as limiting farmers’ capacity to acquire the goods necessary for cultivation and access to information in the modern agricultural process.

Additionally, gender and farm size were not significantly associated with characteristics of farmers’ Adaptation, as indicated by gender and farm size. They had coefficients of −0.2493 and 0.5882, and p-values of 0.5864 and 0.2387. Summarily, the findings suggest that education, income, livestock income, access to extension services, and non-formal research reports have a positive impact on a farmer’s ability to adapt to climate change. In contrast, liquidity conditions and market access have a limiting effect. This study emphasizes the importance of enhancing extension services, access to marketing, lending, and sharing information and learning from neighbours in the agricultural sector. Extension to create climate adaptation.

4.11 Relationship between socioeconomic and farming variables and farmers’ mitigation responses to climate change

The results of the ordered logistic regression analysis presented in Table 10 below shed light on the socioeconomic, institutional, and experiential drivers of farmers’ adaptation and mitigation practices in the context of climate change in the Barind region of Bangladesh. The goodness-of-fit statistics fell within an acceptable pattern, with Residual deviance = 484.91 and AIC = 512.91 for Adaptation, and Residual deviance = 587.53 and AIC = 619.53 for Mitigation. Thus, it shows that the predicting variables have captured a substantial amount of variation, and the adaptation and mitigation strategies are a function of various personal, financial, and institutionalized forces. Turning to the socioeconomic factors, age, education, and family income were significant drivers of both Adaptation and Mitigation. The adaptation and mitigation models had positive and significant age coefficients of 0.1042 and 0.0807, respectively. It suggests that older farmers possess more agricultural knowledge and are better equipped to manage and adapt to the risks posed by climate change. Education had positive effects on the adaptive and mitigative concurrent models, with respective coefficients of 0.2110 and 0.1733. This also suggests that educated farmers are better off purchasing and translating useful knowledge on new technologies and practices. Family income was a strong positive correlate in both the adaptation and mitigation models. The adaptation trust value was 1.4066, and the mitigation reliability trust values were 1.2997. This suggests that wealthy families have more room to adopt new technologies and ideas, making them less vulnerable or formidable.

Table 10

Table 10. Relationship between independent variables and climate change mitigation in the Barind Tract of Bangladesh (N = 385).

Institutional/societal-level factors deeply influenced the adaptive and mitigative process. The utilization of agricultural extension services promotes adaptability and mitigates to a lesser but substantial extent. This subsection highlights the importance of extension agents and groups in educating individuals on climate-smart practices and inducing behavioural change. Farmer-to-Farmer significantly enhanced Adaptation and had a somewhat beneficial impact on Mitigation. The statistics indicate that social learning and peer influence significantly contribute to the dissemination of climate knowledge and actions. Cattle were essential for both Adaptation and Mitigation. Livestock serves both as a valuable resource and a technique for addressing climate change, therefore enhancing the resilience of households.

Conversely, certain factors had significant adverse impacts, indicating that Adaptation and Mitigation were constrained. Access to loans harmed Adaptation and Mitigation. Farmers may experience financial stress from borrowing, which could prevent them from taking risks and hence restrict their ability to invest in adaptive or mitigative innovation. Similarly, access to markets had a significant detrimental impact on Adaptation and Mitigation. Farmers cannot access inputs, information, or sales due to market restrictions; therefore, they are unable to generate new ideas. Farming experience was adversely correlated with adaptability; however, this correlation was not statistically significant for Mitigation. This finding may suggest that seasoned farmers are more reliant on conventional methods and less inclined to adopt innovative concepts.

Conversely, gender and farm size showed no statistical significance in either model. This indicates that adaptation and mitigation actions in the study show no statistically significant differences based on gender and landholding within the study area. The same vulnerability situation may be attributed to equivalent climate exposure and comparable community-level susceptibility in the Barind Tract. In general, both ICCA and each other state component CCAs are shaped by socioeconomic and institutional state factors. Education, income, access to extension services, social connections, and ownership of domestic animals are all made easier.

On the other hand, access to markets, unpaid debts, and outdated and innovative approaches to doing things are all problems. Policymakers suggested general steps that could lead to long-term institutional empowerment through reforms that encourage agricultural growth, the creation of new farms, improved access to markets, and enhanced financial stability for farmers. ICCA promotes farming-by-farming training and root-building in the Barind Tract of Bangladesh to help farmers create skill-dependent institutions that make them more resilient over time.

