Understanding climate vulnerability and economic impacts on rural livelihoods: lessons from Narowal, Punjab

1 Introduction

Climate change and its increasing variability have become major environmental and social challenges, driving a rapid rise in climate–induced hazards and their destructive impacts worldwide (Rehman et al., 2024). Worldwide, 1.57 billion acreages of land are covered by crop production (Fuglie et al., 2024). The agricultural system provides livelihood to 3.83 billion people worldwide (FAO, 2023) and shares 26% of gross domestic production in the economy of many developing countries. In many developing countries, where poverty is widespread and agriculture remains the primary source of livelihood, climate variability poses a critical challenge and has become a

major focus of research and policy attention (Huong et al., 2019; Shahzad et al., 2019a). In agriculture sector, among all the natural events, climate related natural events like floods and droughts have the most devastating effects (Tran et al., 2022). In the last 30 years, an estimated USD 3.8 trillion in crops and livestock production has been lost due to disasters (FAO, 2023a). This shows that on average disasters have caused loss of USD 123 billion annually, which makes up of 5% of the annual global agricultural gross domestic product. According to recent global studies, climate change is a threat to human wellbeing and planetary health, with higher risk and projected adverse impacts due to incremental global warming (IPCC, 2023; Khan et al., 2022; Shahzad, 2025). The intense droughts and high temperature are expected to lower water availability by 19%, further causing up to 30% decrease in food crops by 2050 (Aryal et al., 2020).

Climate change combined with economic shocks has become essential factor that extremely affect the food unavailability issues (Shahzad, 2025). The State of Food Security and Nutrition in the World Report highlights that 2.4 billion people experienced food insecurity of moderate or severe levels while 900 million people faced severe levels of food insecurity (FAO, 2023b). Due to changing climate, the earth's temperature continues to increase, thus increasing the likelihood of disasters like floods, heat and cold waves (Shahzad, 2024). (Hultgren et al. (2025) show that climate change continues to significantly reduce global agricultural productivity, with cross–continental analysis underscores the urgency for crop–level and region–specific adaptation evidence (Rehman et al., 2015). It is recognized that low production of food crops is due to un-accessibility and non- availability of technical and latest knowledge required to adapt to cope with climate variability Dorji and Morrison-Saunders, 2026). In addition, climate variability also affects other sources of livelihoods such as animals raised on farms, rangelands, fruits, vegetables and horticulture. Vulnerability in terms of land productivity is reflected through indicators such as farm intensity, crop yield, soil fertility, and farmland location (Field and Barros, 2014). These factors are further influenced by alterations in weather patterns, including sudden flooding events, temperature fluctuations, and erratic rainfall (Teshome, 2016; Shahzad et al., 2019b).

Across South Asia, rising temperatures, erratic rainfall, and CO₂ emissions are undermining food security and agricultural output (Behera, 2024; Shahzad et al., 2024). A recent regional assessment projects a 7.5% drop in agricultural output by 2050 and highlights key adaptation priorities, including irrigation modernization and expanded digital climate–information services for farmers (Gbadegesin et al., 2026).

A recent south Asian climatic review highlighted 60% reduction in wheat yield in Bangladesh; 20%−25% cotton loss in India, and situating Pakistan within a broader region of high agricultural vulnerability (Shahzad et al., 2024; Thakur and Bajagain, 2019). Pakistan is experiencing pronounced impacts of climate change, evidenced by rising trends in hot days and nights, increased frequency of cold nights, and more frequent extreme precipitation events (Nida et al., 2025).

