Climate Change, Economic Vulnerability and Unequal Adaptation in Rainfed Cereal Farming Systems in the Western Himalayas

Climate Change, Economic Vulnerability and Unequal Adaptation in Rainfed Cereal Farming Systems in the Western Himalayas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Change, Economic Vulnerability and Unequal Adaptation in Rainfed Cereal Farming Systems in the Western Himalayas Binny Sharma, Ganta Durga Rao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8887579/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mountain regions are among the most climate-sensitive socio-ecological systems, yet adaptation outcomes within rainfed cereal farming remain uneven and insufficiently researched. This study examines how economic vulnerability mediates climate adaptation in the rainfed cereal systems of the Western Himalayas, focusing on the Jammu Division of Jammu & Kashmir, India. Primary data were collected in 2025 through a cross-sectional household survey of 408 farmers selected using a multi-stage disproportionate stratified random sampling design across temperate, intermediate, and subtropical agro-climatic zones. Economic vulnerability was operationalised across yield, input-cost, and income dimensions, and analysed using binary logistic regression to assess structural determinants and differential adaptation outcomes. The findings show that climate variability translates into differentiated livelihood outcomes through unequal access to land, education, institutional support, and livelihood diversification opportunities. Adaptation emerges not as a uniform technical adjustment but as a socially differentiated process embedded within structural inequality. A key contribution of the study is the identification of a cost–resilience paradox: stabilising production under climatic stress often requires higher input expenditure, which simultaneously increases financial exposure and income volatility for resource-constrained households. As a result, resource-endowed farmers are more likely to convert adaptation into yield and income stability, while marginal farmers remain locked in high-sensitivity production systems and coping practices. By providing region-specific empirical evidence from Jammu & Kashmir, which is an underrepresented area in Himalayan scholarship, the study demonstrates that resilience in mountain rainfed cereal systems depends less on technological adoption alone than on reducing underlying economic vulnerability and structural inequality. Climate change Adaptation Economic vulnerability Rainfed cereal farming systems Mountain agriculture Western Himalayas 1. Introduction Mountain regions cover nearly one quarter of the world’s land surface, support about 12 percent of the global population, and offer critical ecosystem services such as water provision, biodiversity conservation and climate regulation (Paudel et al., 2022; Wani & Marothia, 2019 ). Characterised by steep topography, sharp altitudinal gradients, diverse microclimates, predominantly rainfed agriculture, and high exposure to extreme events, these regions are among the most climate-sensitive socio-ecological systems in the world (FAO, 2015 ; Hussain & Guha, 2021 ; Körner et al., 2017 ; Romeo et al., 2020 ; Wani & Marothia, 2019 ). Food insecurity is significantly more pronounced in mountain regions than in plains, particularly in developing countries (Bandara et al., 2024 ; Poudel et al., 2017 ). This heightened food insecurity is closely linked to the structural and bio-physical constraints facing mountain agriculture (FAO, 2015 ; Körner et al., 2017 ). Harsh terrain, land degradation, climate-induced disasters, weak infrastructure, limited market access, and poor institutional support jointly constrain agricultural productivity and livelihood security (Paudel et al., 2021 ; Romeo et al., 2020 ; Shaheen et al., 2017 ). Of the estimated 648 million people living in mountain areas of developing countries, around 346 million were vulnerable to food insecurity by 2017, reflecting the scale and persistence of vulnerability in these regions (Romeo et al., 2020 ). Asia alone accounts for nearly one-third of the world’s mountain area and over half of the global mountain population, with South Asia experiencing a sharp rise in food insecurity among mountain dwellers over the past two decades (Bandara et al., 2024 ). A substantial share of food insecurity among South Asia’s mountain dwellers is concentrated in the Himalayan mountain system, the largest and most densely inhabited mountain region in the subcontinent, with distinct climatic and livelihood vulnerabilities (Dhimal et al., 2021 ; Pandey, 2016 ; Rasul et al., 2019). A central driver of this food insecurity is the region’s acute agricultural vulnerability to climate change (Dahal et al., 2023 ; Dhimal et al., 2021 ). Within this broader Himalayan context, the Western Himalayas, has been identified as a sub-region where climatic and livelihood vulnerabilities are most acutely translated into household-level risk (Kapruwan et al., 2024 ; Namgyal et al., 2025 ; Shah et al., 2026 ). In this context of food insecurity in mountain regions, cereal-based farming systems, particularly cultivation of rice, wheat, and maize represent some of the most climate-sensitive agricultural livelihood systems. In these regions, climate change manifests not only through biophysical impacts on crop yields but also through economic consequences such as yield variability, rising input costs, unstable market returns for farming households (Bandara et al., 2024 ; Dahal et al., 2023 ; Wani & Marothia, 2019 ). Together, these pressures erode farm incomes, increase indebtedness, and limit the ability of households to withstand climate shocks and invest in adaptive measures (Namgyal et al., 2025 ; Shah et al., 2026 ). These economic impacts can be conceptualised as economic vulnerability, which links climate risks to livelihood insecurity by capturing how shocks translate into income losses, welfare decline, and constrained adaptive responses (Adger, 2006 ; Intergovernmental Panel on Climate Change (IPCC), 2022; Ribot, 2011 ). Importantly, economic vulnerability also explains differential outcomes among farmers through unequal adaptation capacities. While some households are able to diversify, adopt improved technologies, and access institutional support, others remain constrained by limited assets and opportunities, resulting in unequal adaptation outcomes within the same agro-ecological setting (Pandey, 2016 ; Paudel et al., 2022). The cereal-based farming systems in the Western Himalayas present a critical case where climate change acts as a risk multiplier by interacting with pre-existing socio-economic, institutional, and biophysical constraints. Rising temperatures, erratic precipitation, and extreme events intersect with non-climatic stressors such as male out-migration, labour shortages, insecure land tenure, land degradation, limited access to inputs and extension services, and weak market integration, leading to yield instability, income volatility, and rising production costs (O’Brien & Leichenko, 2008 ; Poudel et al., 2017 ). Differences in economic resources, institutional access, and livelihood diversification shape farmers’ capacity to respond, producing unequal adaptation outcomes within the same agro-ecological setting. Economic vulnerability thus functions as the key intermediary through which climate variability is translated into differentiated adaptation outcomes, rather than being a secondary effect of biophysical change (Adger, 2006 ; Dercon, 2005 ; Ribot, 2011 ). Against this backdrop, the present study examines economic vulnerability and unequal adaptation capabilities among rainfed cereal farming households in the Western Himalayas, focusing on how differential access to resources and institutions mediates climate risk and livelihood outcomes. Accordingly, the study is guided by the following research questions: 1. How does climate variability affect economic vulnerability among rainfed cereal farming households in the Western Himalayas of Jammu & Kashmir? 2. How do differences in access to assets, institutions, and livelihood opportunities shape households’ adaptation capacities and strategies? 3. In what ways does economic vulnerability mediate unequal adaptation outcomes within the same agro-ecological setting? In this study, economic vulnerability is framed as the susceptibility of farming households to income and livelihood losses stemming from exposure to climatic and non-climatic shocks, combined with limited capacity to cope, adapt, or recover (Adger, 2006 ; Chambers, 1989 ; Dercon, 2005 ). In climate-sensitive agricultural systems, economic vulnerability extends beyond short-term income fluctuations to encompass medium-term and long-term challenges such as yield uncertainty, rising input costs, indebtedness, and constrained investment in adaptive measures (FAO, 2016; IPCC, 2022 ). For rainfed cereal farming systems in the Western Himalayas, economic vulnerability thus reflects the interaction of structural constraints and unequal access to assets, institutions, and risk-buffering mechanisms, which together shape unequal adaptation capabilities. The study is grounded in an integrated vulnerability-livelihoods framework that links climate change, economic vulnerability, and unequal adaptation outcomes within the cereal-based farming systems of the Western Himalayas. In line with vulnerability theory, climate change affects farming households through the combined effects of exposure to climatic variability, sensitivity of rainfed cereal systems, and differences in adaptive capacity (Adger, 2006 ; IPCC, 2014 ). Drawing on the sustainable livelihoods approach and political economy perspectives, the study conceptualises adaptation as a socially differentiated process shaped by unequal access to natural, physical, human, financial, and institutional assets, as well as by governance and power relations (Adger et al., 2005 ; Chambers & Conway, 1992 ; DFID, 1999 ; Ribot, 2014 ). 2. Review of literature 2.1 Economic vulnerability in agrarian and mountain livelihoods Cereal-based farming systems occupy a central position in global food production, food security, and rural livelihoods, particularly in low- and middle-income countries across Africa, Asia, and Latin America (Kruseman et al., 2020). Cereals such as rice, wheat, and maize provide the primary sources of dietary energy and protein for large populations, contribute substantially to nutritional security, and generally exhibit lower environmental footprints compared to many alternative food systems (Kruseman et al., 2020; Willet et al., 2019). At the household level, cereal cultivation also constitutes a major source of income and employment for millions of smallholder farmers and rural workers (Reddy & Rahut, 2025 ). However, projections indicate that rising cereal demand will coincide with declining per-capita land availability and increasing climatic stress, heightening the economic vulnerability of cereal-dependent farming households (Kropff & Morell, 2019 ). Vulnerability frameworks emphasise that farming households dependent on climate-sensitive crops experience increased income uncertainty when exposure to climatic shocks combines with limited access to assets, credit, insurance, and institutional support (Adger, 2006 ; Chambers, 1989 ; Dercon, 2005 ). This vulnerability is particularly noticeable in rainfed cereal systems, which account for nearly 60% of global cereal crop area but contribute a disproportionately smaller share of total output due to persistent yield gaps caused by climatic stress, soil degradation, and management constraints (Anderson et al., 2016 ). As a result, rainfed cereal-based farming systems are characterised by high production volatility and economic risk. In mountain regions, these vulnerabilities are amplified by ecological fragility, limited arable land, and weak infrastructure. Mountain cereal farming is generally constrained by steep slopes, fragile soils, poor irrigation coverage, and restricted market access, intensifying income uncertainty under climate stress (FAO, 2016; Körner et al., 2017 ). Empirical evidence indicates that rainfed cereals in mountain regions exhibit high sensitivity to rainfall variability and temperature fluctuations, leading to yield declines and heightened livelihood risk for marginal and small farmers (Bouras et al., 2020 ; Willet et al., ). Accordingly, economic vulnerability in mountain agrarian systems is closely linked to the structural characteristics of rainfed cereal-based farming systems. 2.2 Unequal adaptation capacities and strategies in mountain agriculture Adaptation within cereal-based farming systems is increasingly recognised as a socially differentiated process, shaped by unequal access to land, capital, technology, information, and institutions (Adger, 2006 ; Alimagham et al., 2024 ; Ribot, 2011 ; Wei et al., 2025 ). While climate-smart practices such as drought- and heat-tolerant cereal varieties, improved irrigation scheduling, conservation tillage, and crop diversification are widely promoted as pathways to enhance resilience and income stability, adoption remains uneven across farming households (Hussain & Guha, 2021 ; Kropff & Morell, 2019 ). Households with greater resource endowments and institutional access are better positioned to adopt such productivity-enhancing adaptations, while resource-poor farmers often rely on short-term coping strategies that do little to reduce long-term economic vulnerability (Dercon, 2005 ; Rao & Sharma, 2025 ). In rainfed cereal systems, unequal adaptation capacities are clearly evident among farming households. At the global level, studies show that low-input cereal farming under erratic rainfall and weak extension services results in chronically low and unstable yields, with yield gaps between actual and potential production ranging from 0.5 to over 5 t/ha in Sub-Saharan Africa (Alimagham et al., 2024 ; Anderson et al., 2016 ). In North Africa, rainfed wheat and barley systems exhibit high sensitivity to drought and warming, leading to strong inter-annual yield fluctuations and income instability (Benmehaia et al., 2020 ; Bouras et al., 2020 ). Even in relatively advanced cereal production systems in southern Europe, such as Spain, rising temperatures, declining rainfall, and land degradation are increasing climate-induced risks, underscoring that adaptation capacity remains uneven across regions (Rivas-Tabares et al., 2021 ). Mountain cereal farmers experience additional constraints that further differentiate adaptation outcomes. Evidence indicates that farmers with access to irrigation, improved cereal varieties, extension services, and institutional support are more likely to adopt yield-stabilising and income-enhancing strategies, whereas marginal farmers depend on migration, borrowing, consumption reduction, or crop abandonment to cope with climate stress (Poudel et al., 2017 ; Rao & Sharma, 2025 ; Shah et al., 2026 ). In South Asia, adaptation through crop substitution, such as shifting land from climate-sensitive rice to alternative cereals has been shown to reduce climate-induced losses or increase farm profits, but such transitions are feasible primarily for farmers with sufficient land, capital, and market access (Hussain & Guha, 2021 ; Wei et al., 2025 ). These findings underscore that adaptation in cereal-based farming systems is highly unequal and closely linked to economic vulnerability. 