Farmers' Perception about Climate Change, its Impact on Livestock Health, and Adaptation Measures in Gandaki Province, Nepal

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Climate change harms livestock health through heat stress and disease, reducing productivity and increasing illness risks. This study was conducted to examine the farmers’ perception about climate change, its impact on livestock health and adaptation measures in Gandaki Province, Nepal. Pre-tested closed ended questionnaire was used to collect information from the livestock farmers. Results Livestock farmers recognized signs of climate change, including rising temperatures, altered rainfall, and increased floods and droughts. Farmers widely perceived that climate change negatively affects livestock health, causing higher disease rates, mortality, and reduced productivity. Adaptation measures such as feeding supplements and modifying sheds were moderately used, while veterinary services and disease-tolerant breeds had low uptake. Ordinal logistic regression revealed that adaptation was significantly associated with factors such as off-farm employment, awareness of climate change, urban residence, and access to credit. Farmers with higher food sufficiency and formal education showed lower odds of adaptation. Conclusion Livestock farmers in Nepal are conscious of climate change and its adverse effects on animal health, yet uptake of adaptive measures remains low. Adaptation strategies are shaped by socioeconomic factors such as income levels, off-farm jobs, climate awareness, and access to financial resources. Expanding institutional support and resource accessibility is important to enhance adaptive capacity and mitigate climate risks in livestock systems. Adaptation climate change farmers’ perception Gandaki province livestock health Figures Figure 1 Figure 2 Background Climate change is a global crisis characterized by increasing temperatures, unpredictable rainfall patterns and severe weather occurrences. As revealed by the synthesis report by inter-governmental panel on climate change [ 1 ], the surface temperature of the earth was 1.08 0 C [0.95 to 1.20] 0 C in between 2011 and 2020. It is one of the serious challenges affecting the agricultural system and food security [ 2 ]. Although, agriculture is the key means of survival of the majority of the rural people in developing countries, it has heightened vulnerability due to climate change. Livestock is a key sector of agriculture in the world economy that contributes about 40.0 percent of the world’s agriculture GDP [ 3 ]. Though livestock sector has a significant contribution, it is increasingly threatened by climate-induced stressors such as heat stress, increasing trends of diseases, and feed scarcity [ 4 ]. Nepal is a climate-sensitive country, with observed temperature increases of 0.056°C annually and unpredictable monsoon patterns disrupting agro-ecological systems [ 5 ]. Since, around 40 percent of arable land in Nepal is still rain-fed, weather conditions have a significant impact of agricultural production [ 6 ]. Nonetheless, agriculture continues to be a major contributor to both the national economy as well as local livelihoods. Additionally, the traditional farming practices in Nepal rely heavily on livestock, with cattle, goat, sheep and buffalo being the main animals raised to support several aspects of farming. This sector contributes about 20 percent in agriculture GDP while it contributes 5.58 percent in overall GDP of the country [ 7 ]. Climate change affects the livestock health either directly or indirectly. The direct effects are the deaths, decrease in milk and meat and, illness due to climatic stresses while indirect impacts are related to the incidence and distribution of infectious diseases because the transmission is positively related to temperature, humidity and rainfall [ 8–11] Provided that temperature, humidity, and precipitation have a significant impact on infectious disease transmission, it is probable that climate change is affecting the seasonality as well as distribution of some of the key infectious diseases [ 12,13 ]. Large number of studies on climate change impact on livestock showed that most farmers observed increased diseases, loss of appetite, and reduced milk and meat production in Nepal [ 14,15 ]. A study in the Gandaki river basin (Myagdi, Mustang, and Chitwan) found that over half of the farmers noticed climate change effects, including more livestock diseases, fodder shortages, infertility, and water scarcity. To adapt, farmers improved feeding and used more veterinary services, but challenges like lack of climate information, labor, money, and market access hindered adaptation [ 16 ]. Climate impacts on livestock include