According to Pakistan Economic Survey (2023–24), agriculture sector is crucial to the country's economic growth, food security, generation of employment and particularly poverty reduction at farm level. It contributes 24% to the gross domestic production and provides 37.4% employment to the labor force. Moreover, 65%−70% of the population depends on agriculture for their income source (Pakistan Economic Survey, 2024). Agriculture sector in Pakistan is dominated by the production of cash crops like, rice, wheat, maize and sugarcane, which majorly contributes to the overall performance of agriculture sector. According to Government of Pakistan 2019, the country's average temperature has increased by 0.6 °C during past decade (GOP, 2019). Pakistan has experienced over USD 3.7 billion loss in agriculture and livestock sector due to floods of 2022 (World Bank, 2022). Additionally, due to water shortages, agriculture sector is adversely affected in terms of maintaining growth (0.85%), leading to poverty and food unavailability in farm households (Shahzad et al., 2024; Pakistan Bureau of Statistics, 2025; Riaz et al., 2026; OCHA, 2019). Climate–driven declines in fodder quality and quantity are also undermining livestock productivity, weakening rural livelihoods, and posing a growing threat to national food security (Dogar et al., 2025). National Nutritional Survey reveals that 36.9% of households in Pakistan faced “food insecurity,” with 18.3% experiencing severe levels of it (UNICEF, 2018). In Pakistan, like other developing countries, climate simulation models have forecasted the decline of rice production from 15.2 to 17.2% by the year 2040–2069 (Ahmad et al., 2015).

Understanding food security under climate change requires an examination of household-level vulnerability, as these factors determine the capacity to cope with water stress and agricultural risks (Anwar et al., 2020). Vulnerability assessments in agricultural households are commonly conducted using indicator-based approaches (Tate, 2012; Williams et al., 2018). While widely applied, these methods often fail to capture variations in vulnerability (Vincent, 2007; Bennett et al., 2016). Vulnerability is a multidimensional concept that varies across spatial and temporal scales, influenced by socio-economic, environmental, and geographic factors (Füssel and Klein, 2006). Indicator-based approaches help identify and prioritize vulnerable regions and populations, but challenges remain regarding weighting, data accuracy, and conceptual framing (Birkmann, 2006; Shukla et al., 2021). Frameworks such as the Sustainable Livelihood Approach (SLA), which considers human, physical, financial, natural, and social capital, have been used to develop climate vulnerability indices (Shahzad et al., 2019c; Ullah et al., 2021). In Pakistan, limited studies assess vulnerability and adaptation at the household level, with existing research primarily focusing on climate impacts on agricultural productivity (Ahmad and Ma, 2020; Khan et al., 2021, 2024; Faisal et al., 2021).

This study is novel because it addresses a clear research gap: despite the central role of Punjab's rice–wheat system in national food security, farm–level vulnerability assessments that integrate socioeconomic, livelihood, and climate-related factors remain scarce. By examining how these determinants jointly shape household exposure, sensitivity, and adaptive capacity, the study makes an important contribution to the empirical literature, offering a more nuanced and context-specific understanding of vulnerability than existing, more aggregated analyses. Its findings carry strong significance, as they provide actionable insights into how agricultural households navigate climate variability, thereby informing more targeted, equitable, and effective adaptation interventions in one of Pakistan's most climate-sensitive agricultural regions.

To achieve this, we employ the Intergovernmental Panel on Climate Change (IPCC) framework—encompassing exposure, sensitivity, and adaptive capacity—within a Climate Vulnerability Index (CVI) model, integrated with the Analytical Hierarchy Process (AHP). This combined approach enables a comprehensive assessment of agricultural households' vulnerability and adaptive responses. The primary objectives of this study are, first, to explicitly assess the extent to which changing meteorological patterns—such as shifts in temperature, rainfall, and extreme weather events—have affected the livelihoods, production systems, and overall wellbeing of farm households; and second, to systematically examine the range of adaptation strategies adopted by rice–wheat farmers in Punjab, Pakistan, in response to these evolving climatic conditions.