2.3 Research gap In the Himalayan region, cereal-based farming systems, particularly rainfed wheat, maize, and millets form the backbone of rural livelihoods but are highly exposed to climate variability and structural constraints. Studies from Nepal and India document how climate stress in rainfed cereal systems translates into food insecurity, income instability, and differentiated livelihood outcomes among smallholder farmers (Poudel et al., 2017 ; Shaheen et al., 2017 ). Research from Ladakh highlights how access to irrigation, livestock, and state support enables some households to stabilise cereal production and incomes, while others remain highly vulnerable (Namgyal et al., 2025 ). Similarly, evidence from Uttarakhand underscores the role of institutional interventions such as watershed development, extension services, and government schemes in shaping adaptation outcomes in cereal-based mountain agriculture (Kapruwan et al., 2024 ; Wani & Marothia, 2019 ). Despite this growing body of work, empirical evidence on economic vulnerability and unequal adaptation in cereal-based farming systems in the Western Himalayas remains geographically uneven. Existing studies are concentrated largely in Uttarakhand (Kapruwan et al., 2024 ; Shah et al., 2026 ), with limited systematic analysis from the Jammu & Kashmir region of the Western Himalayas. While Namgyal et al. ( 2025 ) provide important insights from the trans-Himalayan context of Ladakh, the agro-ecological, institutional, and socio-political conditions of Jammu & Kashmir differ markedly from both Uttarakhand and Ladakh in terms of land tenure systems, irrigation dependence, market integration, governance structures, and exposure to climatic risks. These conditions also vary significantly across sub-regions within Jammu and Kashmir. Consequently, the pathways through which climate change affects cereal production, household incomes, and adaptation capacities in Jammu & Kashmir remain insufficiently examined. This spatial and contextual gap limits the generalisability of the conclusions drawn from the existing studies and highlights the need for region-specific empirical evidence. The present study responds to this gap by providing empirical insights from the Western Himalayas of Jammu & Kashmir. 3. Methods 3.1 Study area, sampling design and data collection The study was conducted in the Western Himalayan region, focusing on the three (temperate, intermediate, and subtropical) agro-climatic zones within the Jammu Division of Jammu and Kashmir, India. A multi-stage disproportionate stratified random sampling method was employed to ensure balanced representation across agro-climatic zones. In the first stage, Rajouri and Kathua districts were purposively selected from the ten districts of the Jammu Division, as they are the only districts having all three agro-climatic zones. This unique agro-climatic diversity enables comparative analysis of climate exposure, economic vulnerability, and adaptation outcomes across ecological gradients within a broadly similar institutional and socio-political setting. According to the 10th Agriculture Census (2015–16), Rajouri and Kathua together account for 158,150 operational landholdings, representing over one-fourth of all operational holdings in the Jammu Division. In the second stage of sampling, twelve tehsils (six per district) were randomly selected from Rajouri and Kathua districts to cover all three agro-climatic zones. In the final stage, 34 cereal-farming households were randomly selected from each tehsil, resulting in a total sample of 408 households evenly distributed across agro-climatic zones and districts. The minimum sample size (384) was estimated using Cochran’s formula at a 95% confidence level and a 5% margin of error. The minimum sample was increased to 408 to enhance representativeness and account for potential non-response. Equal distribution of households across agro-climatic zones was adopted despite differences in population size to enable systematic comparison of vulnerability and adaptation outcomes. This strategy aligns with the study’s theoretical focus on differentiated exposure and unequal adaptive capacity of households and compensates for the absence of reliable sub-district-level population data. Following sampling process, a cross-sectional household survey design was employed to analyse climate-induced economic vulnerability and variability in adaptation practices in rainfed cereal farming systems. Primary data were collected in 2025 using an interview schedule that captured socio-economic characteristics, production outcomes, climate perceptions, economic vulnerability, adaptation practices, institutional access, and livelihood diversification. Responses were recorded by the researcher during the interviews. 3.2 Measurement of key variables 3.2.1 Economic vulnerability indicators Consistent with the vulnerability-livelihoods framework, economic vulnerability was operationalised using three outcome dimensions (yield vulnerability, input-cost vulnerability, and income vulnerability) reflecting exposure, sensitivity, and adaptive capacity. Yield vulnerability was measured as a binary variable, coded as 1 for households producing ≥ 10 quintals of the main cereal crop and 0 for those producing < 10 quintals. Input-cost vulnerability was captured through annual agricultural input expenditure incurred for the main cereal crop, coded as 1 for expenditures exceeding ₹20,000 and 0 for expenditures at or below ₹20,000. Income vulnerability was assessed using farm income derived from the cultivation of the main cereal crop, coded as 1 for households earning more than ₹50,000 and 0 for those earning ₹50,000 or less. These thresholds were determined based on sample distributions and regional farming statistics and represent economically meaningful cut-offs within smallholder-dominated rainfed cereal farming systems in the region. 3.2.2 Climate stress perception Farmers’ perceptions of climate change were measured with the help of a set of binary indicators capturing perceived changes in temperature, rainfall, and other climatic phenomena. In addition, perceived severity of temperature rise and the perceived percentage decline in rainfall were recorded using a 10-point Likert scale. Together, these indicators capture experienced climate exposure and sensitivity at the household level and are consistent with the IPCC vulnerability framework. 3.2.3 Adaptation practices Adaptation practices adopted by farming households were classified into incremental, systemic, and transformational strategies following climate adaptation theory. Incremental adaptations include adjustments in sowing practices (e.g., repeated sowing, altered labour scheduling, additional input use) and moisture conservation practices (mulching, bunding, and moisture pits). Systemic adaptations comprise crop diversification and adoption of improved irrigation and agricultural machinery. Transformational adaptations include crop switching and off-farm migration. Each adaptation variable was coded as a binary indicator (1 = adopted, 0 = not adopted), enabling assessment of their differential effects on yield, input expenditure, and farm income. 3.2.4 Structural and human capital controls To capture unequal adaptive capacity across households, the analysis incorporated structural and human capital variables such as education level, agro-climatic zone, landholding size, and age. Education and landholding size represent human and natural capital endowments, while agro-climatic zone captures spatial exposure to climatic stress. Age is treated as a life-cycle indicator reflecting experience, risk preferences, and labour capacity, and is therefore used only in analyses examining adaptation behaviour and outcomes rather than in baseline vulnerability estimations. 3.3 Analytical strategy The empirical analysis was conducted in four stages. First, descriptive statistics (frequencies and percentages) were used to present socio-economic characteristics, farm structure, income levels, and institutional access, thereby establishing a baseline assessment of economic vulnerability among rainfed cereal-farming households. Second, cross-tabulations and chi-square tests were employed to examine variation in climate change perceptions, measured using the variables defined in section 3.2.2 , across agro-climatic zones, locations, age groups, and education levels, enabling the identification of socially and spatially differentiated patterns of exposure and awareness. Third, binary logistic regression models were employed to assess the structural determinants of economic vulnerability across the outcome dimensions defined in section 3.2.1 (yield vulnerability, input-cost vulnerability, and income vulnerability). The models included education, agro-climatic zone, and landholding size as predictors representing human capital, spatial exposure, and asset endowments, respectively. Finally, to examine unequal adaptation outcomes, adaptation variables discussed in section 3.2.3 were incorporated into the regression models alongside the structural controls and age. This analysis assessed whether adaptation practices mitigate or redistribute economic vulnerability by influencing yields, production costs, and income levels, consistent with a political-economy perspective on differentiated and unequal adaptation. 3.4 Limitations of the Study: While this study offers valuable insights into the economic vulnerability aspects of rainfed cereal farming systems in Western Himalayas, several limitations should be noted. The research was confined to two districts, which may not fully represent the diversity of farming systems across the Western Himalayas. Its cross-sectional design limits the ability to establish long-term causal relationships between climate variables and farming outcomes. The analysis relied primarily on farmers’ perceptions, which, while crucial for understanding adaptation behaviors, may not always correspond with meteorological data. 4. Results 4.1 Socio-economic profile and baseline vulnerability of sample households The socio-economic profile of sampled households presents interlinked structural and human vulnerabilities that shape differentiated exposure and sensitivity to climate change in rainfed cereal farming systems in the Western Himalayas. The farming population is ageing, with 51 per cent of respondents above 50 years and limited youth participation. This demographic tilt reflects declining agricultural viability amid climatic uncertainty. It also weakens long-term adaptive capacity of farmers by constraining labour availability, innovation uptake, and generational renewal. Agriculture is male-dominated (83.8%), reflecting persistent gender asymmetries in land ownership, decision-making, and institutional recognition. The limited reported participation of women in farming activities indicates structural exclusion, reinforcing gendered vulnerability in access to resources and adaptation opportunities. Marginalised groups such as Scheduled Tribes (40 per cent) and Scheduled Castes (19.1 per cent) constitute a majority of respondent households, highlighting the intersection of social identity with agrarian vulnerability. These households often face cumulative constraints related to land quality, market integration, credit access, and institutional support. Education, as a human capital indicator shows that the majority of respondents possess non-technical formal qualifications, with only slightly over 10 per cent having technical education. Limited technical education restricts farmers’ ability to access climate information, employ sustainable practices, and engage with extension systems, resulting in informational and technological disadvantages. The data reveal that more than 90 per cent of households are engaged in supplementary activities signalling widespread diversification with heavy reliance on casual labour and informal employment. While diversification can enhance resilience, its prevalence in this context reflects distress-driven coping rather than livelihood improvement, exposing households to income volatility and seasonal insecurity. The high degree of land fragmentation, with more than 83 per cent of farmers operating on less than two hectares, reinforces structural vulnerability. Small holdings limit economies of scale and constrain farmers’ capacity to absorb climatic and market risks, while the predominance of rainfed cultivation (40%) heightens sensitivity to rainfall variability. Nearly three-fourths of respondents harvest less than 10 quintals of their main crop, indicating low productivity across the region. Further, low input expenditure (₹20,000 or less for 72.1% of households) reflects limited investment capacity, pointing to a persistent low-input–low-output trap. Income levels show economic precarity, with more than half of the respondents earning less than ₹50,000 annually from the main cereal crop, indicating the limited viability of agriculture as a primary source of livelihood. Institutional vulnerability compounds these challenges, as nearly 70 per cent of farmers reported rare or no access to extension services, restricting access to climate advisories and risk-management options. Table 1 Socio-economic characteristics of sample households (N = 408) Variable Categories Frequency Percentage (%) Age Below 50 200 49.0 Above 50 208 51.0 Gender Male 342 83.8 Female 66 16.2 Category General 117 28.7 Other Backward Class 48 11.8 Scheduled Caste (SC) 78 19.1 Scheduled Tribe (ST) 163 40.0 Others 2 0.5 Educational Qualification No formal education 71 17.4 Formal education (non-technical, below secondary) 111 27.2 Secondary education and above (non-technical) 186 45.6 Technical education (any level) 40 9.8 Other occupation(s) besides farming Agricultural labor 83 20.3 Government job 56 13.7 Private sector job 37 9.1 Self-employed/Business 97 23.8 Daily wage labor (non-agricultural) 71 17.4 No other occupation 26 6.4 Others 38 9.3 Landholding size Less than 1 hectare 158 38.7 1–2 hectares 181 44.4 Above 2 hectares 69 16.9 Type of farming practiced Rainfed (non-irrigated) 163 40.0 Irrigated 124 30.4 Mixed 121 29.7 Total yield of main crop last year (quintals) Less than 10 quintals 304 74.5 11 and more quintals 104 25.5 Annual expenditure on inputs (INR) 20,000 and below 294 72.1 20,001 and above 114 27.9 Annual income from farming (INR) Below 50,000 233 57.1 Above 50,000 175 42.9 Total household income (INR) Less than 50,000 4 1.0 50,001–1,00,000 27 6.6 1,00,001–2,00,000 102 25.0 2,00,001–5,00,000 162 39.7 More than 5,00,000 113 27.7 Access to extension services Regular 13 3.2 Occasional 116 28.4 Rare 221 54.2 Never 58 14.2 Source: Primary Data, 2025. 4.2 Perceived climate stress and variability 4.2.1 Descriptive patterns of perceived climate variability Climate stress is experienced as an immediate and lived reality as reflected in the farmers’ widespread and consistent perceptions of climatic change. All respondents (100%) reported noticeable climatic changes over the past 10–20 years, a pattern attributable to high awareness shaped by direct exposure rather than institutional information. Farmers across agro-climatic zones reported consistent temperature and rainfall variability, whereas other meteorological changes (strong winds, cloud cover and snowfall) were largely local in nature. A majority (75.7%) perceived an overall increase in temperature, with 40.2% noting hotter summers and 32.8% reporting milder or shorter winters. Over half of the respondents reported declining rainfall (52.9%) and delayed monsoon onset (50.7%), while 43.6% observed increasingly erratic rainfall patterns. Only 22.8 per cent farmers reported a reduction in the number of rainy days, suggesting that vulnerability is driven primarily by disruptions in rainfall timing rather than absolute scarcity, with direct implications for sowing, crop establishment, and yields. The perceptions of respondents also highlight the intensity of climate stress. More than half of the respondents (57.6%) rated summer temperature increases at six or above on a ten-point scale, indicating moderate to high heat stress. Similarly, a substantial majority (83.8%) rated the decline in rainfall at six or above on the same ten-point scale, suggesting that perceived rainfall stress is both pronounced and widespread. These patterns reinforce that vulnerability arises not merely from variability, but from the experienced severity of climatic changes. Farmers’ climate perception is shaped predominantly by experiential and social sources. Personal experience (85.5%), family traditions (80.9%), and peer interactions were identified as the most influential, while extension services and training programmes were widely perceived as weak or absent. This reliance on lived experience reflects both limited institutional reach and the necessity for farmers to rely on personal knowledge and observation to interpret climate risk in highly uncertain environments such as the Western Himalayas. Table 2 Farmers’ perceptions, intensity, and influencing factors of climate change (N = 408) A. Perception of specific climatic changes Category Specific change observed Yes (f, %) No (f, %) Temperature Overall increase in temperature 309 (75.7) 99 (24.3) Hotter summers 164 (40.2) 244 (59.8) Milder/shorter winters 134 (32.8) 274 (67.2) Rainfall Decreased rainfall 216 (52.9) 192 (47.1) Delayed monsoon 207 (50.7) 201 (49.3) Erratic rainfall 178 (43.6) 230 (56.4) Fewer rainy days 93 (22.8) 315 (77.2) Other changes Strong winds 278 (68.1) 130 (31.9) Reduced cloudiness 77 (18.9) 331 (81.1) Changes in snowfall 86 (21.1) 322 (78.9) Other meteorological changes 11 (2.7) 397 (97.3) B. Perceived intensity of climate stress (10-point scale) Climate Stress Indicator Rated 6 and Above (f, %) Rated Below 6 (f, %) Summer temperature increase 235 (57.6%) 173 (42.4%) Rainfall decline 342 (83.8%) 66 (16.2%) C. Influencing factors of climate change perception Factors Most Influential (f, %) Influential (f, %) Neutral (f, %) Less Influential (f, %) Not Influential (f, %) Personal experience 349 (85.5) 53 (13.0) 6 (1.5) 0 (0.0) 0 (0.0) Media/news 64 (15.7) 151 (37.0) 97 (23.8) 55 (13.5) 41 (10.0) Peer farmers 209 (51.2) 133 (32.6) 43 (10.5) 21 (5.1) 2 (0.5) Extension officers 0 (0.0) 10 (2.5) 78 (19.1) 204 (50.0) 116 (28.4) Training/workshops 2 (0.5) 19 (4.7) 36 (8.8) 110 (27.0) 241 (59.1) Family traditions 330 (80.9) 70 (17.2) 4 (1.0) 0 (0.0) 4 (1.0) Source: Primary Data, 2025. 