Socio-Economic Effect of Climate Change on Local Communities: A Case Study of The Abokyikrom and Jamasi Communities in Ghana

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Amponsah, Mumuni M. Amin, Daniel K.O. Asamoah, Frank Baffour-Ata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9114994/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study examined the socio-economic effects of climate change on the Abokyikrom and Jamasi communities in Ghana, focusing on agricultural productivity, household income, and food security. A cross-sectional survey involving 200 respondents, complemented by secondary climate data (1980–2024), was conducted to assess perceptions of climate variability, water accessibility, adaptation strategies, and socio-economic impacts. Findings revealed that most farmers, predominantly older and long-settled residents, perceived significant increases in temperature, prolonged dry seasons, erratic rainfall, and extreme weather events over time. These changes were associated with decreased crop yields, reduced household income, and increased food insecurity. Although households adopted various adaptation strategies, including new farming technologies, crop shifts, and alternative livelihoods, most were deemed ineffective, largely due to limited external support. The study addresses the urgent need for targeted interventions to enhance climate resilience and livelihood sustainability in rural Ghanaian communities. Climate change Socio-economic impacts Agricultural livelihoods Ghana Water accessibility Smallholder farmers Rainfall variability Figures Figure 1 Figure 2 Figure 3 1. Introduction Climate change has emerged as one of the most pressing global challenges of the twenty-first century, characterized by long-term alterations in temperature, precipitation patterns, and the increasing frequency of extreme weather events (Robinson, 2021 ). These shifts have far-reaching implications for both natural and human systems, particularly in developing countries, where economic livelihoods heavily depend on climate-sensitive sectors such as agriculture (Banu & Fazal, 2025 ). The agricultural sector, especially in sub-Saharan Africa, remains the primary source of employment and income for rural households, making it highly vulnerable to even slight climatic variations (Bedeke, 2023 ; Connolly-Boutin & Smit, 2016 ). In Ghana, agriculture continues to contribute significantly to the Gross Domestic Product (GDP) and provides livelihoods for a large proportion of the population, supporting national development (Darfour & Rosentrater, 2016 ). However, the country’s reliance on rain-fed agriculture increases its exposure to climate variability. Farmers across various ecological zones are increasingly experiencing erratic rainfall patterns, prolonged droughts, and sporadic flooding, all of which disrupt planting and harvesting cycles, reduce crop yields, and threaten household food security (Altieri et al., 2015 ). These climatic challenges have broader socio-economic implications, including declining incomes, increased poverty levels, and reduced access to essential services such as education and healthcare (Patel et al., 2021 ). Local communities depend primarily on smallholder farming as their main source of livelihood, yet they have undertaken limited adaptive measures to mitigate or respond to climate-related risks (Verma & Sudan, 2021 ). Persistent droughts and irregular rainfall have significantly reduced agricultural productivity, leading to declining yields and increased vulnerability among farming households (Borona, 2021 ). The resultant socio-economic strain manifests in reduced income levels, food insecurity, and overall instability of livelihoods. Despite these evident challenges, empirical assessments focusing on the specific socio-economic consequences of climate change in such localities remain limited. Previous research in Ghana has primarily examined national or regional impacts of climate variability, with less emphasis on the community-level dynamics that shape vulnerability and adaptive capacity (Antwi-Agyei et al., 2017 ; Dapilah et al., 2020 ). There is a need to explore how climate change affects local communities, such as Abokyikrom and Jamasi, where agriculture forms the economic backbone and significantly influences social well-being. Understanding the magnitude and dimensions of these effects is crucial for developing context-specific adaptation strategies that enhance resilience and promote sustainable development. This study, therefore, examines the socio-economic effects of climate change on the Abokyikrom and Jamasi communities in Ghana. Specifically, it assesses how changes in rainfall patterns, drought frequency, and other extreme weather events influence agricultural productivity and examines the resulting impacts on household income, food security, and poverty levels. 2. Materials and Methods 2.1 Study area The study was conducted in Abokyikrom, in the Birim North District, and in Jamasi, in the Kwahu West District, Ghana. The Kwahu West Municipal District, established in 2004 with Nkawkaw as its capital, spans approximately 414 km² and has a population of around 93,584, growing at an annual rate of 2.5% (Ankah, 2019 ). It is known for its strong agricultural base, particularly in cocoa, maize, and plantain production, and experiences two rainy seasons (May and October) with average temperatures of around 26°C (Julius, 2019 ). Abokyikrom, located in the Birim North District, is a predominantly farming community that cultivates rice, cocoa, and oil palm under rain-fed conditions (Donkoh, 2017 ). The district, established in 2008, spans about 520 km² with a population of 78,907 and a 2.7% annual growth rate (Antwi, 2022 ). It has a tropical climate with an average temperature of 27°C and abundant rainfall that supports agriculture. Nearby forest reserves also enhance their natural appeal and offer prospects for eco-tourism (Boakye, 2020 ). 2.2 Research design A cross-sectional survey design was employed to examine the socio-economic effects of climate change on local communities. This approach enabled the collection of qualitative data reflecting residents' experiences, perceptions, and adaptation responses in the two selected communities at a single point in time. The design was suitable for assessing demographic characteristics, perceptions of climate variability, water accessibility, socio-economic impacts, and adaptation measures among community members. 2.3 Sampling technique The study targeted farmers and residents of Abokyikrom and Jamasi, who rely on agriculture for their livelihoods. A total of 200 respondents were selected through purposive and random sampling. Purposive sampling was used to identify community members actively engaged in farming or affected by climate-related challenges, while random sampling ensured diversity across age, occupation, and duration of residence. The sample size was sufficient to represent the population and provide insight into community-level perceptions and experiences of climate change. 2.3.1 Sample size determination The sample size for this study was determined using Cochran’s formula, which provides a scientifically valid method for estimating an appropriate sample from a larger population. The formula is given as: $$n=\frac{{Z}^{2}.p(1-p)}{{e}^{2}}$$ Where: n = required sample size Z = Z-value corresponding to the desired confidence level (for a 95% confidence level, Z = 1.96) p = estimated proportion of the population (0.5 is used for maximum variability) e = margin of error (0.07 for a 7% margin) $$\text{n}=\frac{{\left(1.96\right)}^{2}\times0.5(1-0.5)}{{\left(0.07\right)}^{2}}$$ $$\text{n}=\frac{3.8416\times0.25}{0.0049}$$ $$\text{n}=\frac{0.9604}{0.0049}\approx196$$ To enhance representativeness and account for potential non-responses, the sample size was rounded up to 200 respondents. 2.4 Data collection procedure Primary data were gathered through structured questionnaires and oral interviews involving farmers, traditional leaders, and district agricultural officers in Abokyikrom and Jamasi. The questionnaire captured information on demographic characteristics, perceptions of climate change, water availability, socio-economic impacts, and community adaptation strategies. In addition to the primary data, secondary data, including the Eastern Regional Annual Climate Data for 2024 (January–November) and Historical Climate Data of the Region (1980–2020), were obtained from the Ghana Meteorological Agency and district offices to support trend analysis and comparison with community-level observations. 