Regression diagnostics were also done to ensure the adaptation model is statistically valid and robust. The normality assumption was satisfied by the Shapiro–Wilk (p > 0.05) and the Q-Q test since the distribution of the residues was found to be approximately normal. According to Rietveld and van Hout (2015) and Cheah et al. (2021), small deviations of normality are reasonable in large samples since the Shapiro–Wilk test is also excessively sensitive to sample sizes, and the test does not significantly alter the reliability of regression in the cases when the plots of the residuals exhibit near-normal behaviour.

To test the possibility of the multicollinearity of the explanatory variables, the Variance Inflation Factor (VIF) was calculated, and all the values were below the critical value of 5. This means that all the predictor variables did not appear to be overly correlated, and they all made their own unique contribution to the model, as suggested by Salihu et al. (2015). The Breusch-Pagan test for homoscedasticity (p > 0.05) was conducted; the findings showed that constant variance could be observed among the fitted values of residuals. This result meets one of the basic assumptions of a linear regression and shows that the model is not affected by heteroscedasticity (Field and Wilcox, 2017). In total, all these diagnostic findings confirm the statistical soundness and reliability of the adaptation model’s estimates.

5 Discussion

This research shows that farmers in the Barind Tract, which is prone to droughts, have an intermediate degree of awareness of climate change. Still, their conversion of such awareness to more sophisticated Adaptation and mitigation strategies is limited. Most of the interviewees were right about the key environmental changes, namely, a decrease in the water supply, diminished soil fertility, and agricultural returns. These results are in line with recent research by Anjum et al. (2024), who found that farmers in northwestern Bangladesh are very conscious of irregular rain and changes in temperature. The increased anxiety about water shortage also supports the study of Islam (2024), who found that the dwindling water tables are the leading factor of climate risk perception in semi-arid farming areas.

Socioeconomic research has shown that major factors influencing climate consciousness are age, education, income, and livestock ownership. Farmers who were older, more educated exhibited greater insight into climatic processes, possibly because of the experience of their lives and their greater access to information. This helps back the claim by Ganeshan et al. (2018) that cognitive ability can be increased with education to handle environmental information. In the same fashion, more well-off farmers were more prone to awareness and action and had the financial resources required to invest in adaptive technologies.

An intermediate score (13–16) on the 21-point scale indicates that a majority of the farmers know that there are visible changes in the local weather patterns, such as increased temperature, delayed start of the rainy season, and shorter rainy seasons, but might not necessarily relate the changes to the overall phenomenon of climate change. This is an expression of practical interpretation of the variability of climate through personal experience as opposed to scientific or policy awareness.

With respect to Adaptation, we find that although response mechanisms are extensive, they are mostly autonomous and incremental as opposed to being transformative. About half of the households surveyed have altered the pattern of crop planting, implemented new varieties, or taken some simple steps of soil conservation. These measures are the no-regret adaptation options, i.e., those that are beneficial no matter how severe the climate is. The strategy was most dominant, crop diversification, which is also reported in analogous agroecological environments in Nepal and India, where diversification has been a buffer to the complete failure of crops (Alomia-Hinojosa et al., 2020). Nonetheless, the data may indicate that even with a strong desire to make changes, farmers are not encouraged to use complex and capital-intensive technologies because of financial and technical limitations.

This research is very much in line with the Climate-Smart Agriculture (CSA) framework, which focuses on the co-existence of productivity, Adaptation, and Mitigation in the agroecosystems that are susceptible to climate variability. Our findings show that farmers in the Barind Tract are mostly pursuing autonomous low-cost modes of adaptation approaches, including crop diversification and soil management, which represent an incremental but not transformative form of adaptation pathway. The trend is not new, given that the literature on vulnerability presumes that smallholder farmers in resource-constrained settings are likely to focus on short-term risk buffering over long-term structural change (Sithole et al., 2025).