4.2.2 Differential perceptions across agro-climatic zones, locations, age and education Chi-square analysis shows that perceptions of farmers regarding rainfall (declining rainfall, delayed monsoon onset, erratic rainfall) and other meteorological changes (strong winds, and changes in snowfall) differ across agro-climatic zones, locations, age and education. In contrast, temperature-related perceptions (overall warming, hotter summers, and shorter winters) do not show significant variation across these spatial and social categories, suggesting a widely shared perception of warming throughout the region. Rainfall-related indicators (declining rainfall, delayed monsoon onset, erratic rainfall) and other meteorological changes (strong winds, and changes in snowfall) varied significantly across agro-climatic zones (p < 0.001). Subtropical zones reported greater rainfall irregularity, while temperate and intermediate zones more acutely perceived declining snowfall, reflecting zone-specific exposure to climatic stressors. All rainfall and other meteorological indicators varied significantly across tehsils (p < 0.01), with particularly high chi-square values for rainfall variability and snowfall changes. This pronounced location specific variation highlights the importance of local climatic conditions in shaping lived experiences of climate stress and cautions against regionally uniform vulnerability assessments. Age-based differences also shaped farmers’ perceptions. Older farmers were more likely to perceive delayed monsoons, milder winters, strong winds, and changes in snowfall, suggesting that long-term experiential knowledge enhances sensitivity to gradual and seasonal climatic shifts. Younger farmers, by contrast, appeared less cognizant of such changes, potentially limiting anticipatory adaptation practices. Education also exerted a considerable influence on farmers’ perceptions. More educated farmers were more likely to perceive rainfall decline, erratic rainfall, fewer rainy days, and changes in snowfall. This indicates that education enhances awareness of complex and less immediately visible climatic processes. Table 3 a: Chi-square results of farmers’ perceptions across agro-climatic zones and locations Climate Variable Perception Indicator Zones χ² (df = 2) p-value Sig. Locations χ² (df = 11) p-value Sig. Temperature Overall increase 3.92 0.141 No 29.49 0.002 Yes Hotter summers 3.99 0.136 No 68.60 < .001 Yes Milder/shorter winters 2.76 0.252 No 58.90 < .001 Yes Rainfall Decreased rainfall 15.29 < .001 Yes 116.88 < .001 Yes Delayed monsoon 37.48 < .001 Yes 109.47 < .001 Yes Erratic rainfall 54.13 < .001 Yes 120.03 < .001 Yes Fewer rainy days 5.27 0.072 No 42.83 < .001 Yes Meteorological changes Strong winds 32.60 < .001 Yes 82.86 < .001 Yes Reduced cloudiness 5.03 0.081 No 55.88 < .001 Yes Changes in snowfall 61.65 < .001 Yes 243.93 < .001 Yes Source: Primary Data, 2025. Table 3 b: Chi-square results of farmers’ perceptions across age and education Climate Variable Perception Indicator Age χ² (df = 1) p-value Sig. Education χ² (df = 2) p-value Sig. Temperature Overall increase 0.12 0.734 No 3.17 0.205 No Hotter summers 1.67 0.197 No 2.89 0.235 No Milder/shorter winters 10.94 0.001 Yes 8.75 0.013 Yes Rainfall Decreased rainfall 1.36 0.243 No 14.48 0.001 Yes Delayed monsoon 10.65 0.001 Yes 1.13 0.567 No Erratic rainfall 1.10 0.294 No 11.68 0.003 Yes Fewer rainy days 1.63 0.201 No 8.05 0.018 Yes Meteorological changes Strong winds 5.74 0.017 Yes 4.81 0.090 No Reduced cloudiness 1.77 0.184 No 2.64 0.267 No Changes in snowfall 5.71 0.017 Yes 24.11 < .001 Yes Source: Primary Data, 2025. 4.3 Determinants of yield, input-cost, and income vulnerability among farmers in the Jammu Division The regression results reveal how structural inequalities condition economic vulnerability in rainfed cereal farming systems of the Jammu Division. Landholding size emerges as the most consistent determinant of yield, investment behaviour, and income generation, while education and agro-climatic zones exert differentiated effects on the three outcome dimensions under conditions of climatic change. The results highlight the uneven distribution of resources and adaptive capacities across social and spatial outcome dimensions. 4.3.1 Yield vulnerability Landholding size emerges as a key determinant of yield. Compared to marginal farmers (< 1 ha), farmers operating 1–2 hectares (AOR = 4.54, p < 0.001) and more than 2 hectares (AOR = 3.28, p = 0.003) were significantly more likely to achieve yields of at least 10 quintals. This shows the importance of larger landholdings in facilitating input use, manging crops and coping with risk. In contrast, education and agro-climatic zones were not significantly associated with yield. Although, farmers with secondary education and above showed higher odds of achieving better yields, the association was not statistically significant. These findings suggest that structural inequalities in land access are the primary drivers of yield vulnerability, outweighing differences in human capital and agro-climatic zones, and thereby shaping differential capacity to withstand climatic variability. 4.3.2 Input-cost vulnerability Landholding size emerged as a strong determinant of input-cost vulnerability. Farmers with 1–2 hectares (AOR = 2.74, p = 0.003) and more than 2 hectares (AOR = 2.21, p = 0.027) were significantly more likely to incur higher input costs, reflecting both larger scale of operation and greater capacity to invest. A significant association is found between education and input expenditure. Farmers with primary education (AOR = 1.86, p = 0.046) and secondary education and above (AOR = 3.21, p < 0.001) were more likely to spend on agricultural inputs, suggesting that education enhances awareness, planning ability, and willingness to adopt adaptive practices. Agro-climatic zone also emerged as a significant determinant. Farmers in the intermediate (AOR = 1.96, p = 0.032) and subtropical zones (AOR = 1.96, p = 0.034) were more likely to incur annual input expenditures exceeding ₹20,000 than those in the temperate zone. This indicates higher input intensity and cost pressures in the intermediate and sub-tropical agro-climatic zones, driven by greater rainfall dependence and increased investment in fertilizers, pesticides, and irrigation to buffer against rainfall variability and production uncertainty. Input-cost vulnerability thus reflects not only exposure to agro-climatic pressures but also differentiated investment capacity shaped by landholding size and education. 4.3.3 Income vulnerability Landholding size also emerged as a strong determinant of income vulnerability. Farmers owning 1–2 hectares (AOR = 15.81, p < 0.001) and more than 2 hectares (AOR = 2.30, p = 0.020) exhibited substantially higher odds of earning more than ₹50,000 annually from farming, confirming land access as a central determinant of farm income security. Education likewise emerged as a significant determinant. Farmers with primary education (AOR = 1.95, p = 0.043) and those with secondary education and above (AOR = 3.24, p < 0.001) were significantly more likely to achieve higher farm incomes, possibly reflecting improved managerial efficiency, information access, and market engagement. Agro-climatic zone also emerged as a significant determinant of income vulnerability. Farmers in the intermediate zone were significantly more likely to cross the income threshold (AOR = 2.05, p = 0.034) than those in temperate areas. This may indicate relatively favourable agro-ecological conditions, more stable rainfall regimes, and cropping patterns that generate higher marketable surplus. Greater alignment between production potential and market access in the intermediate zone likely enhances income levels. While farmers in the subtropical zone showed higher odds, the association was not statistically significant. Together, these findings indicate that income vulnerability is mediated by structural, human capital and locational factors. Table 4 Determinants of yield, input cost and income vulnerability (Binary Logistic Regression) Determinant Category Yield ≥ 10 quintals AOR p-value Input cost >₹20,000 AOR p-value Income >₹50,000 AOR p-value Education (Primary education) 0.67 0.374 1.86 0.046 1.95 0.043* Education (Secondary & above) 1.44 0.325 3.21 < 0.001* 3.24 < 0.001* Agro-climatic zone (Intermediate) 1.49 0.238 1.96 0.032* 2.05 0.034* Agro-climatic zone (Sub-tropical) 1.33 0.383 1.96 0.034* 1.78 0.073 Landholding size (1–2 ha) 4.54 < 0.001* 2.74 0.003* 15.81 2 ha) 3.28 0.003* 2.21 0.027* 2.30 0.020* Note : AOR = Adjusted Odds Ratio; p < 0.05 indicates statistical significance. Reference categories No formal education (education); Temperate agro-climatic zone (agro-climatic zone); and Landholding size < 1 hectare (landholding). Source Primary Data, 2025. 4.4 Adaptation practices and economic vulnerability among mountain farmers in the Western Himalayas Building on the preceding analysis of socio-economic and locational determinants of vulnerability, this section examines whether adaptation practices influence yield, input expenditure, and farm income among mountain farmers in the study area. Binary logistic regression models were employed to assess the effects of incremental, systemic, and transformational adaptation strategies on economic outcomes, while controlling for landholding size, education, agro-climatic zone, and age. The results are presented in Table 5 (Adaptation Practices and Economic Vulnerability) and Table 6 (Structural Controls and Economic Vulnerability). 4.4.1 Incremental adaptation and cost-led vulnerability The regression results indicate that adjustments in sowing practices, while widely adopted, are not significantly associated with improvements in yield (AOR = 1.21, p = 0.18) or income levels (AOR = 1.12, p = 0.41). However, they significantly increase the likelihood of higher input costs (AOR = 1.67, p < 0.05), indicating that such adjustments may raise production costs without corresponding income gains, thereby intensifying input-cost vulnerability. Moisture conservation practices not only significantly improve yield outcomes (AOR = 1.34, p < 0.05) but also substantially raise input expenditure (AOR = 1.89, p < 0.01). However, their association with higher income remains statistically weak. These findings suggest that moisture conservation practices improve yield but do not significantly enhance income levels despite higher input costs. 4.4.2 Systemic adaptation: productivity gains with elevated risk Crop diversification is significantly associated with improved yield (AOR = 1.76, p < 0.01) and higher income outcomes (AOR = 1.58, p < 0.05). However, it also significantly increases the likelihood of high input expenditure (AOR = 2.41, p < 0.01), indicating that diversification may enhance production and income while simultaneously raising production costs. Similarly, the adoption of improved irrigation and machinery is significantly associated with higher yields (AOR = 2.12, p < 0.001) and substantially higher input expenditure (AOR = 3.26, p < 0.001). Although the association with income is positive, it is not statistically significant. These findings indicate that such capital-intensive adaptations are linked to productivity gains but are also accompanied by increased financial exposure through higher input costs. 4.4.3 Transformational adaptation and exit-based resilience Crop switching is not significantly associated with improvements in yield but is significantly associated with higher income outcomes (AOR = 1.87, p < 0.05). This indicates that shifts in cropping patterns may enhance farm income levels without necessarily improving yield. Off-farm migration demonstrates the strongest association with higher income levels (AOR = 2.94, p < 0.001). However, it shows no significant association with yield or input expenditure. These findings indicate that migration operates primarily through income diversification rather than through improvements in agricultural productivity or input expenditure. Table 5 Regression results: adaptation practices and economic vulnerability Adaptation Practice Yield ≥ 10 qtls (AOR) p-value Input Cost >₹20,000 (AOR) p-value Income >₹50,000 (AOR) p-value Adjustments in sowing practices (Incremental) 1.21 0.18 1.67 0.03* 1.12 0.41 Moisture conservation (Incremental) 1.34 0.04* 1.89 < 0.01* 1.29 0.09 Crop diversification (Systemic) 1.76 < 0.01* 2.41 < 0.01* 1.58 0.02* Improved irrigation/machinery (Systemic) 2.12 < 0.001* 3.26 < 0.001* 1.41 0.06 Crop switching (Transformational) 1.09 0.47 1.33 0.14 1.87 0.01* Off‑farm migration (Transformational) — — — — 2.94 < 0.001* Note : AOR = Adjusted Odds Ratio; p < 0.05 indicates statistical significance. Reference category: No adaptation practice adopted (for each adaptation variable). Source Primary Data, 2025. 5. Discussion The results indicate that economic vulnerability mediates adaptation outcomes in mountain rainfed cereal systems. In the Western Himalayan context of Jammu & Kashmir, climate change does not directly or uniformly affect livelihoods. Instead, its impacts are transmitted through yield instability, rising input costs, income volatility, and shifts in labour availability, producing differentiated adaptive capacities within the same agro-ecological setting. Thus, environmental change in this mountain region operates not only through biophysical stressors but also through structural and socio-economic factors that shape households’ ability to adopt adaptation strategies. Rainfed cereal systems in mountain environments are highly sensitive to climatic variability, including rainfall fluctuations, temperature extremes, frost, and other meteorological stresses. In such systems, climatic factors are not merely environmental inputs but the primary determinants of crop survival and productivity. Even minor deviations in monsoon timing or temperature can cause disproportionate yield fluctuations. At the same time, inherent ecological and structural constraints of mountain rainfed systems such as small and fragmented landholdings, low moisture retention, limited irrigation facilities, fragile slopes, short growing seasons, thin soils, terraced topography, and limited mechanisation restrict the transformative potential of agricultural intensification. However, the results indicate that exposure alone does not determine vulnerability. Instead, household economic conditions reflected in production thresholds, expenditure levels, land distribution, and income stability mediate how climatic variability is experienced and managed. This aligns with W. Neil Adger’s argument that vulnerability is socially produced and embedded within institutional and economic arrangements rather than determined solely by climatic stresses. Similarly, the vulnerability framework articulated by the Intergovernmental Panel on Climate Change emphasises that adaptive capacity is shaped by access to assets and institutional support, both of which were unevenly distributed among the sampled households. Within this socio-ecological system, economic vulnerability emerges as the central process linking climatic variability to unequal adaptation outcomes. Landholding size is particularly decisive in this regard. Land is not merely a productive asset but a marker of socio-economic position. Farmers with larger holdings benefit from stronger linkages to information networks, institutional support systems, credit markets and input supply chains. These connections enable anticipatory adaptation strategies, including selective intensification, irrigation expansion, and crop/livelihood diversification. In contrast, smallholders often face financial, informational and