greater susceptibility to vector-borne diseases, reduced forage availability, and water scarcity, particularly in alpine pastures in Nepal [ 17 ]. Farmers are the primary actors to combat with climate change impacts on livestock productivity and health, and adaptations are the key measures of minimizing adverse impacts of climate change. Therefore, recognizing the potential adaptation strategies to deal with climate change impacts on livestock health may be beneficial to formulate effective policy [ 18 ]. As reported by [ 19 ] and [ 3 ], primary strategies for adaptation for livestock farmers included diversifying their mixed-crop livestock, switching up the livestock species, decreasing the size of their herds, conserving water resources, and providing more feed and forage production. Moreover, use of veterinary services, adaptation of stress tolerant breed, modification of shed, livestock diversification, improved feeding, risk management strategies are the key adaptation strategies of livestock farmers in Gandaki Province, Nepal [ 20 ]. Majority of the literatures pertaining to the relationship between climate change and health have focused on vector-borne disease in humans, however, little is known about the way local farmers view the effects of changing climatic conditions on the livestock health and the mitigating measures that they adopt in response. Therefore, existing literature is insufficient to fully evaluate the impact of climate change on livestock health and the coping strategies implemented by farmers in Gandaki Province. Additionally, understanding how farmers address livestock health challenges and manage these issues despite restricted adaptation options is crucial. Therefore, this study attempts to explore the perceptions of farmers on climate change, its impact on the health of their livestock, and the adaptation measures that they employed in Gandaki Province. Methods Study Area This research was carried out in Gandaki Province of Nepal, which has a total land area of 21,974 km² and a population of 2,479,745 [ 21 ]. The province features diverse topography, resulting in temperature variations ranging from a sub-tropical climate in the southern plains to an alpine climate northern Himalayan ranges. From administrative point of view, there are 11 districts and 85 local governing bodies, comprising one metropolis, 26 municipalities, and 58 rural municipalities. The province experiences annual maximum temperatures averaging from 0.1°C in Manang to 30°C in Nawalpur, while minimum temperatures range from − 5°C in two northern districts i.e. Manang and Mustang to 30°C in the southern district of Nawalpur. Precipitation levels fluctuate annually between 250 mm and 2500 mm, with Mustang, a Himalayan district, receiving the lowest rainfall and Lumle in Kaski district recording the highest [ 21 ]. Gandaki Province was chosen for this study due to its diverse livestock holdings, including cattle, buffalo, yak / nac/chauri , sheep, goats, and pigs. According to recent livestock statistics, the province houses 521,498 buffaloes, 363,806 cattle, 1,673,925 goats, 99,869 sheep, 170,855 pigs, 45,043 ducks, and 6,670,523 chickens, averaging 3.5 livestock per person [ 7 ] . For the purpose of this study, five districts of Province namely Tanahu, Kaski, Gorkha, Mustang, and Nawalpur were purposively chosen considering their substantial livestock populations. Tanahu was included for its high numbers of cattle, goats, and pigs; Kaski for its large buffalo population; and Gorkha for its abundance of sheep. Mustang and Nawalpur were selected as the representatives of the Himalayan and Terai regions, respectively. Together, these districts encompass the province's full topographic range, from the mountainous Himalayas to the plains of the Terai. Study design Household questionnaire survey was conducted to collect data on knowledge related to climate change, its impact on livestock health and adaptation measures against it. Sampling design A multistage random sampling technique was used to survey 1,158 households across the five districts. Since no prior data on farmers' perceptions of climate change impacts and livestock adaptation strategies were available for the research area, the proportion (p) and its complement (q) were taken to be 0.50. With a 3.5% margin of error and a 5% significance level, this calculation yielded a required sample size of 772 households. To account for reduced sampling efficiency due to multistage sampling, an average design effect of 1.5 was applied, resulting in a final minimum sample size of 1,158 households (772 × 1.5). The respondents for the study were chosen using a multistage sampling technique. At first, Gandaki Province was purposively chosen. In the second stage, three