2.4 Data Analysis Data collected from questionnaires and secondary climate records were organized, coded, and analyzed using descriptive statistical methods. Socio-demographic characteristics, perceptions of climate change, water accessibility, socio-economic impacts, adaptation strategies, and historical and cultural contexts were summarized using frequencies and percentages. Climate data for the Eastern Region (2024) and historical trends (1980–2020) were analyzed to identify patterns in rainfall and temperature, including seasonal variations and long-term changes. Findings were presented in tables and figures to facilitate comparison and interpretation of respondents’ experiences with climate change, its socio-economic effects, and adaptation measures in the study communities. 2.5 Ethical Considerations Ethical approval was obtained from the District Assembly and local community leaders prior to data collection. Participants were informed about the study’s purpose, assured of confidentiality, and given the right to withdraw at any stage. 3. Results 3.1 Socio-demographic information The majority of the farmers were aged 46–55 years (41%), followed by those aged 36–45 years (32%). Farmers aged 56–65 and 65 + accounted for 16% and 11%, respectively, while only 2% were aged 26–35, and no respondents were within the 18–25 age group. In terms of duration in the community, most respondents (75%) had lived for more than 30 years, 24% for 21–30 years, 2% for 10–20 years, and none for less than 10 years. Regarding occupation, the majority were farmers (90%), followed by teachers (1%), drivers (2%), traders (5%), and other occupations (3%). Table 1 Socio-demographic characteristics of respondents Demographic Variable Category Frequency (n = 200) Percentage (%) Age group of Farmers 18–25 0 0 26–35 4 2 36–45 63 32 46–55 81 41 56–65 31 16 65+ 21 11 Duration in the Community Less than 10 0 0 10–20 4 2 21–30 47 24 More than 30 149 75 Occupation Farmers 179 90 Teachers 2 1 Drivers 4 2 Traders 9 5 Others 6 3 3.2 Perceptions of climate change over time All respondents (100%) indicated that the climate had changed over time. Observations included higher temperatures (100%), longer dry seasons (99%), more extreme weather events (96%), and more erratic rainfall (22%). When comparing current agricultural seasons with those from 30 years ago, most respondents (97%) reported that conditions were worse now, 2% indicated they were about the same, and 2% had no opinion. Regarding changes in crop yield over the past 30 years, the majority (93%) observed a significant decrease, 6% noted a moderate decrease, 2% reported no change, and none reported any increase. Table 2 Respondents’ perceptions of climate change and agricultural impacts Perception of Climate Change Over Time Category Frequency (n = 200) Percentage (%) Has the climate changed over time? Yes 200 100 No 0 0 What have you observed? Increased Temperature 200 100 More Erratic Rainfall 43 22 Longer Dry Season 197 99 Extreme Weather Events 192 96 Current agricultural seasons compared with 30 years ago Much Better 0 0 About the Same 4 2 Worse Now 193 97 No Opinion 3 2 Changes in crop yield over the past 30 years Significant Increase 0 0 Moderate Increase 0 0 No Change 4 2 Moderate Decrease 11 6 Significant Decrease 185 93 3.3 Water availability and accessibility A majority (83%) indicated that water availability and accessibility had decreased over time, while 16% stated that it had remained the same, and only 2% reported an improvement. All respondents (100%) walked less than 1 km to access water. Regarding household water shortages, 82% reported none, 14% reported occasional shortages, and 5% reported frequent shortages. Table 3 Respondents’ access to water and household water shortages Water Availability and Accessibility Category Frequency (n = 200) Percentage (%) How has water been available or accessible to you? Improved 3 2 Stayed the Same 31 16 Decreased 166 83 Distance covered to access water Less than 1 km 200 100 Water shortages in households Yes, frequently 9 5 Yes, occasionally 27 14 No 164 82 3.4 Socio-economic impact of climate change A majority (93%) reported that climate change had significantly reduced their household income, while 6% reported a slight decrease and 2% reported no change. Regarding food security, 89% stated that climate change had a significant impact, 6% reported slight effects, 4% observed no significant impact, and 2% indicated no impact at all. Additionally, 82% of respondents confirmed that climate change had affected their primary source of income over the past 30 years, while 19% reported no change. Table 4 Respondents’ views on the socio-economic effects of climate change Socio-Economic Impact of Climate Change Category Frequency (n = 200) Percentage (%) Effect of climate change on household income Increase Significantly 0 0 Increase Slightly 0 0 No Change 4 2 Decrease Slightly 11 6 Decrease Significantly 185 93 Effects of climate change on food security Yes, significantly 178 89 Yes, slightly 11 6 No Significant Impact 8 4 Not at All 3 2 Has climate change resulted in changes in the primary source of income over the past 30 years? Yes 163 82 No 37 19 3.5 Adaptation strategies and community responses More than half of the respondents (52%) reported using new farming technologies, while 19% had shifted to different crops, 16% had pursued alternative livelihoods, 12% had resorted to migration, and 2% had adopted other strategies. In terms of effectiveness, 75% found these strategies ineffective, 18% moderately effective, and only 7% very effective. Regarding external assistance, 59% of respondents reported receiving no support from government or NGOs, 35% reported some level of support, and 7% reported significant support. Table 5 Respondents’ adaptation strategies and support systems Adaptation Strategy and Community Responses Category Frequency (n = 200) Percentage (%) What adaptation strategies are used? Shifting to Different Crops 37 19 Using New Farming Technologies 104 52 Alternative Livelihood 32 16 Migration 24 12 Others 3 2 How effective has it been? Very Effective 14 7 Moderately Effective 36 18 Not Effective 150 75 Government/NGO support to adapt Yes, Significant 13 7 Yes, Some Support 69 35 No Support 118 59 3.6 Historical and cultural context Most respondents (88%) reported significant changes in traditional farming practices, 9% observed moderate changes, and 4% indicated no change. All respondents (100%) stated that cultural practices did not influence climate. Regarding climate perception among different age groups, 92% reported that both young and old perceived climate change, while 8% indicated otherwise. Table 6 Respondents’ views on cultural influences and climate perception Historical and Cultural Context Category Frequency (n = 200) Percentage (%) Changes in traditional farming practices Significant Change 176 88 Moderate Change 17 9 No Change 7 4 Influence of cultural practices on climate Yes 0 0 No 200 100 Climate perception among young and old Yes 184 92 No 16 8 3.7 Rainfall and Temperature Trends The Eastern Regional annual climate data for 2024, covering the period from January to November, showed variations in both rainfall and temperature. Rainfall was lowest at the start of the year, with January recording approximately 25 mm and February about 50 mm. It increased steadily from March (approximately 110 mm), peaked in June (approximately 225 mm), then declined to a post-peak low in August (approximately 70 mm), rose again in September and October (approximately 175 mm), and dropped sharply in November (approximately 50 mm). Temperatures were highest in February (≈ 29.5°C) and March (≈ 30°C), gradually fell to their lowest in August (≈ 25.5°C), and then rose again to around 28°C in November. The data indicated a main rainy season from April to October, with an inverse relationship between rainfall and temperature during the peak rainfall months. 3.8 Historical Climate Trends Figure 3 shows that from 1980 to 2020, the average temperature in the region rose steadily from about 27.0°C to 29.2°C, while the average annual rainfall decreased consistently from around 1300 mm to about 870 mm. This indicated that as the years passed, temperatures increased while rainfall declined. The trend indicated that the Eastern Region experienced the effects of climate change, characterised by warming conditions and reduced precipitation over the 40-year period. 4 Discussion 4.1 Socio-demographic information Farming in the study communities was dominated by an older population, with most respondents aged 46–55 years, followed by those between 36–45 years. This age pattern aligned with earlier findings in Ghana and across Africa, where farming households were typically headed by older adults because younger people often migrated to urban areas in search of alternative livelihoods (Tanle et al., 2020 ; Wignall et al., 2019 ). An Indonesian study also reported an ageing farming population, particularly in developing regions where agriculture was perceived as less attractive to the youth (Ngadi et al., 2023 ). The long duration of residence also supported literature suggesting that indigenous ecological knowledge was strongest among long-settled residents who had experienced climatic and environmental changes over time (Lam et al., 2020 ). The occupational distribution, with 90% of the population engaged in farming, reflected national and African rural livelihood patterns, where agriculture remained the primary source of income for the majority of rural households (Bryceson, 2019 ; Moyo, 2016 ). The low representation of younger adults further aligns with existing evidence that youth participation in agriculture in Ghana had declined due to limited incentives, land constraints, and changing aspirations (Zulu et al., 2023 ). 