institutional constraints that narrow their adaptive choices and intensify vulnerability. They rely predominantly on short-term coping measures, such as reducing cultivation intensity, limiting input use, or seeking supplementary wage income. Thus, land inequality translates directly into climate risk inequality. This differentiation reinforces the sustainable livelihoods perspective developed by Robert Chambers and Gordon Conway, which conceptualises resilience as a function of asset endowments rather than exposure alone. Empirically, the findings confirm that rainfed cereal farming systems in Himalayan regions are highly climate-sensitive. However, the present study extends this argument in several important ways. First, it demonstrates that economic vulnerability is multi-dimensional. Rising input expenditures and unstable farm incomes can intensify vulnerability even when yields do not fall below critical thresholds. Adaptation, particularly irrigation and input intensification, often requires substantial financial investment in mountain regions where infrastructure costs are high. This dynamic produces a cost-resilience paradox i.e. maintaining stable production under climatic stress requires higher expenditure, yet increased spending simultaneously heightens financial exposure. Adaptation does not eliminate vulnerability; rather, the rising burden of input costs can offset the gains from yield stabilisation and income improvement. Second, the findings show that embedded structural and spatial differences produce adaptation inequality. Asset endowments, particularly landholding size, consistently structure households’ ability to translate adaptation efforts into yield stability, investment capacity, and income security. Education further differentiates adaptation pathways by enhancing households’ capacity to engage with technologies, markets, and institutional networks, even where direct productivity gains are not evident. Agro-climatic location likewise mediates adaptation outcomes, indicating that resilience is partly shaped by spatial dynamics. Together, these factors demonstrate that adaptation capacity is relational and uneven, rooted in the distribution of assets, knowledge, and locational advantage rather than solely in climatic exposure. Adaptation therefore operates less as a uniform technical adjustment and more as a socially differentiated process that can consolidate advantage for resource-endowed households while reinforcing vulnerability among marginal farmers. Institutional limitations further constrain adaptation. Limited reliance on formal extension services and climate advisories indicates uneven governance penetration in mountain peripheries. In rainfed cereal systems where sowing timing, varietal choice, and moisture management are critical, absence of timely institutional guidance increases uncertainty. Farmers’ reliance on experiential knowledge reflects embedded resilience, yet under accelerating climatic change and inherent economic vulnerability such knowledge may be insufficient to support systemic transformation. From a regional environmental change perspective, climate variability in the Western Himalayas operates through intertwined ecological and socio-economic drivers rather than as an isolated biophysical stress. The Jammu & Kashmir case demonstrates that vulnerability in rainfed cereal systems is structured by land inequality, rising adaptation costs, partial irrigation access, labour mobility, and market integration. Climate stress thus amplifies pre-existing agrarian hierarchies, producing differentiated adaptation trajectories that range from diversification and migration to intensified vulnerability. Equitable transformation in mountain farming systems therefore requires structurally grounded strategies that address asset disparities, ecological constraints, and institutional precarity alongside climatic risk. 6. Policy Implications The results indicate that climate change adaptation policy in mountain rainfed cereal farming systems must be explicitly designed around economic vulnerability rather than assuming uniform adaptive capacity. In regions such as Jammu & Kashmir, smallholders operate under land fragmentation, limited irrigation access, weak market integration, and constrained credit systems. Adaptation support must therefore extend beyond subsidised inputs toward strengthening livelihood security. Expanding institutional credit tailored to rainfed farmers, improving decentralised storage and aggregation facilities, and enhancing physical market connectivity are critical to reducing structural constraints. Second, agricultural transition strategies must be ecologically and socially grounded. Rainfed cereal systems in mountain regions cannot be treated as residual sectors within national adaptation planning. Policies promoting crop substitution or commercial diversification should be introduced gradually, supported by risk assessments, extension services, and assured market linkages to prevent destabilisation of subsistence-oriented households. Third, governance architecture requires strengthening at the regional scale. Decentralised extension systems, empowered local institutions, and farmer cooperatives can improve access to timely climate information, adaptive technologies, and financial services. Reducing administrative fragmentation and transaction costs is particularly important in geographically dispersed mountain settings. Fourth, climate adaptation should be integrated with social protection. Employment guarantees, targeted subsidies, price support mechanisms, and disaster compensation frameworks can buffer climate shocks and prevent distress migration or land abandonment. Finally, monitoring frameworks must incorporate vulnerability indicators such as asset stability, income diversification, and institutional access rather than relying solely on productivity metrics. Building resilience in mountain rainfed agriculture requires coordinated regional development strategies that address economic precarity alongside climatic risk. 7. Conclusion This study advances understanding of climate change adaptation practices in the context of Himalayan mountain agriculture by empirically demonstrating that economic vulnerability mediates adaptation outcomes in rainfed cereal systems of the Western Himalayas in Jammu & Kashmir. Consistent with vulnerability and livelihoods scholarship, the findings show that climatic exposure translates into differentiated household outcomes through unequal access to land, assets, institutional support, and livelihood diversification opportunities. In this context, rainfed cereal farming is embedded within structural constraints such as fragmented landholdings, limited irrigation, weak market integration, and infrastructural deficits that shape both economic risk and adaptive capacity. By addressing the relative scarcity of systematic evidence from Jammu & Kashmir, the study contributes region-specific insight to Himalayan adaptation literature that has largely focused on Uttarakhand or trans-Himalayan contexts. The results underscore that resilience in mountain cereal systems depends less on technological adoption alone than on reducing underlying economic vulnerability. Integrating vulnerability analysis into regional climate governance is therefore essential for strengthening sustainable adaptation in fragile Western Himalayan rainfed cereal farming systems. Declarations Funding The authors did not receive support from any organization for the submitted work. Conflicts of interest The authors declare they have no financial interests. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Clinical trial number Clinical trial number - not applicable.’ Consent to Participate Declaration All participants were informed about the purpose of the study and voluntarily agreed to participate. Informed consent was obtained from all individual participants included in the study. Data Availability Statement Data sharing not applicable to this article as no datasets were generated or analysed during the current study. Ethics Declaration This study did not require formal approval from an institutional ethics committee, as it did not involve any sensitive personal data, invasive procedures, or vulnerable populations. The participants were adult farmers voluntarily participating in the study, and no questions of a sensitive or personal nature were asked. All procedures were conducted in accordance with the ethical standards of social science research and the principles outlined in the Declaration of Helsinki, 1964. Prior to data collection, informed consent was obtained from all participants, and their anonymity and confidentiality were strictly maintained throughout the study. References Adger WN (2006) Vulnerability. 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Nat Commun 16(1):2108. https://doi.org/10.1038/s41467-025-12108-5 Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, Garnett T, Tilman D, DeClerck FAJ, Wood A, Jonell M, Clark M, Gordon LJ, Fanzo J, Hawkes C, Zurayk R, Rivera JA, De Vries W, Majele Sibanda L, Murray CJL (2019) Food in the Anthropocene: The EAT-Lancet Commission on healthy diets from sustainable food systems . The Lancet, 393 (10170), 447–492. https://doi.org/10.1016/S0140-6736(18)31788-4 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8887579","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610804920,"identity":"160c6d69-e269-4c41-bf84-f59ba257dfef","order_by":0,"name":"Binny Sharma","email":"","orcid":"","institution":"Vikram University","correspondingAuthor":false,"prefix":"","firstName":"Binny","middleName":"","lastName":"Sharma","suffix":""},{"id":610804923,"identity":"a921cc74-1ff3-40d5-8328-66c1cee610bd","order_by":1,"name":"Ganta Durga Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDCCAyg8Axsgwdh4AJtKbFoYGxgK0sA0KVo+HMZiNRrgu5H7+MXPPXZy8u5njz/4YXDebm37YaAtNTbRuLRI3kg3s+x5lmxseCYvsbHH4HbytjOJQC3H0nIbcGgxuJHGZsBzgDlxY0OOYQMPUIvZAaAWxobDeLUY/jlQn7ix/41h4x+Dc8lm5x8S1ML8mOfA4cT5EjmGzTwGB+zMbhCwRfLMMzZmmQPHjQ0k3hjOljFITjC7AbQlAY9f+I6nMX98c6BaTr4/x+Djmz929mbn0x8++FBjg1MLELBJgF14AMJLBKtMwK0cBJg/gEh5qKH2+BWPglEwCkbBSAQAhmFrGGM0D18AAAAASUVORK5CYII=","orcid":"","institution":"Central University of Kerala","correspondingAuthor":true,"prefix":"","firstName":"Ganta","middleName":"Durga","lastName":"Rao","suffix":""}],"badges":[],"createdAt":"2026-02-15 17:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8887579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8887579/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106016895,"identity":"4f622ee9-bab0-405c-88d4-6504789d1bd2","added_by":"auto","created_at":"2026-04-02 12:57:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1969307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8887579/v1/7c917a62-fcee-4873-968c-dd3b424d5456.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate Change, Economic Vulnerability and Unequal Adaptation in Rainfed Cereal Farming Systems in the Western Himalayas","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMountain regions cover nearly one quarter of the world\u0026rsquo;s land surface, support about 12 percent of the global population, and offer critical ecosystem services such as water provision, biodiversity conservation and climate regulation (Paudel et al., 2022; Wani \u0026amp; Marothia, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Characterised by steep topography, sharp altitudinal gradients, diverse microclimates, predominantly rainfed agriculture, and high exposure to extreme events, these regions are among the most climate-sensitive socio-ecological systems in the world (FAO, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hussain \u0026amp; Guha, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; K\u0026ouml;rner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Romeo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wani \u0026amp; Marothia, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFood insecurity is significantly more pronounced in mountain regions than in plains, particularly in developing countries (Bandara et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Poudel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This heightened food insecurity is closely linked to the structural and bio-physical constraints facing mountain agriculture (FAO, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; K\u0026ouml;rner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Harsh terrain, land degradation, climate-induced disasters, weak infrastructure, limited market access, and poor institutional support jointly constrain agricultural productivity and livelihood security (Paudel et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Romeo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shaheen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Of the estimated 648\u0026nbsp;million people living in mountain areas of developing countries, around 346\u0026nbsp;million were vulnerable to food insecurity by 2017, reflecting the scale and persistence of vulnerability in these regions (Romeo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Asia alone accounts for nearly one-third of the world\u0026rsquo;s mountain area and over half of the global mountain population, with South Asia experiencing a sharp rise in food insecurity among mountain dwellers over the past two decades (Bandara et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA substantial share of food insecurity among South Asia\u0026rsquo;s mountain dwellers is concentrated in the Himalayan mountain system, the largest and most densely inhabited mountain region in the subcontinent, with distinct climatic and livelihood vulnerabilities (Dhimal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pandey, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rasul et al., 2019). A central driver of this food insecurity is the region\u0026rsquo;s acute agricultural vulnerability to climate change (Dahal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dhimal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Within this broader Himalayan context, the Western Himalayas, has been identified as a sub-region where climatic and livelihood vulnerabilities are most acutely translated into household-level risk (Kapruwan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Namgyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context of food insecurity in mountain regions, cereal-based farming systems, particularly cultivation of rice, wheat, and maize represent some of the most climate-sensitive agricultural livelihood systems. In these regions, climate change manifests not only through biophysical impacts on crop yields but also through economic consequences such as yield variability, rising input costs, unstable market returns for farming households (Bandara et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dahal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wani \u0026amp; Marothia, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Together, these pressures erode farm incomes, increase indebtedness, and limit the ability of households to withstand climate shocks and invest in adaptive measures (Namgyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese economic impacts can be conceptualised as economic vulnerability, which links climate risks to livelihood insecurity by capturing how shocks translate into income losses, welfare decline, and constrained adaptive responses (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Intergovernmental Panel on Climate Change (IPCC), 2022; Ribot, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Importantly, economic vulnerability also explains differential outcomes among farmers through unequal adaptation capacities. While some households are able to diversify, adopt improved technologies, and access institutional support, others remain constrained by limited assets and opportunities, resulting in unequal adaptation outcomes within the same agro-ecological setting (Pandey, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paudel et al., 2022).