districts—Kaski, Gorkha, and Tanahu—were selected due to their high livestock populations, while the inclusion of Mustang and Nawalpur districts was to ensure the representations of the mountainous and Terai regions, respectively. The second stage was followed up by the third one which included the random selection of, municipalities and rural municipalities, followed by a random selection of wards. The 2011 population census provided a comprehensive list of all the households in the selected wards [ 22 ]. The selection of the households from the selected wards was carried out by adopting random route sampling technique. Household heads (or members) who were 45 years of age or older, had lived in the area for at least 15 years, and had at least one primary livestock at the time of the survey were the target respondents of the survey. In case the household head was unavailable, another responsible household member meeting the same criteria was invited to participate. Data collection and analysis Structured questionnaire was developed. To ensure the validity of the survey instrument, the tool was thoroughly reviewed at the beginning and discussed with subject experts. For reliability, the draft questionnaire was pre-tested in a similar setting (10% of the total sample) to identify and fix any unclear or poorly worded items. It showed high degree of reliability, with a Cronbach’s Alpha exceeding 0.92. The original questionnaire was prepared in English, then translated into Nepali using a back-to-back method. The quality of the translation was subsequently evaluated and confirmed during a roundtable discussion prior to finalization. A structured questionnaire was used to collect data, focusing on farmers' knowledge of climate change, their perceptions of its impact on livestock health, and the adaptation measures they adopt. A facto face interview was conducted with the livestock farmers. The factors influencing adaptation measures were determined using descriptive statistics (percentage, mean and standard deviation) and an ordinal logistic regression model. All the assumptions (independence, linearity, multi-collinearity) were fulfilled before running ordinal logistic regression analysis. Initially, composite index (adding all five livestock health related adaptation measures) was constructed. This composite score (5–25) (adaptation measure) was further categorized into five groups: very low (5–8), low (9–12), moderate (13–16), high (17–20) and very high (21–25) based on standard Likert scaling practices. SPSS 24.0 was used to analyze the data. For statistical significance, p values less than 0.05 were considered. Ethical considerations Tribhuvan University, research directorate funded and granted permission to conduct this study. Center for Research and Innovation (CRI), Prithvi Narayan Campus waived the ethical clearance to conduct this study (Ref. No 12/2022). However, all the issues of ethical consideration (confidentiality, quality, safety and privacy) were strictly followed during the interview. Finally, verbal consent was taken from respondents before conducting the final interview. Results Understanding the socio-demographic characteristics of farmers is important to knowing how they observe and react to climate change. Table 1 shows that socio-demographic characteristics of livestock farmers. In average, the farmers were of 54.56 years in age, while the mean household size was found to be 5.19, exceeding the national average of 4.32 (23) NSO, 2021). The farmers' educational attainment was predominantly at the basic level, with over 12 percent being illiterate, 33.1 percent having informal education, 29.0 percent completing basic education, and around 25.0 percent achieving secondary or higher education. On average, farmers had food availability for 8.38 months annually. The mean agricultural landholding was 0.33 hectares, while land allocated for livestock farming was 0.13 hectares, with female farmers having access to less land than males. Less than half of the respondents considered agriculture their primary occupation. Moreover, a majority of the farmers belonged to upper-caste groups and lived in nuclear families. Table 1 Socio-demographic characteristics of livestock farmers Characteristics Total Mean (SD) Age of respondents (years) 54.56 (9.16) Household size (number) 5.19 (2.23) Education (1 = illiterate, 2 = literate (informal), 3 = basic level (1–8), 4 = secondary (9–12), and 5 = Bachelor and above) 2.73(1.07) Food sufficiency (months) 8.38 (4.69) Farm size for agriculture (hectare) 0.33 (0.11) Farm size for livestock (hectare) 0.13 (0.08) Household size 5.19(2.23) *Off-farm job No 555 (47.9%) Yes 603 (52.1%) *Family Type Nuclear 630 (54.4%) Extended 528 (45.6%) *Place of residence