4.2 Perceptions of climate change over time The findings revealed strong consensus among respondents that the climate had changed over time, consistent with broader evidence from Burkina Faso, Chad, and Niger, where farmers consistently reported rising temperatures, prolonged dry seasons, and increasingly erratic rainfall (Sarr et al., 2015 ). The high perception of extreme weather events also aligns with the global literature, indicating that climate variability has intensified, particularly in tropical regions (Seneviratne et al., 2021 ). The fact that 97% of respondents believed the current agricultural seasons were worse than those of 30 years ago supported studies in northern and southern Ghana, which showed declining rainfall reliability and shorter growing seasons (Bessah et al., 2021 ). The reported decreases in crop yield similarly matched findings across sub-Saharan Africa, where climate stressors such as heat, drought, and unpredictable rainfall had significantly reduced productivity, especially for rain-fed farmers (Amede et al., 2023 ; Omay, 2024 ). 4.3 Water availability and accessibility Most respondents reported a decline in water availability and accessibility over time, consistent with studies in southwestern Nigeria that report increasing pressure on local water sources due to changing rainfall patterns and prolonged dry seasons (Ayejoto et al., 2023 ). Although all respondents accessed water within 1 km, a distance consistent with rural water supply standards, this did not guarantee adequate or reliable supply, a pattern also shown in earlier Ghanaian studies (Kulinkina et al., 2016 ). The relatively low levels of reported household water shortages contrasted with broader regional research showing more frequent shortages, suggesting that while water quantity may have declined over time, current access points in the study area still offered some stability (Mishra et al., 2021 ). 4.4 Socio-economic impact of climate change Most respondents perceived strong economic impacts from climate change, reporting significant decreases in household income. This finding was consistent with wider African studies, which have shown that climate-related shocks, such as drought, erratic rainfall, and rising temperatures, have reduced agricultural productivity and, consequently, rural incomes (Diwakar & Lacroix, 2021 ; Nkurunziza et al., 2024 ). Globally, similar patterns had been documented in climate-dependent rural economies, where climate variability was closely linked to income instability (Lamperti et al., 2019 ). The high proportion of respondents who experienced major effects on food security also reflected regional evidence that climate change had worsened crop failures, reduced food availability, and increased vulnerability among farming households (Gezie, 2019 ; Hussain et al., 2016 ). Furthermore, the finding that 82% had changed their primary source of income over the past 30 years aligns with research indicating that climate stress often prompts households to diversify their livelihoods or shift away from farming as a coping strategy (Deb & Haque, 2016 ). 4.5 Adaptation strategies and community responses Farmers employed various adaptation strategies, with just over half adopting new farming technologies, a trend consistent with findings from African countries where farmers increasingly experimented with improved seeds, soil conservation, and water-efficient methods in response to climate stress (Brempong et al., 2023 ; Msweli et al., 2025 ). The shift to different crops, alternative livelihoods, and migration reflected widely documented coping strategies across rural sub-Saharan Africa, where climate variability often forced households to diversify or relocate (Hoffmann et al., 2022 ). However, the fact that 75% considered their strategies ineffective aligns with studies by Asare-Nuamah et al. ( 2022 ) conducted in Zimbabwe, which suggest that most farmer-led adaptations remain limited by inadequate resources, weak extension services, and inconsistent rainfall patterns. The low level of external support also aligns with Ghanaian studies, which reveal gaps in government and NGO interventions, despite national policies promoting climate-smart agriculture (Antwi-Agyei et al., 2025 ; Dziwornu et al., 2024 ). 4.6 Historical and cultural context Danso-Abbeam et al. (2022) showed that climate variability had forced farmers to modify long-standing agricultural methods, such as planting calendars, crop selection, and soil management techniques, as a result of experiencing significant changes in traditional farming practices, a pattern consistent with research in Nigeria. The view that cultural practices did not influence climate, as reported by all respondents, aligned with broader scientific literature emphasizing that climate change is driven primarily by global anthropogenic factors rather than local cultural traditions (Pearson et al., 2023 ). Furthermore, the high level of climate change awareness across age groups supported earlier studies indicating that both older and younger farmers in Ghana increasingly recognized shifts in temperature, rainfall, and seasonal patterns, largely due to their direct exposure to environmental changes (Antwi-Agyei & Nyantakyi-Frimpong, 2021 ; Asante et al., 2017 ). 4.7 Seasonal climate patterns in the Eastern Region (2024) The marked increase in rainfall from March to a peak in June aligned with Ghana’s major rainy season, which typically spans April to July (Bessah et al., 2021 ). The decline in rainfall after June and the secondary rise in September and October resembled the well-documented bimodal rainfall pattern characteristic of the forest and transitional zones of southern Ghana. The observed inverse relationship between rainfall and temperature also reflected established climatological patterns in West Africa, where cloud cover and increased precipitation typically reduce surface temperatures (Diba et al., 2018 ). Similarly, the rise in temperatures during the drier months of February and March corresponded with findings from global tropical climate studies, which show higher temperatures during periods of reduced rainfall and increased solar radiation (Liu et al., 2019 ). The 2024 data aligned with both Ghanaian and global literature on seasonal climate variability in tropical climates. 5 Conclusion and recommendations The study concluded that climate change had significantly affected the Abokyikrom and Jamasi communities, with most respondents perceiving increased temperatures, prolonged dry seasons, erratic rainfall, and extreme weather events over time. These changes contributed to decreased agricultural productivity, reduced household income, and increased food insecurity, prompting households to adopt various coping strategies, including new farming technologies, crop shifts, alternative livelihoods, and migration. However, most adaptation measures were perceived as ineffective, partly due to limited support from the government and NGOs. The findings also showed that farming in the study communities was dominated by older, long-settled residents with strong local ecological knowledge, while cultural practices were not considered a significant driver of climate change. Furthermore, collaboration among local authorities, traditional leaders, and development partners was recommended to improve water resource management, community infrastructure, and early warning systems. Establishing accessible funding and insurance schemes for farmers could also help cushion climate-related economic shocks. Finally, integrating community perceptions into regional climate policy planning would ensure context-specific, inclusive, and sustainable adaptation responses tailored to the needs of older farming populations and to their valuable indigenous knowledge. 5.1 Limitations of the study The study was limited to two communities, which restricts the generalizability of the findings to other areas of Ghana. Its cross-sectional design captured data at a single point in time, preventing assessment of long-term changes or causal relationships. Most respondents were older farmers with long residency, resulting in limited representation of younger community members and potentially biasing perceptions of climate change and adaptation strategies. Additionally, the reliance on subjective data on climate impacts, crop yields, and socio-economic effects may be subject to recall bias. At the same time, the effectiveness of adaptation measures was based on perception rather than objective evaluation. The study was also limited by the inability to interview key local authorities, including the Assemblyman, traditional chiefs, and district officers, which may have restricted access to broader community knowledge and official perspectives. Finally, although secondary climate data were used, limited integration with local measurements may affect the accuracy of climate impact assessments. 