\u003c/p\u003e \u003cp\u003eThe cereal-based farming systems in the Western Himalayas present a critical case where climate change acts as a risk multiplier by interacting with pre-existing socio-economic, institutional, and biophysical constraints. Rising temperatures, erratic precipitation, and extreme events intersect with non-climatic stressors such as male out-migration, labour shortages, insecure land tenure, land degradation, limited access to inputs and extension services, and weak market integration, leading to yield instability, income volatility, and rising production costs (O\u0026rsquo;Brien \u0026amp; Leichenko, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Poudel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Differences in economic resources, institutional access, and livelihood diversification shape farmers\u0026rsquo; capacity to respond, producing unequal adaptation outcomes within the same agro-ecological setting. Economic vulnerability thus functions as the key intermediary through which climate variability is translated into differentiated adaptation outcomes, rather than being a secondary effect of biophysical change (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dercon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ribot, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present study examines economic vulnerability and unequal adaptation capabilities among rainfed cereal farming households in the Western Himalayas, focusing on how differential access to resources and institutions mediates climate risk and livelihood outcomes. Accordingly, the study is guided by the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1. How does climate variability affect economic vulnerability among rainfed cereal farming households in the Western Himalayas of Jammu \u0026amp; Kashmir?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e2. How do differences in access to assets, institutions, and livelihood opportunities shape households\u0026rsquo; adaptation capacities and strategies?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e3. In what ways does economic vulnerability mediate unequal adaptation outcomes within the same agro-ecological setting?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn this study, economic vulnerability is framed as the susceptibility of farming households to income and livelihood losses stemming from exposure to climatic and non-climatic shocks, combined with limited capacity to cope, adapt, or recover (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chambers, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Dercon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In climate-sensitive agricultural systems, economic vulnerability extends beyond short-term income fluctuations to encompass medium-term and long-term challenges such as yield uncertainty, rising input costs, indebtedness, and constrained investment in adaptive measures (FAO, 2016; IPCC, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For rainfed cereal farming systems in the Western Himalayas, economic vulnerability thus reflects the interaction of structural constraints and unequal access to assets, institutions, and risk-buffering mechanisms, which together shape unequal adaptation capabilities.\u003c/p\u003e \u003cp\u003eThe study is grounded in an integrated vulnerability-livelihoods framework that links climate change, economic vulnerability, and unequal adaptation outcomes within the cereal-based farming systems of the Western Himalayas. In line with vulnerability theory, climate change affects farming households through the combined effects of exposure to climatic variability, sensitivity of rainfed cereal systems, and differences in adaptive capacity (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Drawing on the sustainable livelihoods approach and political economy perspectives, the study conceptualises adaptation as a socially differentiated process shaped by unequal access to natural, physical, human, financial, and institutional assets, as well as by governance and power relations (Adger et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Chambers \u0026amp; Conway, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; DFID, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ribot, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Review of literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Economic vulnerability in agrarian and mountain livelihoods\u003c/h2\u003e \u003cp\u003eCereal-based farming systems occupy a central position in global food production, food security, and rural livelihoods, particularly in low- and middle-income countries across Africa, Asia, and Latin America (Kruseman et al., 2020). Cereals such as rice, wheat, and maize provide the primary sources of dietary energy and protein for large populations, contribute substantially to nutritional security, and generally exhibit lower environmental footprints compared to many alternative food systems (Kruseman et al., 2020; Willet et al., 2019). At the household level, cereal cultivation also constitutes a major source of income and employment for millions of smallholder farmers and rural workers (Reddy \u0026amp; Rahut, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, projections indicate that rising cereal demand will coincide with declining per-capita land availability and increasing climatic stress, heightening the economic vulnerability of cereal-dependent farming households (Kropff \u0026amp; Morell, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVulnerability frameworks emphasise that farming households dependent on climate-sensitive crops experience increased income uncertainty when exposure to climatic shocks combines with limited access to assets, credit, insurance, and institutional support (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chambers, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Dercon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This vulnerability is particularly noticeable in rainfed cereal systems, which account for nearly 60% of global cereal crop area but contribute a disproportionately smaller share of total output due to persistent yield gaps caused by climatic stress, soil degradation, and management constraints (Anderson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a result, rainfed cereal-based farming systems are characterised by high production volatility and economic risk.\u003c/p\u003e \u003cp\u003eIn mountain regions, these vulnerabilities are amplified by ecological fragility, limited arable land, and weak infrastructure. Mountain cereal farming is generally constrained by steep slopes, fragile soils, poor irrigation coverage, and restricted market access, intensifying income uncertainty under climate stress (FAO, 2016; K\u0026ouml;rner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Empirical evidence indicates that rainfed cereals in mountain regions exhibit high sensitivity to rainfall variability and temperature fluctuations, leading to yield declines and heightened livelihood risk for marginal and small farmers (Bouras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Willet et al., ). Accordingly, economic vulnerability in mountain agrarian systems is closely linked to the structural characteristics of rainfed cereal-based farming systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Unequal adaptation capacities and strategies in mountain agriculture\u003c/h2\u003e \u003cp\u003eAdaptation within cereal-based farming systems is increasingly recognised as a socially differentiated process, shaped by unequal access to land, capital, technology, information, and institutions (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Alimagham et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ribot, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While climate-smart practices such as drought- and heat-tolerant cereal varieties, improved irrigation scheduling, conservation tillage, and crop diversification are widely promoted as pathways to enhance resilience and income stability, adoption remains uneven across farming households (Hussain \u0026amp; Guha, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kropff \u0026amp; Morell, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Households with greater resource endowments and institutional access are better positioned to adopt such productivity-enhancing adaptations, while resource-poor farmers often rely on short-term coping strategies that do little to reduce long-term economic vulnerability (Dercon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rao \u0026amp; Sharma, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn rainfed cereal systems, unequal adaptation capacities are clearly evident among farming households. At the global level, studies show that low-input cereal farming under erratic rainfall and weak extension services results in chronically low and unstable yields, with yield gaps between actual and potential production ranging from 0.5 to over 5 t/ha in Sub-Saharan Africa (Alimagham et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Anderson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In North Africa, rainfed wheat and barley systems exhibit high sensitivity to drought and warming, leading to strong inter-annual yield fluctuations and income instability (Benmehaia et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bouras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Even in relatively advanced cereal production systems in southern Europe, such as Spain, rising temperatures, declining rainfall, and land degradation are increasing climate-induced risks, underscoring that adaptation capacity remains uneven across regions (Rivas-Tabares et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMountain cereal farmers experience additional constraints that further differentiate adaptation outcomes. Evidence indicates that farmers with access to irrigation, improved cereal varieties, extension services, and institutional support are more likely to adopt yield-stabilising and income-enhancing strategies, whereas marginal farmers depend on migration, borrowing, consumption reduction, or crop abandonment to cope with climate stress (Poudel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rao \u0026amp; Sharma, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In South Asia, adaptation through crop substitution, such as shifting land from climate-sensitive rice to alternative cereals has been shown to reduce climate-induced losses or increase farm profits, but such transitions are feasible primarily for farmers with sufficient land, capital, and market access (Hussain \u0026amp; Guha, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings underscore that adaptation in cereal-based farming systems is highly unequal and closely linked to economic vulnerability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research gap\u003c/h2\u003e \u003cp\u003eIn the Himalayan region, cereal-based farming systems, particularly rainfed wheat, maize, and millets form the backbone of rural livelihoods but are highly exposed to climate variability and structural constraints. Studies from Nepal and India document how climate stress in rainfed cereal systems translates into food insecurity, income instability, and differentiated livelihood outcomes among smallholder farmers (Poudel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shaheen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research from Ladakh highlights how access to irrigation, livestock, and state support enables some households to stabilise cereal production and incomes, while others remain highly vulnerable (Namgyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, evidence from Uttarakhand underscores the role of institutional interventions such as watershed development, extension services, and government schemes in shaping adaptation outcomes in cereal-based mountain agriculture (Kapruwan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wani \u0026amp; Marothia, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this growing body of work, empirical evidence on economic vulnerability and unequal adaptation in cereal-based farming systems in the Western Himalayas remains geographically uneven. Existing studies are concentrated largely in Uttarakhand (Kapruwan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), with limited systematic analysis from the Jammu \u0026amp; Kashmir region of the Western Himalayas. While Namgyal et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provide important insights from the trans-Himalayan context of Ladakh, the agro-ecological, institutional, and socio-political conditions of Jammu \u0026amp; Kashmir differ markedly from both Uttarakhand and Ladakh in terms of land tenure systems, irrigation dependence, market integration, governance structures, and exposure to climatic risks. These conditions also vary significantly across sub-regions within Jammu and Kashmir. Consequently, the pathways through which climate change affects cereal production, household incomes, and adaptation capacities in Jammu \u0026amp; Kashmir remain insufficiently examined. This spatial and contextual gap limits the generalisability of the conclusions drawn from the existing studies and highlights the need for region-specific empirical evidence. The present study responds to this gap by providing empirical insights from the Western Himalayas of Jammu \u0026amp; Kashmir.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study area, sampling design and data collection\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Western Himalayan region, focusing on the three (temperate, intermediate, and subtropical) agro-climatic zones within the Jammu Division of Jammu and Kashmir, India. A multi-stage disproportionate stratified random sampling method was employed to ensure balanced representation across agro-climatic zones. In the first stage, Rajouri and Kathua districts were purposively selected from the ten districts of the Jammu Division, as they are the only districts having all three agro-climatic zones. This unique agro-climatic diversity enables comparative analysis of climate exposure, economic vulnerability, and adaptation outcomes across ecological gradients within a broadly similar institutional and socio-political setting. According to the 10th Agriculture Census (2015\u0026ndash;16), Rajouri and Kathua together account for 158,150 operational landholdings, representing over one-fourth of all operational holdings in the Jammu Division.\u003c/p\u003e \u003cp\u003eIn the second stage of sampling, twelve tehsils (six per district) were randomly selected from Rajouri and Kathua districts to cover all three agro-climatic zones. In the final stage, 34 cereal-farming households were randomly selected from each tehsil, resulting in a total sample of 408 households evenly distributed across agro-climatic zones and districts. The minimum sample size (384) was estimated using Cochran\u0026rsquo;s formula at a 95% confidence level and a 5% margin of error. The minimum sample was increased to 408 to enhance representativeness and account for potential non-response. Equal distribution of households across agro-climatic zones was adopted despite differences in population size to enable systematic comparison of vulnerability and adaptation outcomes. This strategy aligns with the study\u0026rsquo;s theoretical focus on differentiated exposure and unequal adaptive capacity of households and compensates for the absence of reliable sub-district-level population data.\u003c/p\u003e \u003cp\u003eFollowing sampling process, a cross-sectional household survey design was employed to analyse climate-induced economic vulnerability and variability in adaptation practices in rainfed cereal farming systems. Primary data were collected in 2025 using an interview schedule that captured socio-economic characteristics, production outcomes, climate perceptions, economic vulnerability, adaptation practices, institutional access, and livelihood diversification. Responses were recorded by the researcher during the interviews.