Rural 665(57.4%) Urban 493(42.6%) *Gender Male 736 (63.6%) Female 422 (36.4%) *Experience of climate change No 384(33.2%) Yes 774(66.8%) *Access to Credit scheme No 702(60.6%) Yes 456(39.4%) Total 1158 Note : *values are numbers and percentages in parentheses. Perception about climate change Livestock farmers’ perception about climate change was measured based on the perception about temperature, rainfall, frequency of flood and droughts. Figure 1 shows that livestock farmer’s perception about climate change in five key dimensions. Each statement is rated on a Likert scale ranging from "Strongly Disagree coded as 1" to "Strongly Agree coded as 5". More than 66.0 percent of farmers believed that maximum temperature increased while few percent disagreed. Moreover, greater than two thirds of farmers reported that timing and amount of rainfall changed. They further added that the frequency of flood and drought incidents were increased. This indicates that livestock farmers largely acknowledge the reality of climate change, characterized by rising temperatures, altered rainfall patterns, and a rise in the frequency of floods and droughts. However, a small proportion of respondents do not share this perception. Livestock farmers perceived clear changes in climate, especially increase in temperature, altered rainfall patterns, and an increase in extreme weather events (droughts and floods). Perceived impacts of climate change on livestock health among farmers Farmers were also requested to report their perceived views on the impacts of climate change across six key livestock health issues. Figure 2 shows the perceived impact of climate change on livestock health across different factors. It illustrates that livestock farmers largely perceived climate change as having negative effects on livestock health. Farmers reported an increase in disease incidence, morbidity, and mortality, along with a decline in livestock weight, quality of milk and meat. These findings suggest a widespread belief among farmers that climate variability and extreme weather events contributed to worsening livestock health conditions. Adaptation measures to climate change impact on livestock health They survey instrument collected various adaptation measures to climate change impacts on livestock farming but only five activities (composition of feed changed, fed vitamins and minerals, received veterinary services, modification of shed and adaption of climatic tolerant breeds) were used as main adaptation measures of livestock farmers that were directly related to the livestock health. Table 2 presents the distribution of adaptation measures implemented by livestock farmers in response to climate change, categorized by their frequency of adoption. The data include the number and percentage of farmers reporting each adaptation measure as No, too little, little, moderate, or as needed (coded as 1, 2, 3, 4 and 5 respectively) along with the corresponding mean and standard deviation (Mean ± SD). Veterinary service was the least used adaption measure (1.90 mean score, with 45.3% of farmers not using it) while use of vitamin and mineral supplementation was the highest adapted strategy (2.73 mean score). Changing feed composition (2.28), modifying sheds (2.51), and adopting disease-tolerant breeds (2.11) showed low to moderate adoption strategies among livestock farmers. Determinants of adaptation measures to climate change impacts on livestock health Ordinal logistic regression analysis was run to investigate the relationship between different level of adaptation measure (measured on ordinal scale) and set of predictors. The overall fit of model was statistically significant (Chi-Square = 95.62, p 0.22) suggesting an acceptable level of variation of selected independent variables on adaptation measures. Likewise, goodness of fit statistics was assessed using Pearson’s statistics (Chi-Square = 399.3, p > 0.05) that showed no significant difference between observed data and fitted model. Moreover, test of parallel lines also showed the odds of falling into the higher (vs. lower) categories on the dependent variable were same across the categories. It indicated that the odds of predictors falling into the categories of dependent variable were the same across the response categories. Table 3 shows the results of ordinal logistic regression model showing the relationship between adaptation measure and selected background characteristics. Table 3 Ordinal logistic regression model results for adaptation measure of livestock health. Dependent variable is the adaptation measures: very low, low, moderate, high and very high (base line) Characteristics Estimates Std. Error Wald Adjusted Odds Ratio P-value Adapt = very low 1.450 0.473 9.400 0.002 Adapt = low 