1.1 Conceptual framework of climate vulnerability assessment and adaptation measures in agricultural households

The conceptual framework for climate vulnerability assessment and adaptation in agricultural households of the current study is grounded in the Intergovernmental Panel on Climate Change (IPCC) approach, which identifies three key components of vulnerability: exposure, sensitivity, and adaptive capacity (IPCC, 2007) (Figure 1, Table 1). Vulnerability, as defined by the IPCC Climate Change (2014), refers to the degree to which a system is unable to cope with the adverse effects of climate variability and extremes. It is shaped by multiple socioeconomic and environmental factors, including fragile economic growth, high dependence on agriculture, rapid population growth, urbanization, weak policy decisions, low literacy rates, gender inequality, water scarcity, land degradation, and declining forest cover (Ahmed et al., 2021; Mekonen and Berlie, 2021; Abid et al., 2016). In this study, vulnerability is assessed across climatic, biological, and biophysical dimensions (Hinkel, 2011). Climatic factors include temperature and rainfall variability; biological factors encompass pest infestations and plant diseases; and biophysical factors involve riverbank erosion, soil fertility loss, floods, and droughts. Sensitivity reflects the extent to which farming systems are affected by climate stimuli, either directly—such as changes in crop yield due to temperature and rainfall variation—or indirectly, through impacts like soil degradation and resource scarcity (IPCC, 2018). Farm-level sensitivity includes lack of equipment, limited land for cereal cultivation, soil fertility loss, seed shortages, and inadequate fertilizer availability, as well as livestock-related constraints. Adaptation, on the other hand, refers to the capacity to minimize yield losses and capitalize on favorable climatic conditions (Adams, 1999), and depends on regional, local, and community-level capacities (Maddison, 2007; Gbetibouo, 2009; Gbetibouo et al., 2010). Adaptive capacity is evaluated through five capital assets: natural (land availability and soil fertility), physical (market access, livestock ownership, irrigation type), financial (credit access, farm insurance, income sources), human (education, farming experience, health access), and social (community support and organizational membership). This integrated framework allows for the inclusion of both qualitative and quantitative indicators across multiple dimensions—climatic, biophysical, social, financial, human, and natural—providing a comprehensive basis for assessing resilience and adaptation strategies (Datta and Behera, 2022; Fahad et al., 2020; Naqvi et al., 2020).

Table 1. Criteria indicators of climate change risks in Tehsil Narowal and Shakargarh.

2 Methodology

This study assesses farmers' perceptions of vulnerability and adaptation at the household level using a combination of statistical techniques and Geographic Information System (GIS). The methodology includes: (i) study area and site selection, (ii) identification of risk perception and adaptation indicators, (iii) data collection, (iv) construction of vulnerability and adaptation indices, and (v) analysis of indicator impacts.

2.1 Study area and site selection

Punjab is Pakistan's leading agricultural province, accounting for nearly half of the country's arable land and producing over 70% of its grain crops (GOP, 2019). This study focuses on District Narowal (32°05′58″N, 74°52′29″E) in Gujranwala Division, a major rice–wheat agro-ecological zone located on fertile alluvial plains with an average elevation of 236 meters (Figure 2). The district covers 4,623 sq. km and comprises three tehsils—Narowal, Shakargarh, and Zafarwal—with a population of approximately 1.7 million, of which 85% resides in rural areas and depends largely on agriculture, livestock, and related activities for livelihoods (Pakistan Bureau of Statistics, 2025). The region experiences a humid subtropical climate, with mean temperatures rising in recent decades, summer peaks exceeding 42 °C, winter averages around 17 °C, and annual rainfall of about 900 mm, more than 70% of which occurs during the monsoon season, often in intense and erratic patterns (GOP (Government of Pakistan), 2020). Narowal was selected due to its classification as highly vulnerable under the National Monsoon Contingency Plan (National Disaster Management Authority (NDMA), 2018), having experienced major flood events in 2010, 2012, and 2022 that inundated over 65 villages and large areas of cropland, particularly affecting rice and livestock systems. In addition to flooding, the district faces droughts, pest outbreaks, waterlogging, and soil degradation, creating a multi-hazard risk environment (Mehmood, 2022). Within the district, Shakargarh and Narowal tehsils were purposively selected to capture contrasting vulnerability conditions: Shakargarh is highly exposed to riverine flooding due to its proximity to the Ravi River, while Narowal faces comparatively lower flood intensity but greater challenges from rainfall variability and resource constraints. This combination of climatic variability, agro-ecological sensitivity, and socio-economic dependence on agriculture makes Narowal an appropriate case for analyzing multi-hazard climate vulnerability and adaptation strategies.

2.2 Data sources and collection tools

A mixed-method approach combining quantitative and qualitative techniques was employed. Primary data were collected through structured questionnaires, focus group discussions (FGDs), and field observations, while secondary data on temperature and precipitation trends (2002–2021) were obtained from the Pakistan Meteorological Department (PMD).