5.2 Future research directions Future studies should expand the geographical scope beyond Abokyikrom and Jamasi to include multiple communities across different ecological zones in Ghana, enabling broader comparisons and improved generalizability of findings. Panel studies are recommended to track climate change impacts and adaptation responses over time, providing stronger evidence of trends and causal relationships. Future research should also incorporate the perspectives of younger community members to better understand intergenerational differences in climate perception, vulnerability, and adaptive behaviors. Additionally, mixed-method approaches that combine perception-based data with objective measurements, such as crop yield records, hydrological data, and localized climate observations, would enhance the validity of climate impact assessments. Engaging key stakeholders, including traditional authorities, assembly members, and district officials, in future studies would provide deeper policy insights and a more comprehensive understanding of institutional response mechanisms. Further research should also evaluate the actual effectiveness and cost–benefit of specific adaptation strategies to guide the design of evidence-based climate resilience interventions. Declarations Funding Not applicable Conflict of interest The authors assert that they do not possess any recognized competing interests. References Altieri MA, Nicholls CI, Henao A, Lana MA (2015) Agroecology and the design of climate change-resilient farming systems. 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Sustain (2071 – 1050), 17 (3) Ngadi N, Zaelany AA, Latifa A, Harfina D, Asiati D, Setiawan B, Rajagukguk Z (2023) Challenge of agriculture development in Indonesia: rural youth mobility and aging workers in agriculture sector. Sustainability 15(2):922 Nkurunziza F, Kabanda R, McSharry P (2024) Contextual Perspective on Climate-Related Shocks, Coping Strategies and Household Consumption in Sub-Saharan Africa (SSA): Trends and Insights. J Scientometr Res 13(3):877–893 Omay PO (2024) Analysis of Present and Future Changes in Extreme Rainfall Events Linked to Food Security in the Igad Region of Eastern Africa (Doctoral dissertation, University of Nairobi) Patel S, Dey A, Singh SK, Singh R, Singh HP (2021) Socio-economic impacts of climate change. Clim Impacts Sustainable Nat Resource Manage, 237–267 Pearson J, Jackson G, McNamara KE (2023) Climate-driven losses to knowledge systems and cultural heritage: A literature review exploring the impacts on Indigenous and local cultures. Anthropocene Rev 10(2):343–366 Robinson WA (2021) Climate change and extreme weather: A review focusing on the continental United States. J Air Waste Manag Assoc 71(10):1186–1209 Sarr B, Atta S, Ly M, Salack S, Ourback TE, Subsol SE, George DA (2015) Adapting to climate variability and change in smallholder farming communities: A case study from Burkina Faso, Chad and Niger. J agricultural Ext rural Dev 7(1):16–27 Seneviratne SI, Zhang X, Adnan M, Badi W, Dereczynski C, Luca AD, Allan R (2021) Weather and climate extreme events in a changing climate Tanle A, Ogunleye-Adetona CI, Arthor G (2020) Rural-urban migration and household livelihood in the Agona West Municipality, Ghana. J Geogr Reg Plann 13(1):1–18 Verma S, Sudan FK (2021) Impact of climate change on marginal and small farmers’ livelihood and their adaptation strategies-a review. Reg Economic Dev Res 2(2):96–112 Wignall R, McQuaid K, Gough KV, Esson J (2019) We built this city’: Mobilities, urban livelihoods and social infrastructure in the lives of elderly Ghanaians. Geoforum 103:75–84 Zulu LC, Djenontin IN, Kamoto JF, Kampanje-Phiri JM, Fischer G (2023) Do youth conceptualizations influence the inclusion of young people in sustainable agriculture intensification? Insights from Ghana and Malawi. Environ Dev Sustain 25(12):13909–13935 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Apr, 2026 Reviewers invited by journal 25 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 13 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9114994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607209554,"identity":"10d56cc2-89ea-4cc1-a6c9-6ee2347bb5ad","order_by":0,"name":"Lydia O. Amponsah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACPjhLgvkwjGkAxAdwamFDaGFLBlOkaOExJlILe/PTzQUVd+z5Z/d8Ni5sq6tjYG/eJsHw6w5uLTzHzG7POPMsccads5uTZ7YdlmDgOVYmwdj3DLcWiQSz27xthxMYbuRuPszbdkCCQSLHTIKx5zAeLenfQFrs5W/kPAZqqZNgkH9DSEsO2BbGDTdymJN525iBtvCYSTD8wKOF50wZ0C+HEzfeSDM25jl3WLKNJ63YIrEBtxZ+9vZttwsqDtvL3Uh+LM1TVsfPz354440Pf3BrAQFmVHtBRGIbXh1oWiDgD34to2AUjIJRMKIAAFPYVDSia/CZAAAAAElFTkSuQmCC","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Lydia","middleName":"O.","lastName":"Amponsah","suffix":""},{"id":607209555,"identity":"65dc17f9-2f39-473f-a7b1-dd69c33d45c1","order_by":1,"name":"Mumuni M. Amin","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mumuni","middleName":"M.","lastName":"Amin","suffix":""},{"id":607209556,"identity":"399b1d39-97d9-4fa8-acd1-3b0ca99cc766","order_by":2,"name":"Daniel K.O. Asamoah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"K.O.","lastName":"Asamoah","suffix":""},{"id":607209557,"identity":"4e1baa87-181a-44a2-91a6-a14ed329408d","order_by":3,"name":"Frank Baffour-Ata","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Baffour-Ata","suffix":""}],"badges":[],"createdAt":"2026-03-13 12:55:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9114994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9114994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105363446,"identity":"14ec6bf6-907e-4958-bb4a-f2fda5ba5baf","added_by":"auto","created_at":"2026-03-25 08:16:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAbokyikrom and Jamasi in the Eastern Region of Ghana\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9114994/v1/8d634e17ad442a71359bd419.png"},{"id":105363445,"identity":"158a0fd3-9140-4048-bdcc-1c13f36f79d4","added_by":"auto","created_at":"2026-03-25 08:16:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMonthly rainfall and temperature variations in the Eastern Region of Ghana (January–November 2024)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9114994/v1/f862458932a27c22367f83a9.png"},{"id":105565616,"identity":"ce625f0d-9e9b-437f-87a9-bf8855bee20b","added_by":"auto","created_at":"2026-03-27 12:53:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistorical rainfall and temperature trends in the Eastern Region of Ghana (1980–2020)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9114994/v1/ece817bd16b57d07fff41436.png"},{"id":105570087,"identity":"332bf198-b394-4674-90eb-d5bd11075746","added_by":"auto","created_at":"2026-03-27 13:14:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114994/v1/5063d5e2-e120-4f18-a48b-f00d5e447178.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socio-Economic Effect of Climate Change on Local Communities: A Case Study of The Abokyikrom and Jamasi Communities in Ghana","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change has emerged as one of the most pressing global challenges of the twenty-first century, characterized by long-term alterations in temperature, precipitation patterns, and the increasing frequency of extreme weather events (Robinson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These shifts have far-reaching implications for both natural and human systems, particularly in developing countries, where economic livelihoods heavily depend on climate-sensitive sectors such as agriculture (Banu \u0026amp; Fazal, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The agricultural sector, especially in sub-Saharan Africa, remains the primary source of employment and income for rural households, making it highly vulnerable to even slight climatic variations (Bedeke, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Connolly-Boutin \u0026amp; Smit, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Ghana, agriculture continues to contribute significantly to the Gross Domestic Product (GDP) and provides livelihoods for a large proportion of the population, supporting national development (Darfour \u0026amp; Rosentrater, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the country\u0026rsquo;s reliance on rain-fed agriculture increases its exposure to climate variability. Farmers across various ecological zones are increasingly experiencing erratic rainfall patterns, prolonged droughts, and sporadic flooding, all of which disrupt planting and harvesting cycles, reduce crop yields, and threaten household food security (Altieri et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These climatic challenges have broader socio-economic implications, including declining incomes, increased poverty levels, and reduced access to essential services such as education and healthcare (Patel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLocal communities depend primarily on smallholder farming as their main source of livelihood, yet they have undertaken limited adaptive measures to mitigate or respond to climate-related risks (Verma \u0026amp; Sudan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Persistent droughts and irregular rainfall have significantly reduced agricultural productivity, leading to declining yields and increased vulnerability among farming households (Borona, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The resultant socio-economic strain manifests in reduced income levels, food insecurity, and overall instability of livelihoods. Despite these evident challenges, empirical assessments focusing on the specific socio-economic consequences of climate change in such localities remain limited.