1.071 0.472 5.159 0.023 Adapt = moderate 3.156 0.488 41.839 0.000 Adapt = high 5.019 0.574 76.326 0.000 Age 0.001 0.007 0.013 1.001 0.910 Household size -0.054 0.033 2.686 0.947 0.101 Food sufficiency -0.028 0.013 4.458 0.972 0.035 Farm size for livestock 0.597 0.458 1.698 1.817 0.193 sex = female 0.072 0.133 0.296 1.075 0.586 sex = Male (ref) Family type = Nuclear -0.160 0.148 1.161 0.852 0.281 Family type = Joint (ref) Off-farm job = yes 0.211 0.128 2.699 1.235 0.005 Off-farm Job = no (ref) Exp. of CC = yes 0.699 0.134 27.371 2.012 0.000 Exp. of CC = no (ref) Res = urban 0.779 0.134 34.034 2.179 0.000 Res = rural (ref) Edu = formal edu -0.257 0.205 1.569 0.743 0.010 Edu = literate (informal) -0.290 0.144 4.076 0.778 0.044 Education = illiterate (ref) Access to credit scheme = yes 0.947 0.127 55.60 2.578 0.000 Access to credit scheme = No Off-farm jobs, experience of climate change, residence (rural-urban), education level, sufficiency of food were found significantly related to the adaptation measures among livestock farmers. Increased food sufficiency is less likely related (aOR = 0.972, p < 0.05) to the high level of adaptation measure towards livestock health. Moreover, farmers engaged in off-farm jobs were more likely (aOR = 1.235, p < 0.01) to adopt as compared to those farmers not having off-farm jobs. Similarly, there was increased odds (aOR = 2.012, p < 0.01) of high adaptation among farmers who were aware about climate change. Farmers of urban areas had higher odds (aOR = 2.179, p < 0.01) of adaptation towards livestock health as compare to rural farmers. Similarly, high levels of education were significantly related with the low level of adaptation as compared to farmers having low level of education (Table 3 ). Discussion The findings of this study revealed that over two-thirds of the farmers perceived changes in climate, particularly in relation to maximum temperature, rainfall patterns, precipitation levels, droughts, and flood events. This shows that livestock farmers were aware about climate change. Majority of the Nepalese studies are consistent with the finding that maximum temperature and frequency of landslides are increasing, and rainfall is erratic in the last thirty years [ 17 ]. These studies further confirm that consistency between meteorological and perception data in the context of Nepal [ 24,25 ]. In developing countries like Nepal, the effects of climate change on livestock production are not exactly known or confirmed. While climate change might bring some benefits to livestock health and productivity, the negative impacts tend to outweigh those advantages [ 13,26–28, 29 ]. In this study, farmers’ reports of increased disease incidence, mortality, and reduced livestock productivity and quality are consistent with global evidence linking heat stress and erratic rainfall to vector-borne diseases and malnutrition in livestock in Nepal [ 17,30 ], Tanzania [ 4,31–33] [34 ], Brazil [ 35 ] and Ethiopia [ 19 ]. For instance, the decline in milk and meat quality aligns with findings from sub-Saharan Africa, where prolonged droughts reduced fodder availability in Kenya [ 18 ]. However, the reliance on subjective perceptions introduces potential biases; for example, farmers may conflate climate impacts with poor management practices. Further studies should integrate veterinary records to validate these perceptions. In this study, vitamin/mineral supplementation (mean = 2.73) emerged as the most adopted strategy due to its low cost and immediate benefits, similar to practices in Ethiopia [ 19 ] and, in Egypt and Spain [ 36 ]. Access to veterinary services is a key way to reduce the harmful effects of climate change on livestock health and improve their wellbeing. However, many developing countries in Asia have been struggling to provide effective veterinary care because of shortages in skilled staff, lack of training, and poor laboratory facilities [ 37 ]. Moreover, effective veterinary services showed a positive significant impact on adaptation strategies among small livestock herders in Pakistan [ 38 ]. In Nepal, accessibility of veterinary services was highly criticized by the participants of FGDs in Hill and Mountain regions. Low utilization of veterinary services (mean = 1.90) highlights systemic barriers, such as limited access and high costs. So improvement of animal healthcare services and systems for responding to diseases are the immediate action. The adoption of disease-tolerant breeds was low (mean = 2.11) among livestock farmers. This may be due to the financial constraints, low level of awareness and accessibility, a common issue in low-income setting. The finding of this study is also consistent with the findings of the study [ 36 ] in Spain and Egypt. Although young