The study population comprised rice–wheat farming households located in flood-prone riverbank areas of District Narowal. A total sample of 300 farm households was selected using a 95% confidence level and 6% margin of error (Fahad et al., 2018). A multistage purposive sampling approach was applied: two tehsils were selected, followed by three union councils per tehsil and four villages per council, prioritizing locations proximate to riverbanks. From each village, 10–12 farmers engaged in rice–wheat farming were interviewed. Data were collected between December 2022 and March 2023 in Punjabi and Urdu. Farmers' perceptions of vulnerability and adaptive capacity were assessed using a Likert scale (none, low, moderate, high).

2.3 Climate change vulnerability and index measurement

Farmers' vulnerability perceptions were assessed using the Climate Change Risk Perception Score (CCRPS). Respondents rated 13 climate-related risks on a four-point Likert scale: 0 (none), 1 (low), 2 (moderate), and 3 (high). The CCRPS was calculated as a weighted aggregate:

CCRPS=(0×CCRPn)+(1×CCRPl)+(2×CCRPm)+(3×CCRPh)

where CCRP_*n*, CCRP_*l*, CCRP_*m*, and CCRP_*h*represent the number of respondents reporting no, low, moderate, and high perception levels, respectively.

To ensure comparability across risks, the CCRPS was standardized into the Standardized Climate Change Risk Perception Index (SCCRPI), scaled from 0 to 100:

SCCRPI=(CCRPSMaximum Possible CCRPS)×100

where the maximum possible CCRPS equals the total number of respondents multiplied by the highest Likert score (3). The SCCRPI was calculated separately for each risk and then analyzed collectively to assess overall exposure patterns.

2.4 Assessment of farmers' adaptation strategies

Adaptation was evaluated using the Sustainable Livelihood Framework (SLF) across five capital assets: natural, physical, financial, human, and social. Indicator weights were derived using the Analytic Hierarchy Process (AHP) based on expert judgement (Saaty, 2008). Pairwise comparison matrices were normalized, and consistency was verified using the Consistency Ratio (CR), with CR < 0.10 considered acceptable.

The Adaptive Capacity Index (ACI) was calculated as a weighted sum of indicators:

ACI=∑(wi×Xi)

where w_i represents the AHP-derived weight and X_i the corresponding indicator value. All indicators were normalized using the min–max method to a 0–1 scale prior to aggregation.

2.5 Climate Vulnerability Index (IPCC-CVI)

The study applied the IPCC vulnerability framework, where vulnerability is a function of exposure (E), sensitivity (S), and adaptive capacity (AC):

CVI=(E-AC)×S

Also shown in detail components in Table 1. All three components were normalized to a 0–1 scale to ensure comparability. Exposure and sensitivity indices were derived from SCCRPI and related indicators, while adaptive capacity was calculated using the ACI. Equal weighting was applied to the three components to avoid bias, following standard IPCC-based approaches.

The final CVI ranged from −1 (least vulnerable) to +1 (most vulnerable), enabling comparative assessment across tehsils.

2.6 Climate data analysis and change detection

Historical climate data, including temperature and rainfall records for the period 2002–2021, were obtained from the Pakistan Meteorological Department (PMD) to assess trends and variability. Simple Linear Regression (SLR) was applied to detect changes in mean minimum and maximum temperatures, as well as rainfall patterns over time.

Rainfall data were further classified into seasonal categories, distinguishing between winter and summer periods. The summer season was subdivided into pre-monsoon, mid-monsoon, and post-monsoon phases to capture intra-seasonal variability.

Statistical analyses, including descriptive statistics and index calculations, were conducted using SPSS (Version 21). Spatial distribution maps of the study area were developed using ArcGIS to support the visualization of climatic patterns and trends.

3 Results

3.1 Demographic and climatic trends in District Narowal

Most respondents in both tehsils were middle-aged (36–50 years), followed by young (36.4 and 29.4%) and older groups (30.7 and 28.8%). Household heads were predominantly male (90.7 and 82.5%), with only 9.3 and 17.5% female-headed households. Education levels were low, as 37.1 and 43.8% had primary education, while 29.3% were illiterate. Average family size was six, with most households having 6–10 members. Over half of respondents (53.6 and 40.6%) owned medium-sized farms (2.5–6.5 acres), and most belonged to medium-income groups (≈ PKR 51,000/month). Farming experience averaged 47–51 years, with 41%−44% having 21–30 years of experience. Cemented houses were common (62.1 and 53.1%).