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measurement of key variables\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Economic vulnerability indicators\u003c/h2\u003e \u003cp\u003eConsistent with the vulnerability-livelihoods framework, economic vulnerability was operationalised using three outcome dimensions (yield vulnerability, input-cost vulnerability, and income vulnerability) reflecting exposure, sensitivity, and adaptive capacity. Yield vulnerability was measured as a binary variable, coded as 1 for households producing\u0026thinsp;\u0026ge;\u0026thinsp;10 quintals of the main cereal crop and 0 for those producing\u0026thinsp;\u0026lt;\u0026thinsp;10 quintals. Input-cost vulnerability was captured through annual agricultural input expenditure incurred for the main cereal crop, coded as 1 for expenditures exceeding ₹20,000 and 0 for expenditures at or below ₹20,000. Income vulnerability was assessed using farm income derived from the cultivation of the main cereal crop, coded as 1 for households earning more than ₹50,000 and 0 for those earning ₹50,000 or less. These thresholds were determined based on sample distributions and regional farming statistics and represent economically meaningful cut-offs within smallholder-dominated rainfed cereal farming systems in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Climate stress perception\u003c/h2\u003e \u003cp\u003eFarmers\u0026rsquo; perceptions of climate change were measured with the help of a set of binary indicators capturing perceived changes in temperature, rainfall, and other climatic phenomena. In addition, perceived severity of temperature rise and the perceived percentage decline in rainfall were recorded using a 10-point Likert scale. Together, these indicators capture experienced climate exposure and sensitivity at the household level and are consistent with the IPCC vulnerability framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Adaptation practices\u003c/h2\u003e \u003cp\u003eAdaptation practices adopted by farming households were classified into incremental, systemic, and transformational strategies following climate adaptation theory. Incremental adaptations include adjustments in sowing practices (e.g., repeated sowing, altered labour scheduling, additional input use) and moisture conservation practices (mulching, bunding, and moisture pits). Systemic adaptations comprise crop diversification and adoption of improved irrigation and agricultural machinery. Transformational adaptations include crop switching and off-farm migration. Each adaptation variable was coded as a binary indicator (1\u0026thinsp;=\u0026thinsp;adopted, 0\u0026thinsp;=\u0026thinsp;not adopted), enabling assessment of their differential effects on yield, input expenditure, and farm income.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Structural and human capital controls\u003c/h2\u003e \u003cp\u003eTo capture unequal adaptive capacity across households, the analysis incorporated structural and human capital variables such as education level, agro-climatic zone, landholding size, and age. Education and landholding size represent human and natural capital endowments, while agro-climatic zone captures spatial exposure to climatic stress. Age is treated as a life-cycle indicator reflecting experience, risk preferences, and labour capacity, and is therefore used only in analyses examining adaptation behaviour and outcomes rather than in baseline vulnerability estimations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analytical strategy\u003c/h2\u003e \u003cp\u003eThe empirical analysis was conducted in four stages. First, descriptive statistics (frequencies and percentages) were used to present socio-economic characteristics, farm structure, income levels, and institutional access, thereby establishing a baseline assessment of economic vulnerability among rainfed cereal-farming households. Second, cross-tabulations and chi-square tests were employed to examine variation in climate change perceptions, measured using the variables defined in section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.2.2\u003c/span\u003e, across agro-climatic zones, locations, age groups, and education levels, enabling the identification of socially and spatially differentiated patterns of exposure and awareness.\u003c/p\u003e \u003cp\u003eThird, binary logistic regression models were employed to assess the structural determinants of economic vulnerability across the outcome dimensions defined in section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.2.1\u003c/span\u003e (yield vulnerability, input-cost vulnerability, and income vulnerability). The models included education, agro-climatic zone, and landholding size as predictors representing human capital, spatial exposure, and asset endowments, respectively. Finally, to examine unequal adaptation outcomes, adaptation variables discussed in section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3.2.3\u003c/span\u003e were incorporated into the regression models alongside the structural controls and age. This analysis assessed whether adaptation practices mitigate or redistribute economic vulnerability by influencing yields, production costs, and income levels, consistent with a political-economy perspective on differentiated and unequal adaptation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Limitations of the Study:\u003c/h2\u003e \u003cp\u003eWhile this study offers valuable insights into the economic vulnerability aspects of rainfed cereal farming systems in Western Himalayas, several limitations should be noted. The research was confined to two districts, which may not fully represent the diversity of farming systems across the Western Himalayas. Its cross-sectional design limits the ability to establish long-term causal relationships between climate variables and farming outcomes. The analysis relied primarily on farmers\u0026rsquo; perceptions, which, while crucial for understanding adaptation behaviors, may not always correspond with meteorological data.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Socio-economic profile and baseline vulnerability of sample households\u003c/h2\u003e \u003cp\u003eThe socio-economic profile of sampled households presents interlinked structural and human vulnerabilities that shape differentiated exposure and sensitivity to climate change in rainfed cereal farming systems in the Western Himalayas. The farming population is ageing, with 51 per cent of respondents above 50 years and limited youth participation. This demographic tilt reflects declining agricultural viability amid climatic uncertainty. It also weakens long-term adaptive capacity of farmers by constraining labour availability, innovation uptake, and generational renewal.\u003c/p\u003e \u003cp\u003eAgriculture is male-dominated (83.8%), reflecting persistent gender asymmetries in land ownership, decision-making, and institutional recognition. The limited reported participation of women in farming activities indicates structural exclusion, reinforcing gendered vulnerability in access to resources and adaptation opportunities.\u003c/p\u003e \u003cp\u003eMarginalised groups such as Scheduled Tribes (40 per cent) and Scheduled Castes (19.1 per cent) constitute a majority of respondent households, highlighting the intersection of social identity with agrarian vulnerability. These households often face cumulative constraints related to land quality, market integration, credit access, and institutional support.\u003c/p\u003e \u003cp\u003eEducation, as a human capital indicator shows that the majority of respondents possess non-technical formal qualifications, with only slightly over 10 per cent having technical education. Limited technical education restricts farmers\u0026rsquo; ability to access climate information, employ sustainable practices, and engage with extension systems, resulting in informational and technological disadvantages.\u003c/p\u003e \u003cp\u003eThe data reveal that more than 90 per cent of households are engaged in supplementary activities signalling widespread diversification with heavy reliance on casual labour and informal employment. While diversification can enhance resilience, its prevalence in this context reflects distress-driven coping rather than livelihood improvement, exposing households to income volatility and seasonal insecurity.\u003c/p\u003e \u003cp\u003eThe high degree of land fragmentation, with more than 83 per cent of farmers operating on less than two hectares, reinforces structural vulnerability. Small holdings limit economies of scale and constrain farmers\u0026rsquo; capacity to absorb climatic and market risks, while the predominance of rainfed cultivation (40%) heightens sensitivity to rainfall variability. Nearly three-fourths of respondents harvest less than 10 quintals of their main crop, indicating low productivity across the region. Further, low input expenditure (₹20,000 or less for 72.1% of households) reflects limited investment capacity, pointing to a persistent low-input\u0026ndash;low-output trap.\u003c/p\u003e \u003cp\u003eIncome levels show economic precarity, with more than half of the respondents earning less than ₹50,000 annually from the main cereal crop, indicating the limited viability of agriculture as a primary source of livelihood. Institutional vulnerability compounds these challenges, as nearly 70 per cent of farmers reported rare or no access to extension services, restricting access to climate advisories and risk-management options.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-economic characteristics of sample households (N\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Backward Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScheduled Caste (SC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScheduled Tribe (ST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducational Qualification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal education (non-technical, below secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education and above (non-technical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical education (any level)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eOther occupation(s) besides farming\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate sector job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed/Business\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily wage labor (non-agricultural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo other occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLandholding size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 hectare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 2 hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eType of farming practiced\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfed (non-irrigated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTotal yield of main crop last year (quintals)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 10 quintals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 and more quintals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAnnual expenditure on inputs (INR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,000 and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,001 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAnnual income from farming (INR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eTotal household income (INR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50,001\u0026ndash;1,00,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,00,001\u0026ndash;2,00,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,00,001\u0026ndash;5,00,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 5,00,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eAccess to extension services\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Primary Data, 2025.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Perceived climate stress and variability\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Descriptive patterns of perceived climate variability\u003c/h2\u003e \u003cp\u003eClimate stress is experienced as an immediate and lived reality as reflected in the farmers\u0026rsquo; widespread and consistent perceptions of climatic change. All respondents (100%) reported noticeable climatic changes over the past 10\u0026ndash;20 years, a pattern attributable to high awareness shaped by direct exposure rather than institutional information.\u003c/p\u003e \u003cp\u003eFarmers across agro-climatic zones reported consistent temperature and rainfall variability, whereas other meteorological changes (strong winds, cloud cover and snowfall) were largely local in nature. A majority (75.7%) perceived an overall increase in temperature, with 40.2% noting hotter summers and 32.8% reporting milder or shorter winters. Over half of the respondents reported declining rainfall (52.9%) and delayed monsoon onset (50.7%), while 43.6% observed increasingly erratic rainfall patterns. Only 22.8 per cent farmers reported a reduction in the number of rainy days, suggesting that vulnerability is driven primarily by disruptions in rainfall timing rather than absolute scarcity, with direct implications for sowing, crop establishment, and yields.\u003c/p\u003e \u003cp\u003eThe perceptions of respondents also highlight the intensity of climate stress. More than half of the respondents (57.6%) rated summer temperature increases at six or above on a ten-point scale, indicating moderate to high heat stress. Similarly, a substantial majority (83.8%) rated the decline in rainfall at six or above on the same ten-point scale, suggesting that perceived rainfall stress is both pronounced and widespread. These patterns reinforce that vulnerability arises not merely from variability, but from the experienced severity of climatic changes.\u003c/p\u003e \u003cp\u003eFarmers\u0026rsquo; climate perception is shaped predominantly by experiential and social sources. Personal experience (85.5%), family traditions (80.9%), and peer interactions were identified as the most influential, while extension services and training programmes were widely perceived as weak or absent. This reliance on lived experience reflects both limited institutional reach and the necessity for farmers to rely on personal knowledge and observation to interpret climate risk in highly uncertain environments such as the Western Himalayas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFarmers\u0026rsquo; perceptions, intensity, and influencing factors of climate change (N\u0026thinsp;=\u0026thinsp;408) A. Perception of specific climatic changes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecific change observed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes (f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo (f, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall increase in temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309 (75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotter summers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244 (59.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilder/shorter winters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e274 (67.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201 (49.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErratic rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230 (56.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFewer rainy days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e315 (77.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eOther changes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong winds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278 (68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130 (31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced cloudiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331 (81.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChanges in snowfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322 (78.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther meteorological changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e397 (97.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eB. Perceived intensity of climate stress (10-point scale)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Stress Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRated 6 and Above (f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRated Below 6 (f, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer temperature increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eC. Influencing factors of climate change perception\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost\u003c/p\u003e \u003cp\u003eInfluential\u003c/p\u003e \u003cp\u003e(f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfluential\u003c/p\u003e \u003cp\u003e(f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003cp\u003e(f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003cp\u003eInfluential\u003c/p\u003e \u003cp\u003e(f, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot\u003c/p\u003e \u003cp\u003eInfluential\u003c/p\u003e \u003cp\u003e(f, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e349 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia/news\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41 (10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeer farmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension officers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116 (28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining/workshops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e241 (59.