The considerable influence of income, livestock ownership, extension services, and farmer-to-farmer networks as the vulnerability-resilience factors in this perspective underlines the importance of the adaptive capacity in mediating climate responses. These results are consistent with regional data on South Asia, suggesting that the adaptation consequences are not only influenced by exposure to climatic stress but also by the institutional access, social learning and possession of assets. Notably, the concomitant study of adaptation and mitigation behaviours is an expansion of current regional research that usually considers those dimensions independently. Our findings indicate that CSA-compatible interventions, especially those that combine extension services, social learning processes, and accessibility to productive resources, can mitigate vulnerability and allow low-emission agricultural changes in drought-prone areas like northwestern Bangladesh.

The regression analysis proved that the socioeconomic factors affecting Adaptation also result in mitigation intentions. Access to extension services and financial capital became some of the most important enablers. This justifies the results that institutional support is a requirement in the adoption of emission-reducing practices. Nonetheless, according to Xie et al. (2019), the small-scale farmers in the developing world are usually constrained structurally, and, therefore, sustainable intensification is challenging. The relationship between Perception, Mitigation, and Adaptation that is seen in this study justifies the combination of the Climate-Smart Agriculture (CSA) framework.

A key and theoretically significant implication of the given research study is the statistically significant negative correlation between farming experience and climate change Perception and adaptation behaviour (p = 0.036). Although experience in Farming is thought to be a positive factor in improving adaptive capacity, our findings indicate that long-term farming practice can, in the context of fast climatic change, produce cognitive lock-in and path dependency, which limit behaviour change. Cognitive lock-in is the habit of individuals to stick to the old mental frames, short-cuts, and historical success stories, although the surrounding circumstances have radically transformed. In Farming, this is exemplified by old-fashioned farmers still believing in using practices that have worked in the past. Still, as the seasons advance, the practices have proven to be no longer climatically sound.

Despite the existence of formal access to credit, the negative relationship shows that those farmers who have already accumulated loans have less probability of investing in adaptation or mitigation costs because of financial stress and repayment. Therefore, it is the indebtedness and not the credit shortage that is the crucial limitation to resilience.

This observation is consistent with the path dependency theory that posits that continued reinforcement of the historical strategies causes resistance to innovation because of sunk costs, habituation, and perceived risk to abandon established practice. Several empirical studies have demonstrated that more experienced farmers tend to prefer traditional crop calendars, irrigation systems, and input systems and are less receptive to climate-smart technologies, including drought-tolerant varieties, water-saving technology, or diversified farming institutions (Khatri-Chhetri et al., 2017; Mwongera et al., 2017). Youthful or less-experienced farmers, in their turn, are more prone to displaying a greater cognitive flexibility, being more open to external information, and more inclined towards using extension services, as well as peer learning networks.

This is especially applicable in the situation of the Barind Tract. The experienced farmers have been able to cope with production in the patterns of climatic conditions that have not been experienced before, which supports the belief in experience. Nevertheless, the increased pace at which droughts are occurring, the depletion of groundwater tables, and the fluctuation in rainfall have shifted the agroecological base, making experience-based decisions ineffective on their own. Consequently, the use of previous strategies can have the contrary effect of making them more vulnerable and not resilient. This can be used to explain the impact of farming experience on both Perception and Adaptation in our models, which is negative, contrary to the traditional view of a positive correlation with the competence of agriculture.

The results highlight the importance of specific extension policy interventions that are far more explicit about cognitive lock-in among the experienced farmers. This can be mitigated through participatory learning, demonstration plots and peer-to-peer knowledge exchange to bridge the gap between experiential knowledge and climate science so that seasoned farmers can redefine their experience in a climate change prism. In the absence of such interventions, path-dependent decision-making is likely to slow the pace at which the climate-smart agricultural practices are being diffused in the drought-harvesting areas like northwestern Bangladesh.

Interestingly, the study identified an interesting interaction between interest in farming experience; the hypothesis is that experienced farmers have beneficial traditional knowledge, but are not as likely to appeal to new technologies as young, more educated farmers. This implies that previous experience is no longer adequate to guide the unparalleled climatic changes.

To sum it up, farmers in the Barind Tract are sensitive to changes in the environment; however, they are not able to adapt to them as their structures. The gap between Perception and effective action needs to be filled by a multi-pronged strategy. These are financial inclusion models that are climate-specific investments, enhanced extension services focusing on social learning, and policy changes that decrease transaction costs to smallholders. It is not enough to depend on autonomous Adaptation, and to make agriculture in the Barind Tract sustainable, the institutions must coordinate and mainstream the principles of CSA into the national agenda.