Farmers' perceptions of climate variability were validated using 20 years of meteorological data (2002–2021) on temperature and rainfall (Figure 3). Simple Linear Regression (SLR) revealed a declining trend in mean maximum annual temperature (y = −0.03 + 29.88) and an increasing trend in mean minimum temperature (y = 0.08 + 16.67). The highest mean maximum temperature (30.42 °C) occurred in 2009, while the highest mean minimum temperature (26.03 °C) was recorded in 2018. Rainfall patterns also showed significant variability, with an increasing trend (y = 0.91 + 71.39). Extreme rainfall events were observed in 2003 (133 mm), 2013 (116 mm), and 2014 (110 mm), contributing to severe floods that damaged crops and livestock. These findings align with farmers' reports of erratic rainfall and rising temperatures, particularly during the Kharif season (May–October), when monthly maximum temperatures ranged between 31 °C and 38 °C.

3.2 Climate vulnerability analysis and farmers' response

This section presents the results of climate vulnerability analysis based on farmers' responses. First, the Standardized Climate Change Risk Perception Index (SCCRPI) was calculated along with exposure and sensitivity components, which were ranked to reflect farmers' perceptions of climatic events. Second, farmers' adaptive capacity was assessed and integrated into the IPCC-CVI framework to determine overall vulnerability. Tehsil Narowal exhibited higher exposure (index = 0.83) compared to Shakargarh (index = 0.62) as shown in Tables 2 and 3 respectively. CCRPS values ranged from 269–351 in Narowal and 210–326 in Shakargarh, indicating moderate to high exposure in Narowal and moderate to low exposure in Shakargarh. Flood intensity ranked highest in both tehsils (SCCRPI: Narowal = 95.48; Shakargarh = 94.01), followed by rainfall and temperature fluctuations. SCCRPI values ranged from 95.48 to 73.17 in Narowal and 94.01 to 60.74 in Shakargarh, showing heterogeneous exposure patterns. Both tehsils demonstrated moderate to high sensitivity, with indices of 0.76 (Narowal) and 0.71 (Shakargarh). Sensitivity was influenced by climatic variability and associated biological hazards, affecting farm assets such as fertilizer availability, soil fertility, and access to water and health services. SCCRPI values for sensitivity ranged from 83.47 to 62.19 in Narowal and 85.33 to 70 in Shakargarh.

Table 2. Results of CCRPS and SCCRPI of exposure and sensitivity in tehsil Narowal.

Table 3. Results of CCRPS and SCCRPI in tehsil Shakargarh.

3.3 Adaptive capacity assessment using AHP

Farmers' adaptive capacity was evaluated using the Analytical Hierarchy Process (AHP) to assign weights to indicators under five capital assets: natural, physical, financial, human, and social. Pairwise comparisons produced a Consistency Ratio (CR) of 0.09 for Narowal and 0.05 for Shakargarh, indicating acceptable consistency in judgments (Tables 4, 5). Under natural capital, land ownership had the highest weight (0.13 in Narowal; 0.15 in Shakargarh). For physical capital, irrigation sources were critical (0.06 and 0.07), reflecting reliance on natural water systems. Financial capital indicators such as farm insurance scored 0.10 and 0.08, highlighting limited financial resilience. Social capital emphasized support during hazards (0.10 and 0.09), pointing to gaps in institutional assistance (Tables 6).

Table 4. Normalized comparison matrix and criteria weight of Tehsil Narowal.

NC1-NC3, Natural capital; PC1-PC3, physical capital; FC1-FC3, financial capital; HC1-HC3, human capital; SC1-SC3, social capital of Tehsil Narowal.

Table 5. Normalized comparison matrix and criteria weight of tehsil Shakargarh.

NC1-NC3, natural capital; PC1-PC3, physical capital; FC1-FC3, financial capital; HC1-HC3, human capital; SC1-SC3, social capital of Tehsil Shakargarh.

Table 6. Farmer's response regarding adaptive capacity components.

^aACI is calculated from equation (6).