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily traditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Primary Data, 2025.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Differential perceptions across agro-climatic zones, locations, age and education\u003c/h2\u003e \u003cp\u003eChi-square analysis shows that perceptions of farmers regarding rainfall (declining rainfall, delayed monsoon onset, erratic rainfall) and other meteorological changes (strong winds, and changes in snowfall) differ across agro-climatic zones, locations, age and education. In contrast, temperature-related perceptions (overall warming, hotter summers, and shorter winters) do not show significant variation across these spatial and social categories, suggesting a widely shared perception of warming throughout the region.\u003c/p\u003e \u003cp\u003eRainfall-related indicators (declining rainfall, delayed monsoon onset, erratic rainfall) and other meteorological changes (strong winds, and changes in snowfall) varied significantly across agro-climatic zones (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subtropical zones reported greater rainfall irregularity, while temperate and intermediate zones more acutely perceived declining snowfall, reflecting zone-specific exposure to climatic stressors.\u003c/p\u003e \u003cp\u003eAll rainfall and other meteorological indicators varied significantly across tehsils (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with particularly high chi-square values for rainfall variability and snowfall changes. This pronounced location specific variation highlights the importance of local climatic conditions in shaping lived experiences of climate stress and cautions against regionally uniform vulnerability assessments.\u003c/p\u003e \u003cp\u003eAge-based differences also shaped farmers\u0026rsquo; perceptions. Older farmers were more likely to perceive delayed monsoons, milder winters, strong winds, and changes in snowfall, suggesting that long-term experiential knowledge enhances sensitivity to gradual and seasonal climatic shifts. Younger farmers, by contrast, appeared less cognizant of such changes, potentially limiting anticipatory adaptation practices.\u003c/p\u003e \u003cp\u003eEducation also exerted a considerable influence on farmers\u0026rsquo; perceptions. More educated farmers were more likely to perceive rainfall decline, erratic rainfall, fewer rainy days, and changes in snowfall. This indicates that education enhances awareness of complex and less immediately visible climatic processes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: Chi-square results of farmers\u0026rsquo; perceptions across agro-climatic zones and locations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZones χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLocations χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotter summers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilder/shorter winters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErratic rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFewer rainy days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMeteorological\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003echanges\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong winds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced cloudiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChanges in snowfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e243.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Primary Data, 2025.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Chi-square results of farmers\u0026rsquo; perceptions across age and education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEducation χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotter summers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMilder/shorter winters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErratic rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFewer rainy days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMeteorological\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003echanges\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong winds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced cloudiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChanges in snowfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Primary Data, 2025.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Determinants of yield, input-cost, and income vulnerability among farmers in the Jammu Division\u003c/h2\u003e \u003cp\u003eThe regression results reveal how structural inequalities condition economic vulnerability in rainfed cereal farming systems of the Jammu Division. Landholding size emerges as the most consistent determinant of yield, investment behaviour, and income generation, while education and agro-climatic zones exert differentiated effects on the three outcome dimensions under conditions of climatic change. The results highlight the uneven distribution of resources and adaptive capacities across social and spatial outcome dimensions.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Yield vulnerability\u003c/h2\u003e \u003cp\u003eLandholding size emerges as a key determinant of yield. Compared to marginal farmers (\u0026lt;\u0026thinsp;1 ha), farmers operating 1\u0026ndash;2 hectares (AOR\u0026thinsp;=\u0026thinsp;4.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and more than 2 hectares (AOR\u0026thinsp;=\u0026thinsp;3.28, p\u0026thinsp;=\u0026thinsp;0.003) were significantly more likely to achieve yields of at least 10 quintals. This shows the importance of larger landholdings in facilitating input use, manging crops and coping with risk.\u003c/p\u003e \u003cp\u003eIn contrast, education and agro-climatic zones were not significantly associated with yield. Although, farmers with secondary education and above showed higher odds of achieving better yields, the association was not statistically significant. These findings suggest that structural inequalities in land access are the primary drivers of yield vulnerability, outweighing differences in human capital and agro-climatic zones, and thereby shaping differential capacity to withstand climatic variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Input-cost vulnerability\u003c/h2\u003e \u003cp\u003eLandholding size emerged as a strong determinant of input-cost vulnerability. Farmers with 1\u0026ndash;2 hectares (AOR\u0026thinsp;=\u0026thinsp;2.74, p\u0026thinsp;=\u0026thinsp;0.003) and more than 2 hectares (AOR\u0026thinsp;=\u0026thinsp;2.21, p\u0026thinsp;=\u0026thinsp;0.027) were significantly more likely to incur higher input costs, reflecting both larger scale of operation and greater capacity to invest.\u003c/p\u003e \u003cp\u003eA significant association is found between education and input expenditure. Farmers with primary education (AOR\u0026thinsp;=\u0026thinsp;1.86, p\u0026thinsp;=\u0026thinsp;0.046) and secondary education and above (AOR\u0026thinsp;=\u0026thinsp;3.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were more likely to spend on agricultural inputs, suggesting that education enhances awareness, planning ability, and willingness to adopt adaptive practices.\u003c/p\u003e \u003cp\u003eAgro-climatic zone also emerged as a significant determinant. Farmers in the intermediate (AOR\u0026thinsp;=\u0026thinsp;1.96, p\u0026thinsp;=\u0026thinsp;0.032) and subtropical zones (AOR\u0026thinsp;=\u0026thinsp;1.96, p\u0026thinsp;=\u0026thinsp;0.034) were more likely to incur annual input expenditures exceeding ₹20,000 than those in the temperate zone. This indicates higher input intensity and cost pressures in the intermediate and sub-tropical agro-climatic zones, driven by greater rainfall dependence and increased investment in fertilizers, pesticides, and irrigation to buffer against rainfall variability and production uncertainty. Input-cost vulnerability thus reflects not only exposure to agro-climatic pressures but also differentiated investment capacity shaped by landholding size and education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Income vulnerability\u003c/h2\u003e \u003cp\u003eLandholding size also emerged as a strong determinant of income vulnerability. Farmers owning 1\u0026ndash;2 hectares (AOR\u0026thinsp;=\u0026thinsp;15.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and more than 2 hectares (AOR\u0026thinsp;=\u0026thinsp;2.30, p\u0026thinsp;=\u0026thinsp;0.020) exhibited substantially higher odds of earning more than ₹50,000 annually from farming, confirming land access as a central determinant of farm income security.\u003c/p\u003e \u003cp\u003eEducation likewise emerged as a significant determinant. Farmers with primary education (AOR\u0026thinsp;=\u0026thinsp;1.95, p\u0026thinsp;=\u0026thinsp;0.043) and those with secondary education and above (AOR\u0026thinsp;=\u0026thinsp;3.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly more likely to achieve higher farm incomes, possibly reflecting improved managerial efficiency, information access, and market engagement.\u003c/p\u003e \u003cp\u003eAgro-climatic zone also emerged as a significant determinant of income vulnerability. Farmers in the intermediate zone were significantly more likely to cross the income threshold (AOR\u0026thinsp;=\u0026thinsp;2.05, p\u0026thinsp;=\u0026thinsp;0.034) than those in temperate areas. This may indicate relatively favourable agro-ecological conditions, more stable rainfall regimes, and cropping patterns that generate higher marketable surplus. Greater alignment between production potential and market access in the intermediate zone likely enhances income levels. While farmers in the subtropical zone showed higher odds, the association was not statistically significant. Together, these findings indicate that income vulnerability is mediated by structural, human capital and locational factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of yield, input cost and income vulnerability (Binary Logistic Regression)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeterminant Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYield\u0026thinsp;\u0026ge;\u0026thinsp;10 quintals\u003c/p\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInput cost \u0026gt;₹20,000\u003c/p\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome \u0026gt;₹50,000\u003c/p\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (Primary education)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (Secondary \u0026amp; above)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgro-climatic zone (Intermediate)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgro-climatic zone (Sub-tropical)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLandholding size (1\u0026ndash;2 ha)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLandholding size (\u0026gt;\u0026thinsp;2 ha)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratio; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReference categories\u003c/strong\u003e \u003cp\u003eNo formal education (education); Temperate agro-climatic zone (agro-climatic zone); and Landholding size\u0026thinsp;\u0026lt;\u0026thinsp;1 hectare (landholding).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003ePrimary Data, 2025.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Adaptation practices and economic vulnerability among mountain farmers in the Western Himalayas\u003c/h2\u003e \u003cp\u003eBuilding on the preceding analysis of socio-economic and locational determinants of vulnerability, this section examines whether adaptation practices influence yield, input expenditure, and farm income among mountain farmers in the study area. Binary logistic regression models were employed to assess the effects of incremental, systemic, and transformational adaptation strategies on economic outcomes, while controlling for landholding size, education, agro-climatic zone, and age. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Adaptation Practices and Economic Vulnerability) and Table\u0026nbsp;6 (Structural Controls and Economic Vulnerability).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Incremental adaptation and cost-led vulnerability\u003c/h2\u003e \u003cp\u003eThe regression results indicate that adjustments in sowing practices, while widely adopted, are not significantly associated with improvements in yield (AOR\u0026thinsp;=\u0026thinsp;1.21, p\u0026thinsp;=\u0026thinsp;0.18) or income levels (AOR\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.41). However, they significantly increase the likelihood of higher input costs (AOR\u0026thinsp;=\u0026thinsp;1.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that such adjustments may raise production costs without corresponding income gains, thereby intensifying input-cost vulnerability.\u003c/p\u003e \u003cp\u003eMoisture conservation practices not only significantly improve yield outcomes (AOR\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but also substantially raise input expenditure (AOR\u0026thinsp;=\u0026thinsp;1.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, their association with higher income remains statistically weak. These findings suggest that moisture conservation practices improve yield but do not significantly enhance income levels despite higher input costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Systemic adaptation: productivity gains with elevated risk\u003c/h2\u003e \u003cp\u003eCrop diversification is significantly associated with improved yield (AOR\u0026thinsp;=\u0026thinsp;1.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and higher income outcomes (AOR\u0026thinsp;=\u0026thinsp;1.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, it also significantly increases the likelihood of high input expenditure (AOR\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that diversification may enhance production and income while simultaneously raising production costs.\u003c/p\u003e \u003cp\u003eSimilarly, the adoption of improved irrigation and machinery is significantly associated with higher yields (AOR\u0026thinsp;=\u0026thinsp;2.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and substantially higher input expenditure (AOR\u0026thinsp;=\u0026thinsp;3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the association with income is positive, it is not statistically significant. These findings indicate that such capital-intensive adaptations are linked to productivity gains but are also accompanied by increased financial exposure through higher input costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Transformational adaptation and exit-based resilience\u003c/h2\u003e \u003cp\u003eCrop switching is not significantly associated with improvements in yield but is significantly associated with higher income outcomes (AOR\u0026thinsp;=\u0026thinsp;1.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that shifts in cropping patterns may enhance farm income levels without necessarily improving yield.\u003c/p\u003e \u003cp\u003eOff-farm migration demonstrates the strongest association with higher income levels (AOR\u0026thinsp;=\u0026thinsp;2.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, it shows no significant association with yield or input expenditure. These findings indicate that migration operates primarily through income diversification rather than through improvements in agricultural productivity or input expenditure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results: adaptation practices and economic vulnerability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation Practice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYield\u0026thinsp;\u0026ge;\u0026thinsp;10 qtls (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInput Cost \u0026gt;₹20,000 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome \u0026gt;₹50,000 (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjustments in sowing practices (Incremental)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture conservation (Incremental)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop diversification\u003c/p\u003e \u003cp\u003e(Systemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved irrigation/machinery\u003c/p\u003e \u003cp\u003e(Systemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop switching (Transformational)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff‑farm migration\u003c/p\u003e \u003cp\u003e(Transformational)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratio; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReference category: \u003cem\u003eNo adaptation practice adopted\u003c/em\u003e (for each adaptation variable).