6 Conclusions and recommendations

6.1 Conclusion

This paper presents a thorough discussion of the perceptions and reactions of farmers in drought-prone Barind Tract towards climate change. The empirical evidence shows that a moderate level of climate change perception is held by 46 percent of farmers, and most of them have correctly pointed out water scarcity and soil degradation as the major threats to their livelihood. The regression provides important information that is directly reflected in policy interventions:

Financial Barriers: The authors have discovered that the household income is statistically significant as a determinant of the Perception and capacity to adapt (0.542, p < 0.001). Such a high correlation shows that the major obstacle in the implementation of modern technologies is the lack of financial resources. This empirical observation is a direct indication of the policy suggestion to introduce low-interest agricultural credit schemes and green micro-finance products that will enable resource-poor farmers.

The Part of Education and Networks: Education and ‘Farmer-to-Farmer’ interactions were found to be the major sources of adaptation behaviour. The farmers who have more connections in society were found to be more likely to implement mitigation measures. This observation highlights the need to stop focusing on the past offering of top-down extension services and take a step forward to promote community-based social learning and peer-to-peer knowledge exchange platforms.

Adaptation vs. Mitigation: Although the concept of Adaptation, like crop diversification, is a common practice (autonomous Adaptation), mitigation practices are not utilized fully because of a lack of technical expertise. To sum up, the resilience of the Barind Tract does not only rely on awareness but also on the availability of resources. In this region, there is no way Climate-Smart Agriculture (CSA) could be mainstreamed without tackling those structural financial barriers listed in this research. This is why it is necessary to integrate a policy tool that combines financial inclusion (credit), institutional support (extension), and infrastructural investment (water storage) to transform the Perception of farmers into concrete climate action.

6.2 Policy recommendations

Based on the foregoing conclusions, this study offers the government, non-governmental organizations, and donor agencies evidence-based policy recommendations to enhance the resilience of farmers in Bangladesh’s drought-prone regions to climate change.

Implementation of Union-level Digital Centers (UDCs), which have localized weather prediction devices capable of alerting farmers of heatwaves or dry spells 3–5 days lead time, via SMS in their local dialect. Farmers need to know what is happening before they can take action. Farmers need to learn more about the dangers of climate change and how to address them, as they are often unaware of the risks. Government agencies, NGOs, and research institutions should provide climate information services in multiple indigenous languages. These services should have systems in place for early warning and seasonal forecasting. Climate change education should be an integral part of agricultural extension programs and community-based farmer discovery schools, enabling awareness to translate into action. Radio, a mobile-based advisory platform, and rural ICT centers may help people access climate information quickly.

Extension services that help many individuals. These functions have a significant impact on adaptation and mitigation approaches. There is a need to sensitize extension workers and effect institutional arrangements. Changes in permits and the adoption of new technology will also impact restoration. There should be in-service training of extension staff based on the CSA idea. That means they will need to have skills in managing both water and soil. During this phase of incremental analysis, the voice of other farmers is also essential. There should be closer coordination between universities and extension services. Joint work between universities, extension agencies, and research institutions increases the applicability of findings in the real world.

It is easier to obtain climate funding and low-interest loans. Even in times of scarce resources, they represent essential challenges for Adaptation and Mitigation. Policymakers can collaborate with rural banks and microfinance institutions to develop loan products that support individuals in adapting to climate change. Such projects might be able to offer low-interest loans or subsidies to help people grow drought-resilient crops, build efficient irrigation systems, and switch to renewable sources of energy. Enabling such incentive-based schemes to operate at a higher and more effective level, such as subsidizing organic fertilizers, water-saving techniques, and crop insurance, could bring about many benefits. Government-backed microcredit programs could also incorporate climate risk assessment into their evaluation of an individual’s creditworthiness. This helps to ensure that the smallholders who need funds most receive them.

Due to the extreme depletion of groundwater in the Barind Tract, the policy should give priority to the conservation of surface water by re-excavation of abandoned ponds and canals (khals). Boro rice cultivation should be compelled to adopt Alternate Wetting and Drying (AWD) to cut on water consumption by up to 30%. The primary challenge of the Barind Tract is water scarcity; therefore, groundwater management and rainwater harvesting must be prioritized. The government must provide heavy support for efficient irrigation technologies, such as AWD, drip irrigation, and community-managed water harvesting systems, for paddy cultivation. The best way to manage water in an area is to provide observation on how much groundwater has been tapped and spearhead functions. Improving watersheds and maintaining trees would help make environments stronger or more resilient.