Overall, 28.34% of households exhibited high adaptive capacity, 39.1% moderate, and 32.56% low. Specifically, 55.02% of households reported high adaptive capacity linked to land ownership, soil quality, and irrigation access, while 25 and 19.8% fell into moderate and low categories, respectively.

The considerable variation in the levels of adaptive capacity was found between both tehsils, the farmers of both tehsils had high moderate and low adaptive capacity levels. Households are categorized by their mean adaptive capacity in Table 7.

Table 7. Calculation of IPCC-CVI for Tehsil Narowal and Shakargarh.

^*IPCC-CVI = −1(least vulnerable) to +1 (extreme vulnerable).

After calculating the major components of climate vulnerability for agricultural households in both tehsils, a comparative analysis was conducted (Figure 4). The IPCC-CVI ranges from −1 (least vulnerable) to +1 (most vulnerable). Based on the IPCC-CVI formula, Tehsil Narowal recorded a vulnerability score of 0.26, while Shakargarh scored 0.19, indicating that Narowal is more vulnerable to climatic hazards due to lower adaptive capacity (Table 7). The vulnerability triangle (Figure 5) illustrates that Narowal had higher exposure (0.83) compared to Shakargarh (0.62). Both tehsils showed moderate sensitivity to climatic and natural hazards, with values of 0.76 and 0.72, respectively. Adaptive capacity was relatively low in both areas, with Narowal at 0.48 and Shakargarh at 0.36, reinforcing the overall vulnerability pattern.

3.4 Differential adaptation strategies preferences by the farmers

Farmers adopted several strategies to cope with climatic hazards (Figure 6). The survey revealed that 40.0% of farmers in Narowal and 48.8% in Shakargarh moderately changed their sowing calendars, while 52.8 and 41.9% had high capacity to adjust crop calendars. Fertilizer and pesticide adjustments were common, with 43.6 and 50.6% of respondents reporting moderate changes to maintain soil fertility. However, most farmers did not adopt climate-tolerant crop varieties, showing low adaptive capacity (48.6 and 50.8%). Switching from traditional cereals to cash crops was moderately practiced by 50.0 and 42.5% of farmers to manage financial risks. Tree planting was limited in Narowal (40.0% low capacity) but moderately practiced in Shakargarh (43.8%). A significant proportion of farmers (50.7 and 43.8%) left land fallow for one or more cycles to restore soil organic matter and moisture.

4 Discussion

The study assessed the vulnerability of agricultural households in District Narowal, Punjab, to multi-hazard climate variability using an extended analytical framework. The findings indicate high exposure to climatic hazards, while adaptive capacity remains limited due to inadequate knowledge, financial constraints, and weak institutional linkages. Socio-economic and demographic factors—particularly age, farming experience, landholding size, and ownership rights—emerged as key determinants of vulnerability and adaptation, consistent with previous studies (Abid et al., 2016; Ahmed et al., 2021; Bryan et al., 2013; Mazhar et al., 2021; Yohannes, 2016).

Age-related vulnerability was evident, as older farmers tend to rely on traditional practices and resource-intensive irrigation, increasing environmental stress. Gender dynamics further shape vulnerability: while farming is predominantly male-led, women contribute through off-farm activities such as handicrafts and home gardening (Zamasiya et al., 2017). However, mechanization and sociocultural barriers limit women's participation in modern agricultural practices, corroborating findings by Anjum and Fraser (2021). Education and financial capacity strongly influence adaptive responses. Low education levels constrain awareness of climate risks and preparedness for extreme events, as also observed by Tripathi and Mishra (2017).

Farmers' perceptions of climate change align with meteorological trends, particularly increasing temperature variability and erratic rainfall, which disrupt rice–wheat cropping cycles and reduce productivity (Gbetibouo, 2009). These findings mirror evidence from other regions, including Nigeria, Ethiopia, and Ghana, where similar climatic stresses adversely affect agricultural output (Ayanlade et al., 2017; Araro et al., 2020; Derbile et al., 2022; Ketema and Negeso, 2020).