people may be more active and can take further risk in adopting new technology [ 39 ], age and farm size did not appear as the significant determinants of adaptation strategies among livestock farmers in this study. The finding of this study is also consistent with a study conducted in Pakistan [ 40 ]. Farmers having experience of climate change were about two times more likely to adapt (aOR = 2.012) aligning with the similar study conducted in Latin America [ 41 ]. Other study have shown that farmers in rural areas of Pakistan are more affected by extreme climate events than those living in cities [ 42 ]. In agreement with this, urban livestock farmers of this study showed higher adaptation rates (aOR = 2.179), likely due to better access to markets and extension services. The income from off-farm jobs of livestock farmers may facilitate them to invest in technology, labor, insurance of livestock, veterinary services and improved feeding system that minimizes the risks so that the likelihood of adaptation would be higher among livestock farmers who have off-farm jobs. The results of this study revealed that off-farm job was one of the significant determinants (aOR = 1.235) of adaptation strategies among livestock farmers. This finding is also consistent with a study among Chepang farmers in rural mid hill of Nepal [ 43 ]. Formally educated farmers as compared to others showed lower adaptation levels, possibly due to reliance on off-farm income. However, other studies at global level showed a mixed impact of education on adaptation decisions among farmers. For example; education was a significant positive determinant of adaptation decision of farmers in Pakistan [ 40 , 44 ], a significant negative determinant among transhumance in Benin [ 45 ] and education did not appear as a significant determinant in Ethiopia [46, 47] . In Nepal, many small-scale farmers lack the necessary resources to take adaptive measures against climate related risks, making access to credit a vital lifeline during challenging periods. Access to credit emerged as a strong predictor (OR = 2.578), showing the role of financial support in overcoming resource constraints. This aligns with the related studies in rural Pakistan [ 44 ] and in Nepal [ 48 ]. Conclusion The study revealed important information on livestock farmers’ perception of climate change, it impacts on livestock and the socioeconomic determinants shaping adaptive responses among livestock farmers in Gandaki Province, Nepal. In this study, the knowledge of climate change is universal among livestock farmers. They also had good knowledge of the impact of climate change on livestock health. These perceptions translated into reported declines in livestock health, including increased disease incidence, mortality, and reduced productivity (e.g., lower milk/meat quality and weight loss), emphasizing the urgent need for targeted interventions. The ordinal logistic regression showed that farmers who are more aware of climate change, live in urban areas and have off-farm jobs are more likely to take action to adapt. Conversely, formal education and food sufficiency reduced adaptive engagement, likely due to complacency or diversified livelihood reliance. Access to credit emerged as a strong enabler, highlighting the role of financial support in overcoming resource constraints. These findings highlight unique challenges in Gandaki Province, such as limited veterinary infrastructure and disparities in rural-urban resource access. To enhance resilience, policymakers must prioritize: strengthening veterinary systems, promoting low cost measures, expanding financial access. Self-report bias may overstate climate impacts; integrating veterinary records could validate perceptions. Longitudinal studies will be helpful to track how farmers adapt over time. In addition, national and sub-national studies will help better understand farmers’ perception of climate change. Declarations Ethical approval and consent to participate This study is part of a national priority research project supported by the Directorate of Research at Tribhuvan University and was approved to be conducted in Gandaki Province. Ethical approval (Reg No: 12/2022) was waived by the Center for Research and Innovation at Prithvi Narayan Campus. However, the investigators strictly followed the research protocol as outlined in the proposal. Before each interview, the purpose of the study was clearly explained, and participants were asked to take part. Verbal consent was obtained from all respondents who agreed to participate in the survey. Consent for publication Not Applicable Availability of data and materials The partial datasets used and analyzed during the preparation of this manuscript are available from the