\u003c/p\u003e \u003cp\u003ePrevious research in Ghana has primarily examined national or regional impacts of climate variability, with less emphasis on the community-level dynamics that shape vulnerability and adaptive capacity (Antwi-Agyei et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dapilah et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There is a need to explore how climate change affects local communities, such as Abokyikrom and Jamasi, where agriculture forms the economic backbone and significantly influences social well-being. Understanding the magnitude and dimensions of these effects is crucial for developing context-specific adaptation strategies that enhance resilience and promote sustainable development.\u003c/p\u003e \u003cp\u003eThis study, therefore, examines the socio-economic effects of climate change on the Abokyikrom and Jamasi communities in Ghana. Specifically, it assesses how changes in rainfall patterns, drought frequency, and other extreme weather events influence agricultural productivity and examines the resulting impacts on household income, food security, and poverty levels.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study was conducted in Abokyikrom, in the Birim North District, and in Jamasi, in the Kwahu West District, Ghana. The Kwahu West Municipal District, established in 2004 with Nkawkaw as its capital, spans approximately 414 km\u0026sup2; and has a population of around 93,584, growing at an annual rate of 2.5% (Ankah, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is known for its strong agricultural base, particularly in cocoa, maize, and plantain production, and experiences two rainy seasons (May and October) with average temperatures of around 26\u0026deg;C (Julius, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbokyikrom, located in the Birim North District, is a predominantly farming community that cultivates rice, cocoa, and oil palm under rain-fed conditions (Donkoh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The district, established in 2008, spans about 520 km\u0026sup2; with a population of 78,907 and a 2.7% annual growth rate (Antwi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has a tropical climate with an average temperature of 27\u0026deg;C and abundant rainfall that supports agriculture. Nearby forest reserves also enhance their natural appeal and offer prospects for eco-tourism (Boakye, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research design\u003c/h2\u003e \u003cp\u003e A cross-sectional survey design was employed to examine the socio-economic effects of climate change on local communities. This approach enabled the collection of qualitative data reflecting residents' experiences, perceptions, and adaptation responses in the two selected communities at a single point in time. The design was suitable for assessing demographic characteristics, perceptions of climate variability, water accessibility, socio-economic impacts, and adaptation measures among community members.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sampling technique\u003c/h2\u003e \u003cp\u003eThe study targeted farmers and residents of Abokyikrom and Jamasi, who rely on agriculture for their livelihoods. A total of 200 respondents were selected through purposive and random sampling. Purposive sampling was used to identify community members actively engaged in farming or affected by climate-related challenges, while random sampling ensured diversity across age, occupation, and duration of residence. The sample size was sufficient to represent the population and provide insight into community-level perceptions and experiences of climate change.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Sample size determination\u003c/h2\u003e \u003cp\u003eThe sample size for this study was determined using Cochran\u0026rsquo;s formula, which provides a scientifically valid method for estimating an appropriate sample from a larger population. The formula is given as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$n=\\frac{{Z}^{2}.p(1-p)}{{e}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;required sample size\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Z-value corresponding to the desired confidence level (for a 95% confidence level, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.96)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;estimated proportion of the population (0.5 is used for maximum variability)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ee\u003c/em\u003e\u0026thinsp;=\u0026thinsp;margin of error (0.07 for a 7% margin)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{n}=\\frac{{\\left(1.96\\right)}^{2}\\times0.5(1-0.5)}{{\\left(0.07\\right)}^{2}}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{n}=\\frac{3.8416\\times0.25}{0.0049}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equd\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\text{n}=\\frac{0.9604}{0.0049}\\approx196$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo enhance representativeness and account for potential non-responses, the sample size was rounded up to 200 respondents.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data collection procedure\u003c/h2\u003e \u003cp\u003ePrimary data were gathered through structured questionnaires and oral interviews involving farmers, traditional leaders, and district agricultural officers in Abokyikrom and Jamasi. The questionnaire captured information on demographic characteristics, perceptions of climate change, water availability, socio-economic impacts, and community adaptation strategies. In addition to the primary data, secondary data, including the Eastern Regional Annual Climate Data for 2024 (January\u0026ndash;November) and Historical Climate Data of the Region (1980\u0026ndash;2020), were obtained from the Ghana Meteorological Agency and district offices to support trend analysis and comparison with community-level observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eData collected from questionnaires and secondary climate records were organized, coded, and analyzed using descriptive statistical methods. Socio-demographic characteristics, perceptions of climate change, water accessibility, socio-economic impacts, adaptation strategies, and historical and cultural contexts were summarized using frequencies and percentages. Climate data for the Eastern Region (2024) and historical trends (1980\u0026ndash;2020) were analyzed to identify patterns in rainfall and temperature, including seasonal variations and long-term changes. Findings were presented in tables and figures to facilitate comparison and interpretation of respondents\u0026rsquo; experiences with climate change, its socio-economic effects, and adaptation measures in the study communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas obtained from the District Assembly and local community leaders prior to data collection. Participants were informed about the study\u0026rsquo;s purpose, assured of confidentiality, and given the right to withdraw at any stage.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Socio-demographic information\u003c/h2\u003e \u003cp\u003eThe majority of the farmers were aged 46\u0026ndash;55 years (41%), followed by those aged 36\u0026ndash;45 years (32%). Farmers aged 56\u0026ndash;65 and 65\u0026thinsp;+\u0026thinsp;accounted for 16% and 11%, respectively, while only 2% were aged 26\u0026ndash;35, and no respondents were within the 18\u0026ndash;25 age group. In terms of duration in the community, most respondents (75%) had lived for more than 30 years, 24% for 21\u0026ndash;30 years, 2% for 10\u0026ndash;20 years, and none for less than 10 years. Regarding occupation, the majority were farmers (90%), followed by teachers (1%), drivers (2%), traders (5%), and other occupations (3%).