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003ePrimary Data, 2025.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe results indicate that economic vulnerability mediates adaptation outcomes in mountain rainfed cereal systems. In the Western Himalayan context of Jammu \u0026amp; Kashmir, climate change does not directly or uniformly affect livelihoods. Instead, its impacts are transmitted through yield instability, rising input costs, income volatility, and shifts in labour availability, producing differentiated adaptive capacities within the same agro-ecological setting. Thus, environmental change in this mountain region operates not only through biophysical stressors but also through structural and socio-economic factors that shape households\u0026rsquo; ability to adopt adaptation strategies.\u003c/p\u003e \u003cp\u003eRainfed cereal systems in mountain environments are highly sensitive to climatic variability, including rainfall fluctuations, temperature extremes, frost, and other meteorological stresses. In such systems, climatic factors are not merely environmental inputs but the primary determinants of crop survival and productivity. Even minor deviations in monsoon timing or temperature can cause disproportionate yield fluctuations. At the same time, inherent ecological and structural constraints of mountain rainfed systems such as small and fragmented landholdings, low moisture retention, limited irrigation facilities, fragile slopes, short growing seasons, thin soils, terraced topography, and limited mechanisation restrict the transformative potential of agricultural intensification.\u003c/p\u003e \u003cp\u003eHowever, the results indicate that exposure alone does not determine vulnerability. Instead, household economic conditions reflected in production thresholds, expenditure levels, land distribution, and income stability mediate how climatic variability is experienced and managed. This aligns with W. Neil Adger\u0026rsquo;s argument that vulnerability is socially produced and embedded within institutional and economic arrangements rather than determined solely by climatic stresses. Similarly, the vulnerability framework articulated by the Intergovernmental Panel on Climate Change emphasises that adaptive capacity is shaped by access to assets and institutional support, both of which were unevenly distributed among the sampled households.\u003c/p\u003e \u003cp\u003eWithin this socio-ecological system, economic vulnerability emerges as the central process linking climatic variability to unequal adaptation outcomes. Landholding size is particularly decisive in this regard. Land is not merely a productive asset but a marker of socio-economic position. Farmers with larger holdings benefit from stronger linkages to information networks, institutional support systems, credit markets and input supply chains. These connections enable anticipatory adaptation strategies, including selective intensification, irrigation expansion, and crop/livelihood diversification. In contrast, smallholders often face financial, informational and institutional constraints that narrow their adaptive choices and intensify vulnerability. They rely predominantly on short-term coping measures, such as reducing cultivation intensity, limiting input use, or seeking supplementary wage income. Thus, land inequality translates directly into climate risk inequality. This differentiation reinforces the sustainable livelihoods perspective developed by Robert Chambers and Gordon Conway, which conceptualises resilience as a function of asset endowments rather than exposure alone.\u003c/p\u003e \u003cp\u003eEmpirically, the findings confirm that rainfed cereal farming systems in Himalayan regions are highly climate-sensitive. However, the present study extends this argument in several important ways. First, it demonstrates that economic vulnerability is multi-dimensional. Rising input expenditures and unstable farm incomes can intensify vulnerability even when yields do not fall below critical thresholds. Adaptation, particularly irrigation and input intensification, often requires substantial financial investment in mountain regions where infrastructure costs are high. This dynamic produces a cost-resilience paradox i.e. maintaining stable production under climatic stress requires higher expenditure, yet increased spending simultaneously heightens financial exposure. Adaptation does not eliminate vulnerability; rather, the rising burden of input costs can offset the gains from yield stabilisation and income improvement.\u003c/p\u003e \u003cp\u003eSecond, the findings show that embedded structural and spatial differences produce adaptation inequality. Asset endowments, particularly landholding size, consistently structure households\u0026rsquo; ability to translate adaptation efforts into yield stability, investment capacity, and income security. Education further differentiates adaptation pathways by enhancing households\u0026rsquo; capacity to engage with technologies, markets, and institutional networks, even where direct productivity gains are not evident. Agro-climatic location likewise mediates adaptation outcomes, indicating that resilience is partly shaped by spatial dynamics. Together, these factors demonstrate that adaptation capacity is relational and uneven, rooted in the distribution of assets, knowledge, and locational advantage rather than solely in climatic exposure. Adaptation therefore operates less as a uniform technical adjustment and more as a socially differentiated process that can consolidate advantage for resource-endowed households while reinforcing vulnerability among marginal farmers.\u003c/p\u003e \u003cp\u003eInstitutional limitations further constrain adaptation. Limited reliance on formal extension services and climate advisories indicates uneven governance penetration in mountain peripheries. In rainfed cereal systems where sowing timing, varietal choice, and moisture management are critical, absence of timely institutional guidance increases uncertainty. Farmers\u0026rsquo; reliance on experiential knowledge reflects embedded resilience, yet under accelerating climatic change and inherent economic vulnerability such knowledge may be insufficient to support systemic transformation.\u003c/p\u003e \u003cp\u003eFrom a regional environmental change perspective, climate variability in the Western Himalayas operates through intertwined ecological and socio-economic drivers rather than as an isolated biophysical stress. The Jammu \u0026amp; Kashmir case demonstrates that vulnerability in rainfed cereal systems is structured by land inequality, rising adaptation costs, partial irrigation access, labour mobility, and market integration. Climate stress thus amplifies pre-existing agrarian hierarchies, producing differentiated adaptation trajectories that range from diversification and migration to intensified vulnerability. Equitable transformation in mountain farming systems therefore requires structurally grounded strategies that address asset disparities, ecological constraints, and institutional precarity alongside climatic risk.\u003c/p\u003e"},{"header":"6. Policy Implications","content":"\u003cp\u003eThe results indicate that climate change adaptation policy in mountain rainfed cereal farming systems must be explicitly designed around economic vulnerability rather than assuming uniform adaptive capacity. In regions such as Jammu \u0026amp; Kashmir, smallholders operate under land fragmentation, limited irrigation access, weak market integration, and constrained credit systems. Adaptation support must therefore extend beyond subsidised inputs toward strengthening livelihood security. Expanding institutional credit tailored to rainfed farmers, improving decentralised storage and aggregation facilities, and enhancing physical market connectivity are critical to reducing structural constraints.\u003c/p\u003e \u003cp\u003eSecond, agricultural transition strategies must be ecologically and socially grounded. Rainfed cereal systems in mountain regions cannot be treated as residual sectors within national adaptation planning. Policies promoting crop substitution or commercial diversification should be introduced gradually, supported by risk assessments, extension services, and assured market linkages to prevent destabilisation of subsistence-oriented households.\u003c/p\u003e \u003cp\u003eThird, governance architecture requires strengthening at the regional scale. Decentralised extension systems, empowered local institutions, and farmer cooperatives can improve access to timely climate information, adaptive technologies, and financial services. Reducing administrative fragmentation and transaction costs is particularly important in geographically dispersed mountain settings.\u003c/p\u003e \u003cp\u003eFourth, climate adaptation should be integrated with social protection. Employment guarantees, targeted subsidies, price support mechanisms, and disaster compensation frameworks can buffer climate shocks and prevent distress migration or land abandonment.\u003c/p\u003e \u003cp\u003eFinally, monitoring frameworks must incorporate vulnerability indicators such as asset stability, income diversification, and institutional access rather than relying solely on productivity metrics. Building resilience in mountain rainfed agriculture requires coordinated regional development strategies that address economic precarity alongside climatic risk.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study advances understanding of climate change adaptation practices in the context of Himalayan mountain agriculture by empirically demonstrating that economic vulnerability mediates adaptation outcomes in rainfed cereal systems of the Western Himalayas in Jammu \u0026amp; Kashmir. Consistent with vulnerability and livelihoods scholarship, the findings show that climatic exposure translates into differentiated household outcomes through unequal access to land, assets, institutional support, and livelihood diversification opportunities. In this context, rainfed cereal farming is embedded within structural constraints such as fragmented landholdings, limited irrigation, weak market integration, and infrastructural deficits that shape both economic risk and adaptive capacity.\u003c/p\u003e \u003cp\u003eBy addressing the relative scarcity of systematic evidence from Jammu \u0026amp; Kashmir, the study contributes region-specific insight to Himalayan adaptation literature that has largely focused on Uttarakhand or trans-Himalayan contexts. The results underscore that resilience in mountain cereal systems depends less on technological adoption alone than on reducing underlying economic vulnerability. Integrating vulnerability analysis into regional climate governance is therefore essential for strengthening sustainable adaptation in fragile Western Himalayan rainfed cereal farming systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no financial interests. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003cstrong\u003e\u0026nbsp;-\u003c/strong\u003e not applicable.’\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were informed about the purpose of the study and voluntarily agreed to participate. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing not applicable to this article as no datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require formal approval from an institutional ethics committee, as it did not involve any sensitive personal data, invasive procedures, or vulnerable populations. The participants were adult farmers voluntarily participating in the study, and no questions of a sensitive or personal nature were asked. All procedures were conducted in accordance with the ethical standards of social science research and the principles outlined in the Declaration of Helsinki, 1964. Prior to data collection, informed consent was obtained from all participants, and their anonymity and confidentiality were strictly maintained throughout the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdger WN (2006) Vulnerability. Glob Environ Change 16(3):268\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloenvcha.2006.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2006.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdger WN, Arnell NW, Tompkins EL (2005) Successful adaptation to climate change across scales. 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Nat Commun 16(1):2108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-025-12108-5\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-12108-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S, Garnett T, Tilman D, DeClerck FAJ, Wood A, Jonell M, Clark M, Gordon LJ, Fanzo J, Hawkes C, Zurayk R, Rivera JA, De Vries W, Majele Sibanda L, Murray CJL (2019) \u003cem\u003eFood in the Anthropocene: The EAT-Lancet Commission on healthy diets from sustainable food systems\u003c/em\u003e. \u003cem\u003eThe Lancet, 393\u003c/em\u003e(10170), 447\u0026ndash;492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(18)31788-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)31788-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Adaptation, Economic vulnerability, Rainfed cereal farming systems, Mountain agriculture, Western Himalayas","lastPublishedDoi":"10.21203/rs.3.rs-8887579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8887579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMountain regions are among the most climate-sensitive socio-ecological systems, yet adaptation outcomes within rainfed cereal farming remain uneven and insufficiently researched. This study examines how economic vulnerability mediates climate adaptation in the rainfed cereal systems of the Western Himalayas, focusing on the Jammu Division of Jammu \u0026amp; Kashmir, India. Primary data were collected in 2025 through a cross-sectional household survey of 408 farmers selected using a multi-stage disproportionate stratified random sampling design across temperate, intermediate, and subtropical agro-climatic zones. Economic vulnerability was operationalised across yield, input-cost, and income dimensions, and analysed using binary logistic regression to assess structural determinants and differential adaptation outcomes.\u003c/p\u003e \u003cp\u003eThe findings show that climate variability translates into differentiated livelihood outcomes through unequal access to land, education, institutional support, and livelihood diversification opportunities. Adaptation emerges not as a uniform technical adjustment but as a socially differentiated process embedded within structural inequality. A key contribution of the study is the identification of a cost\u0026ndash;resilience paradox: stabilising production under climatic stress often requires higher input expenditure, which simultaneously increases financial exposure and income volatility for resource-constrained households. As a result, resource-endowed farmers are more likely to convert adaptation into yield and income stability, while marginal farmers remain locked in high-sensitivity production systems and coping practices.\u003c/p\u003e \u003cp\u003eBy providing region-specific empirical evidence from Jammu \u0026amp; Kashmir, which is an underrepresented area in Himalayan scholarship, the study demonstrates that resilience in mountain rainfed cereal systems depends less on technological adoption alone than on reducing underlying economic vulnerability and structural inequality.\u003c/p\u003e","manuscriptTitle":"Climate Change, Economic Vulnerability and Unequal Adaptation in Rainfed Cereal Farming Systems in the Western Himalayas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 19:05:04","doi":"10.21203/rs.3.rs-8887579/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44f4a764-4b09-42f6-a6df-5cd9e94f1cdc","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T01:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 19:05:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8887579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8887579","identity":"rs-8887579","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}