corresponding author on request. Competing interest The authors declare no competing interests. Funding This study was funded by the Research Directorate, Tribhuvan University, under the national priority project (Award No.: TU-NPAR-077/78-ERG-08). Authors’ contributions Vikash Kumar KC designed the concept, prepared the entire draft, and finalized (introduction, methodology, results and discussion, and conclusion) the manuscript. Ananta Raj Dhungana analyzed the data and Purna Bahadur Khand collected the relevant materials. Rajendra Pd. Upadhyay, Amrit Kumar Bhandari and Madhab Pd Baral analyzed the data and edited initial manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the Research Directorate team at Tribhuvan University, Nepal, for providing financial assistance to conduct this study. References Calvin K, Dasgupta D, Krinner G, Mukherji A, Thorne PW, Trisos C, et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. [Internet]. First. Intergovernmental Panel on Climate Change (IPCC); 2023. Available from: https://www.ipcc.ch/report/ar6/syr/ IPCC. 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Fan C Yun, Su D, Tian H, Hu R ting, Ran L, Yang Y, et al. Milk production and composition and metabolic alterations in the mammary gland of heat-stressed lactating dairy cows. J Integr Agric. 2019; 18(12):2844–53. Ouellet V, Cabrera VE, Fadul-Pacheco L, Charbonneau É. The relationship between the number of consecutive days with heat stress and milk production of Holstein dairy cows raised in a humid continental climate. J Dairy Sci. 2019;102(9):8537–45. Bett B, Kiunga P, Gachohi J, Sindato C, Mbotha D, Robinson T, et al. Effects of climate change on the occurrence and distribution of livestock diseases. Prev Vet Med. 2017; 137:119–29. Kimaro EG, Mor SM, Toribio JALML. Climate change perception and impacts on cattle production in pastoral communities of northern Tanzania. Pastoralism. 2018;8(1):19. FerreiraR. N, Andrade R, Ferreira L. Climate change impacts on livestock in Brazil. Int J Biometeorol. 2024;68(12):2693–704. Hashem NM, Martinez-Ros P, Gonzalez-Bulnes A, El-Raghi AA. Case studies on impacts of climate change on smallholder livestock production in Egypt and Spain. Sustainability. 2023;15(18):13975. Forman S, Hungerford N, Yamakawa M, Yanase T. Climate change impacts and risks for animal health in Asia. Rev. sci. tech. Off. int. Epiz., 2008; 27 (2). Faisal M. Do risk perceptions and constraints influence the adoption. Environ Sci and Pollut Res. 2021; 9(214). IPCC. Impacts, adaptation, and vulnerability. Cambridge: Cambridge Univ. Press; 2001. 1032 p. (Climate change 2001: contribution of Working Group ... to the third assessment report of the Intergovernmental Panel on Climate Change). Intergovernmental Panel on Climate Change, 2011. Abbas Q, Han J, Bakhsh K, Ullah R, Kousar R, Adeel A, et al. Adaptation to climate change risks among dairy farmers in Punjab, Pakistan. Land Use Policy. 2022; 119:106184. Fierros-González I, López-Feldman A. Farmers’ perception of climate change: A review of the literature for Latin America. Front Environ Sci. 2021; 9:672399. Abid M, Schilling J, Scheffran J, Zulfiqar F. Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan. Sci Total Environ. 2016; 547:447–60. Piya L, Maharjan KL, Joshi NP. Determinants of adaptation practices to climate change by Chepang households in the rural Mid-Hills of Nepal. Reg Environ Change. 2013;13(2):437–47. Khan I, Lei H, Shah IA, Ali I, Khan I, Muhammad I, et al. Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: Promise and perils from rural Pakistan. Land Use Policy. 2020; 91:104395. Idrissou Y, Assani AS, Baco MN, Yabi AJ, Alkoiret Traoré I. Adaptation strategies of cattle farmers in the dry and sub-humid tropical zones of Benin in the context of climate change. Heliyon. 2020;6(7): e04373. Feleke FB, Berhe M, Gebru G, Hoag D. Determinants of adaptation choices to climate change by sheep and goat farmers in Northern Ethiopia: the case of Southern and Central Tigray, Ethiopia. SpringerPlus. 2016;5(1):1692. Ayal DY, Leal Filho W. Farmers’ perceptions of climate variability and its adverse impacts on crop and livestock production in Ethiopia. J Arid Environ. 2017; 140:20–8. Rijal S, Gentle P, Khanal U, Wilson C, Rimal B. A systematic review of Nepalese farmers’ climate change adaptation strategies. Clim Policy. 2022;22(1):132–46. Additional Declarations No competing interests reported. Supplementary Files SurveyQuestionnaire.