Short-term and drought-resistant rice varieties that can be cultivated before the peak drought season, like BRRI dhan56, BRRI dhan66, and BRRI dhan71, should be promoted specifically by the extension services. In addition, shade-loving crops such as turmeric or ginger should be intercropped with the mango orchards to optimize land use, as well as economic resilience. The evidence suggests that farms are more flexible and less likely to shed labour as they grow when they propagate a variety of crops and animals. Mixed farms that combine crops, dairy animals, educational techniques, and agroforestry would be beneficial for both the environment and income, as they’d even out incomes, help new plants establish, and store carbon. If there were more community seed banks and seed banks for home use, it would be easier for drought-resistant crops to cope with heavy rain.

Make it easier for people to reach markets and perhaps improve infrastructure. Smallholders cannot be nimble when they have little market leverage and high business costs. Farm-to-market roads, cold storage facilities, and local trading centres are some of the types of rural spending that could help farmers access the tools they need and a market for their crop, as well as learn how to work with new technologies. Public-private partnerships can help build value chains for climate-resilient commodities that are inclusive of everyone. Policies elsewhere can also benefit digital agricultural networks that link their members — such as farmers to buyers and sellers — by informing them about prices and keeping them abreast of the weather.

Both adaptation and mitigation approaches should be part of the national agricultural strategy. This paper demonstrates that Adaptation and Mitigation can be combined as the most inarguable pair of terms, even though they are often perceived as two separate concepts. Bangladesh’s overall policy on agriculture and climate change should incorporate the Climate-Smart Agriculture (CSA) approach. There are many low-emissions solutions, including organic fertilizers, waste treatment options, and renewable energy production, which can improve productivity and sustainability. Policy makers could also consider carbon credit programs that reward farmers for developing a plan to reduce their emissions. This would enable district governments to adjust their approach in response to real-time information.

Promote inter-enterprise cooperation and speed up decision-making. There is a need to foster collaboration on climate policies among ministries, district authorities, and community organizations at different levels. For this, regional climate resilience committees should bring about” consistency in policies and their integrated implementation across all regions. These committees need to be inclusive; the voices of farmers and local stakeholders have to play a role in their deliberations. There must be more effective data collection in the district to enable policy changes based on that data.

Foster connections between research, policy, and practice. Diversity in School Policies and Research conducted at Schools is substantial. Policymakers need to enhance their collaboration with universities, think tanks, and agricultural research organizations to gain crucial knowledge about drought management, soil health retention, and carbon sequestration. The scientific approach needs to be transdisciplinary, involving concepts from agronomy, hydrology, and even socioeconomics and behavioural science. Through this approach, modifications will be technically correct, value-acceptable, and cost-effective. Findings from the research have shown that farmers of the Barind Tract are not merely recipients of change. Realizing this, they are desirous of change but must contend against formidable ecological and economic barriers.

The system needs help with that. Policy should consider climate adaptation and Mitigation as elements of agricultural potential. By combining various policy initiatives, including the dissemination of information, financial investment, and institutional changes, Bangladesh has the potential to transform its drought-prone areas into showcases of climate-resilient, low-carbon agriculture.

Data availability statement

The datasets presented in this article are not readily available due to ethical and privacy considerations. Although the data have been anonymized, there remains a potential risk of identifying individual respondents. Therefore, the data cannot be shared publicly. Summary statistics or further details are available from the corresponding author upon reasonable request. Requests to access the datasets should be directed to AP, a3BAcnUuYWMuYmQ=.

Ethics statement

The studies involving humans were approved by Departmental Ethics Review Committee (DERC) of the Department of Agronomy and Agricultural Extension, University of Rajshahi, Bangladesh. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MM: Writing – original draft, Conceptualization, Data curation, Formal analysis, Investigation, Visualization. AP: Conceptualization, Formal analysis, Methodology, Software, Validation, Writing – original draft. AI: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. SS: Data curation, Investigation, Writing – review & editing. MN: Investigation, Resources, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fclim.2026.1744133/full#supplementary-material

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