\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-demographic characteristics of respondents\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\u003eDemographic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eAge group of Farmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;35\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration in the Community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeachers\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrivers\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Perceptions of climate change over time\u003c/h2\u003e \u003cp\u003eAll respondents (100%) indicated that the climate had changed over time. Observations included higher temperatures (100%), longer dry seasons (99%), more extreme weather events (96%), and more erratic rainfall (22%). When comparing current agricultural seasons with those from 30 years ago, most respondents (97%) reported that conditions were worse now, 2% indicated they were about the same, and 2% had no opinion. Regarding changes in crop yield over the past 30 years, the majority (93%) observed a significant decrease, 6% noted a moderate decrease, 2% reported no change, and none reported any increase.\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\u003eRespondents\u0026rsquo; perceptions of climate change and agricultural impacts\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\u003ePerception of Climate Change Over Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eHas the climate changed over time?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\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\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhat have you observed?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased Temperature\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\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore Erratic Rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLonger Dry Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme Weather Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent agricultural seasons compared with 30 years ago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuch Better\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbout the Same\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorse Now\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Opinion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanges in crop yield over the past 30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Change\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Water availability and accessibility\u003c/h2\u003e \u003cp\u003eA majority (83%) indicated that water availability and accessibility had decreased over time, while 16% stated that it had remained the same, and only 2% reported an improvement. All respondents (100%) walked less than 1 km to access water. Regarding household water shortages, 82% reported none, 14% reported occasional shortages, and 5% reported frequent shortages.\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\u003eRespondents\u0026rsquo; access to water and household water shortages\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\u003eWater Availability and Accessibility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eHow has water been available or accessible to you?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStayed the Same\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance covered to access water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 km\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\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater shortages in households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, frequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, occasionally\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\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Socio-economic impact of climate change\u003c/h2\u003e \u003cp\u003eA majority (93%) reported that climate change had significantly reduced their household income, while 6% reported a slight decrease and 2% reported no change. Regarding food security, 89% stated that climate change had a significant impact, 6% reported slight effects, 4% observed no significant impact, and 2% indicated no impact at all. Additionally, 82% of respondents confirmed that climate change had affected their primary source of income over the past 30 years, while 19% reported no change.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespondents\u0026rsquo; views on the socio-economic effects of climate change\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\u003eSocio-Economic Impact of Climate Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eEffect of climate change on household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease Significantly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncrease Slightly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Change\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease Slightly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecrease Significantly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects of climate change on food security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, significantly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, slightly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Significant Impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at All\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas climate change resulted in changes in the primary source of income over the past 30 years?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\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\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\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\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Adaptation strategies and community responses\u003c/h2\u003e \u003cp\u003eMore than half of the respondents (52%) reported using new farming technologies, while 19% had shifted to different crops, 16% had pursued alternative livelihoods, 12% had resorted to migration, and 2% had adopted other strategies. In terms of effectiveness, 75% found these strategies ineffective, 18% moderately effective, and only 7% very effective. Regarding external assistance, 59% of respondents reported receiving no support from government or NGOs, 35% reported some level of support, and 7% reported significant support.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespondents\u0026rsquo; adaptation strategies and support systems\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\u003eAdaptation Strategy and Community Responses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eWhat adaptation strategies are used?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShifting to Different Crops\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\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing New Farming Technologies\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\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlternative Livelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMigration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow effective has it been?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Effective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately Effective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Effective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment/NGO support to adapt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, Significant\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\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, Some Support\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\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Historical and cultural context\u003c/h2\u003e \u003cp\u003eMost respondents (88%) reported significant changes in traditional farming practices, 9% observed moderate changes, and 4% indicated no change. All respondents (100%) stated that cultural practices did not influence climate. Regarding climate perception among different age groups, 92% reported that both young and old perceived climate change, while 8% indicated otherwise.