Flooding emerged as the most significant hazard, causing recurrent crop and livestock losses, especially during post-monsoon periods (Ullah et al., 2018). In addition to direct climatic impacts, biological hazards—including pest outbreaks, invasive weeds, and livestock diseases—significantly exacerbate vulnerability. Farmers reported increased pest attacks, locust swarms, and persistent weeds despite pesticide use, consistent with findings across South Asia and Africa (Khan et al., 2020; Shukla et al., 2021; Dhungana et al., 2020). These pressures have intensified chemical use, degrading soil and water quality while increasing production costs and health risks.

Livestock—which plays a central role in rural livelihoods—has been adversely affected by disease outbreaks such as lumpy skin disease, reducing income and food security (Khan et al., 2021). Financial vulnerability is compounded by the absence of crop insurance, limited access to formal credit, and reliance on exploitative informal lending systems. Land tenure insecurity further constrains adaptation, as smallholders without ownership rights have limited access to government support and reduced incentives to invest in long-term resilience measures.

Social and institutional factors also influence adaptive capacity. Weak extension services, limited infrastructure, and insufficient early warning systems increase sensitivity to climate shocks. Conversely, community networks and social capital enhance resilience by facilitating information sharing and collective coping strategies, as demonstrated in other contexts (Bryan et al., 2013; Tran et al., 2022). Emerging local adaptation practices—such as adjusting sowing calendars, greenhouse cultivation, and short-duration crops—indicate adaptive potential but remain insufficiently scaled.

Comparative evidence from other regions reinforces the patterns observed in this study. For instance, (Mramba and Mapunda 2025) report that smallholder farmers in Tanzania adopt strategies such as crop diversification, early sowing, and the use of drought-tolerant varieties—demonstrating adaptive behaviors that closely parallel those observed among rice–wheat farmers in Punjab. These similarities highlight the broader relevance of localized adaptation strategies across diverse agro-ecological contexts.

Similarly, studies from Ethiopia identify key determinants of adaptation, including crop diversification, adjustments in planting dates, and livestock management practices. These findings provide important comparative insights into how smallholder farmers make adaptation decisions under climate stress, further supporting the evidence from Narowal that adaptive capacity is shaped by both environmental pressures and socio-economic conditions (Shukla et al., 2016; Paul et al., 2019; Parker et al., 2019; Thorlakson and Neufeldt, 2020; Jamshidi et al., 2020).

5 Conclusion and policy implications

This study demonstrates that rice–wheat farming systems in Narowal are subject to significant vulnerability arising from the combined effects of climatic variability, biological hazards, and socio-economic constraints. Climatic stressors, including floods, erratic rainfall, and temperature extremes, disrupt cropping patterns, reduce yields, and increase production costs. At the same time, limited financial capacity, inadequate institutional support, and uneven access to resources constrain farmers' ability to adapt effectively, with small and marginal farmers facing disproportionate risks.

Addressing these vulnerabilities requires a comprehensive and integrated policy response (Arbaiah et al., 2021). Expanding access to finance through crop insurance schemes and affordable credit can enhance farmers' adaptive capacity and reduce reliance on informal lending. At the same time, linking the economic trade-offs inherent in rice–wheat systems to actionable responses such as crop diversification, improved irrigation efficiency, climate-smart agriculture, and land restoration approaches, including Land Degradation Neutrality (LDN)—is critical to enhancing both sustainability and resilience. Such measures can help reduce pressure on water resources while maintaining agricultural productivity.

Investments in infrastructure, including efficient irrigation systems, improved market access, and reliable early warning services, are essential to strengthening system-wide resilience. In parallel, targeted capacity-building initiatives can equip farmers with the knowledge and skills required for climate adaptation and sustainable resource management. Ensuring gender-inclusive approaches is also crucial, as empowering women can enhance household resilience and broaden participation in climate-smart practices.

Integrating these interventions into provincial planning frameworks, while aligning them with national climate and agricultural policies, will be essential for positioning Narowal's farming systems within a broader resilience framework that balances productivity with long-term environmental sustainability under increasing water stress and climate uncertainty.

Generative AI statement

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

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Naghmana Ghafoor, Lahore College for Women University, Pakistan

Reviewed by: Sadhon Chandra Swarnokar, Khulna University, Bangladesh

Zainab Nuhu, Bayero University Kano, Nigeria