\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespondents\u0026rsquo; views on cultural influences and climate perception\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\u003eHistorical and Cultural Context\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;200)\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\"\u003e \u003cp\u003eChanges in traditional farming practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluence of cultural practices on climate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\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\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate perception among young and old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Rainfall and Temperature Trends\u003c/h2\u003e \u003cp\u003eThe Eastern Regional annual climate data for 2024, covering the period from January to November, showed variations in both rainfall and temperature. Rainfall was lowest at the start of the year, with January recording approximately 25 mm and February about 50 mm. It increased steadily from March (approximately 110 mm), peaked in June (approximately 225 mm), then declined to a post-peak low in August (approximately 70 mm), rose again in September and October (approximately 175 mm), and dropped sharply in November (approximately 50 mm). Temperatures were highest in February (\u0026asymp;\u0026thinsp;29.5\u0026deg;C) and March (\u0026asymp;\u0026thinsp;30\u0026deg;C), gradually fell to their lowest in August (\u0026asymp;\u0026thinsp;25.5\u0026deg;C), and then rose again to around 28\u0026deg;C in November. The data indicated a main rainy season from April to October, with an inverse relationship between rainfall and temperature during the peak rainfall months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Historical Climate Trends\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that from 1980 to 2020, the average temperature in the region rose steadily from about 27.0\u0026deg;C to 29.2\u0026deg;C, while the average annual rainfall decreased consistently from around 1300 mm to about 870 mm. This indicated that as the years passed, temperatures increased while rainfall declined. The trend indicated that the Eastern Region experienced the effects of climate change, characterised by warming conditions and reduced precipitation over the 40-year period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Socio-demographic information\u003c/h2\u003e \u003cp\u003eFarming in the study communities was dominated by an older population, with most respondents aged 46\u0026ndash;55 years, followed by those between 36\u0026ndash;45 years. This age pattern aligned with earlier findings in Ghana and across Africa, where farming households were typically headed by older adults because younger people often migrated to urban areas in search of alternative livelihoods (Tanle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wignall et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An Indonesian study also reported an ageing farming population, particularly in developing regions where agriculture was perceived as less attractive to the youth (Ngadi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The long duration of residence also supported literature suggesting that indigenous ecological knowledge was strongest among long-settled residents who had experienced climatic and environmental changes over time (Lam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The occupational distribution, with 90% of the population engaged in farming, reflected national and African rural livelihood patterns, where agriculture remained the primary source of income for the majority of rural households (Bryceson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moyo, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The low representation of younger adults further aligns with existing evidence that youth participation in agriculture in Ghana had declined due to limited incentives, land constraints, and changing aspirations (Zulu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Perceptions of climate change over time\u003c/h2\u003e \u003cp\u003eThe findings revealed strong consensus among respondents that the climate had changed over time, consistent with broader evidence from Burkina Faso, Chad, and Niger, where farmers consistently reported rising temperatures, prolonged dry seasons, and increasingly erratic rainfall (Sarr et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The high perception of extreme weather events also aligns with the global literature, indicating that climate variability has intensified, particularly in tropical regions (Seneviratne et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The fact that 97% of respondents believed the current agricultural seasons were worse than those of 30 years ago supported studies in northern and southern Ghana, which showed declining rainfall reliability and shorter growing seasons (Bessah et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The reported decreases in crop yield similarly matched findings across sub-Saharan Africa, where climate stressors such as heat, drought, and unpredictable rainfall had significantly reduced productivity, especially for rain-fed farmers (Amede et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Omay, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Water availability and accessibility\u003c/h2\u003e \u003cp\u003eMost respondents reported a decline in water availability and accessibility over time, consistent with studies in southwestern Nigeria that report increasing pressure on local water sources due to changing rainfall patterns and prolonged dry seasons (Ayejoto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although all respondents accessed water within 1 km, a distance consistent with rural water supply standards, this did not guarantee adequate or reliable supply, a pattern also shown in earlier Ghanaian studies (Kulinkina et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The relatively low levels of reported household water shortages contrasted with broader regional research showing more frequent shortages, suggesting that while water quantity may have declined over time, current access points in the study area still offered some stability (Mishra et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Socio-economic impact of climate change\u003c/h2\u003e \u003cp\u003eMost respondents perceived strong economic impacts from climate change, reporting significant decreases in household income. This finding was consistent with wider African studies, which have shown that climate-related shocks, such as drought, erratic rainfall, and rising temperatures, have reduced agricultural productivity and, consequently, rural incomes (Diwakar \u0026amp; Lacroix, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nkurunziza et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Globally, similar patterns had been documented in climate-dependent rural economies, where climate variability was closely linked to income instability (Lamperti et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The high proportion of respondents who experienced major effects on food security also reflected regional evidence that climate change had worsened crop failures, reduced food availability, and increased vulnerability among farming households (Gezie, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hussain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, the finding that 82% had changed their primary source of income over the past 30 years aligns with research indicating that climate stress often prompts households to diversify their livelihoods or shift away from farming as a coping strategy (Deb \u0026amp; Haque, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Adaptation strategies and community responses\u003c/h2\u003e \u003cp\u003eFarmers employed various adaptation strategies, with just over half adopting new farming technologies, a trend consistent with findings from African countries where farmers increasingly experimented with improved seeds, soil conservation, and water-efficient methods in response to climate stress (Brempong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Msweli et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The shift to different crops, alternative livelihoods, and migration reflected widely documented coping strategies across rural sub-Saharan Africa, where climate variability often forced households to diversify or relocate (Hoffmann et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the fact that 75% considered their strategies ineffective aligns with studies by Asare-Nuamah et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted in Zimbabwe, which suggest that most farmer-led adaptations remain limited by inadequate resources, weak extension services, and inconsistent rainfall patterns. The low level of external support also aligns with Ghanaian studies, which reveal gaps in government and NGO interventions, despite national policies promoting climate-smart agriculture (Antwi-Agyei et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dziwornu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Historical and cultural context\u003c/h2\u003e \u003cp\u003eDanso-Abbeam et al. (2022) showed that climate variability had forced farmers to modify long-standing agricultural methods, such as planting calendars, crop selection, and soil management techniques, as a result of experiencing significant changes in traditional farming practices, a pattern consistent with research in Nigeria. The view that cultural practices did not influence climate, as reported by all respondents, aligned with broader scientific literature emphasizing that climate change is driven primarily by global anthropogenic factors rather than local cultural traditions (Pearson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the high level of climate change awareness across age groups supported earlier studies indicating that both older and younger farmers in Ghana increasingly recognized shifts in temperature, rainfall, and seasonal patterns, largely due to their direct exposure to environmental changes (Antwi-Agyei \u0026amp; Nyantakyi-Frimpong, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Asante et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Seasonal climate patterns in the Eastern Region (2024)\u003c/h2\u003e \u003cp\u003eThe marked increase in rainfall from March to a peak in June aligned with Ghana\u0026rsquo;s major rainy season, which typically spans April to July (Bessah et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The decline in rainfall after June and the secondary rise in September and October resembled the well-documented bimodal rainfall pattern characteristic of the forest and transitional zones of southern Ghana. The observed inverse relationship between rainfall and temperature also reflected established climatological patterns in West Africa, where cloud cover and increased precipitation typically reduce surface temperatures (Diba et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, the rise in temperatures during the drier months of February and March corresponded with findings from global tropical climate studies, which show higher temperatures during periods of reduced rainfall and increased solar radiation (Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 2024 data aligned with both Ghanaian and global literature on seasonal climate variability in tropical climates.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion and recommendations","content":"\u003cp\u003eThe study concluded that climate change had significantly affected the Abokyikrom and Jamasi communities, with most respondents perceiving increased temperatures, prolonged dry seasons, erratic rainfall, and extreme weather events over time. These changes contributed to decreased agricultural productivity, reduced household income, and increased food insecurity, prompting households to adopt various coping strategies, including new farming technologies, crop shifts, alternative livelihoods, and migration. However, most adaptation measures were perceived as ineffective, partly due to limited support from the government and NGOs. The findings also showed that farming in the study communities was dominated by older, long-settled residents with strong local ecological knowledge, while cultural practices were not considered a significant driver of climate change.\u003c/p\u003e \u003cp\u003eFurthermore, collaboration among local authorities, traditional leaders, and development partners was recommended to improve water resource management, community infrastructure, and early warning systems. Establishing accessible funding and insurance schemes for farmers could also help cushion climate-related economic shocks. Finally, integrating community perceptions into regional climate policy planning would ensure context-specific, inclusive, and sustainable adaptation responses tailored to the needs of older farming populations and to their valuable indigenous knowledge.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Limitations of the study\u003c/h2\u003e \u003cp\u003eThe study was limited to two communities, which restricts the generalizability of the findings to other areas of Ghana. Its cross-sectional design captured data at a single point in time, preventing assessment of long-term changes or causal relationships. Most respondents were older farmers with long residency, resulting in limited representation of younger community members and potentially biasing perceptions of climate change and adaptation strategies. Additionally, the reliance on subjective data on climate impacts, crop yields, and socio-economic effects may be subject to recall bias. At the same time, the effectiveness of adaptation measures was based on perception rather than objective evaluation. The study was also limited by the inability to interview key local authorities, including the Assemblyman, traditional chiefs, and district officers, which may have restricted access to broader community knowledge and official perspectives. Finally, although secondary climate data were used, limited integration with local measurements may affect the accuracy of climate impact assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Future research directions\u003c/h2\u003e \u003cp\u003eFuture studies should expand the geographical scope beyond Abokyikrom and Jamasi to include multiple communities across different ecological zones in Ghana, enabling broader comparisons and improved generalizability of findings. Panel studies are recommended to track climate change impacts and adaptation responses over time, providing stronger evidence of trends and causal relationships. Future research should also incorporate the perspectives of younger community members to better understand intergenerational differences in climate perception, vulnerability, and adaptive behaviors. Additionally, mixed-method approaches that combine perception-based data with objective measurements, such as crop yield records, hydrological data, and localized climate observations, would enhance the validity of climate impact assessments. Engaging key stakeholders, including traditional authorities, assembly members, and district officials, in future studies would provide deeper policy insights and a more comprehensive understanding of institutional response mechanisms. Further research should also evaluate the actual effectiveness and cost\u0026ndash;benefit of specific adaptation strategies to guide the design of evidence-based climate resilience interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert that they do not possess any recognized competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAltieri MA, Nicholls CI, Henao A, Lana MA (2015) Agroecology and the design of climate change-resilient farming systems. 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Environ Dev Sustain 25(12):13909\u0026ndash;13935\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change, Socio-economic impacts, Agricultural livelihoods, Ghana, Water accessibility, Smallholder farmers, Rainfall variability","lastPublishedDoi":"10.21203/rs.3.rs-9114994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9114994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined the socio-economic effects of climate change on the Abokyikrom and Jamasi communities in Ghana, focusing on agricultural productivity, household income, and food security. A cross-sectional survey involving 200 respondents, complemented by secondary climate data (1980\u0026ndash;2024), was conducted to assess perceptions of climate variability, water accessibility, adaptation strategies, and socio-economic impacts. Findings revealed that most farmers, predominantly older and long-settled residents, perceived significant increases in temperature, prolonged dry seasons, erratic rainfall, and extreme weather events over time. These changes were associated with decreased crop yields, reduced household income, and increased food insecurity. Although households adopted various adaptation strategies, including new farming technologies, crop shifts, and alternative livelihoods, most were deemed ineffective, largely due to limited external support. The study addresses the urgent need for targeted interventions to enhance climate resilience and livelihood sustainability in rural Ghanaian communities.\u003c/p\u003e","manuscriptTitle":"Socio-Economic Effect of Climate Change on Local Communities: A Case Study of The Abokyikrom and Jamasi Communities in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 08:16:36","doi":"10.21203/rs.3.rs-9114994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"37254664927924649307538102078527679298","date":"2026-04-18T14:50:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T16:55:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T23:13:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T23:13:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-03-13T12:49:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"648b926a-6737-408f-a853-f4a29d197e28","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T17:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 08:16:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9114994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9114994","identity":"rs-9114994","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}