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6669581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470106865,"identity":"0413b54e-cb44-487e-83da-858f2cdef091","order_by":0,"name":"Vikash Kumar KC","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACHjYGhg8GEjz8IE5CAZFaGGdU2MhINoC0GBCphZnnTJqNwQEQjxgtBmeOJX7gbTvMY3x+deKHBwYM8vxiBwhoOdt2WEISqMXsxtvNEkCHGc6cnYBfi2Q/exuDIVjL2Q0gLQkGt4nRkghy2Iyzm38QpYWft+0Yw4EzaTwG/L3biLOFn+dYsmRDhQ2PxA3ebRYJBhKE/cLGk2b4+Y+BhD1//9nNN39U2MjzSxPQggASYJUSxCoHO/EAKapHwSgYBaNgJAEASrZCU0fqQnwAAAAASUVORK5CYII=","orcid":"","institution":"Prithvi Narayan Campus","correspondingAuthor":true,"prefix":"","firstName":"Vikash","middleName":"Kumar","lastName":"KC","suffix":""},{"id":470106866,"identity":"cc2ea065-f3c0-437e-aeac-71219e4ce1b6","order_by":1,"name":"Ananta Raj Dhungana","email":"","orcid":"","institution":"Pokhara University","correspondingAuthor":false,"prefix":"","firstName":"Ananta","middleName":"Raj","lastName":"Dhungana","suffix":""},{"id":470106867,"identity":"2c69309c-35fa-4dd6-b999-2d4094c13984","order_by":2,"name":"Purna Bahadur Khand","email":"","orcid":"","institution":"Pokhara University","correspondingAuthor":false,"prefix":"","firstName":"Purna","middleName":"Bahadur","lastName":"Khand","suffix":""},{"id":470106868,"identity":"f9f9683b-f820-425d-b80b-9e1740ea192c","order_by":3,"name":"Rajendra Pd Upadhyaya","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Rajendra","middleName":"Pd","lastName":"Upadhyaya","suffix":""},{"id":470106869,"identity":"b089540c-72a4-4e73-ab99-5fdb9028d9be","order_by":4,"name":"Amrit Kumar Bhandari","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Amrit","middleName":"Kumar","lastName":"Bhandari","suffix":""},{"id":470106870,"identity":"b6c28730-693e-4722-996a-59d212e2a789","order_by":5,"name":"Madhab Pd Baral","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Madhab","middleName":"Pd","lastName":"Baral","suffix":""}],"badges":[],"createdAt":"2025-05-15 06:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6669581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6669581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84589941,"identity":"c1f2f878-7406-41c6-a791-e9375202dad8","added_by":"auto","created_at":"2025-06-14 01:55:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLivestock farmers’ perception about the climate change\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6669581/v1/2625456db12c4f7a5a2108fb.png"},{"id":84589710,"identity":"e641306f-f653-4cd9-92c7-50c84cb3248c","added_by":"auto","created_at":"2025-06-14 01:47:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerceive Impacts of climate change on livestock health\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6669581/v1/e4e29bf0fd82215a13f91b11.png"},{"id":90877180,"identity":"ccccd44f-3432-439e-b847-c5002503818e","added_by":"auto","created_at":"2025-09-09 09:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1157332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6669581/v1/a6e69acf-9590-454f-9dc7-7ab4c1b9f6e4.pdf"},{"id":84589712,"identity":"54f0e7a4-42de-461a-a3a9-66ae731b3cad","added_by":"auto","created_at":"2025-06-14 01:47:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":49230,"visible":true,"origin":"","legend":"","description":"","filename":"SurveyQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6669581/v1/318e85a291312fda1639180d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Farmers' Perception about Climate Change, its Impact on Livestock Health, and Adaptation Measures in Gandaki Province, Nepal","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change is a global crisis characterized by increasing temperatures, unpredictable rainfall patterns and severe weather occurrences. As revealed by the synthesis report by inter-governmental panel on climate change [\u003cb\u003e1\u003c/b\u003e], the surface temperature of the earth was 1.08\u003csup\u003e0\u003c/sup\u003eC [0.95 to 1.20]\u003csup\u003e0\u003c/sup\u003eC in between 2011 and 2020. It is one of the serious challenges affecting the agricultural system and food security [\u003cb\u003e2\u003c/b\u003e]. Although, agriculture is the key means of survival of the majority of the rural people in developing countries, it has heightened vulnerability due to climate change.\u003c/p\u003e \u003cp\u003eLivestock is a key sector of agriculture in the world economy that contributes about 40.0 percent of the world\u0026rsquo;s agriculture GDP [\u003cb\u003e3\u003c/b\u003e]. Though livestock sector has a significant contribution, it is increasingly threatened by climate-induced stressors such as heat stress, increasing trends of diseases, and feed scarcity [\u003cb\u003e4\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eNepal is a climate-sensitive country, with observed temperature increases of 0.056\u0026deg;C annually and unpredictable monsoon patterns disrupting agro-ecological systems [\u003cb\u003e5\u003c/b\u003e]. Since, around 40 percent of arable land in Nepal is still rain-fed, weather conditions have a significant impact of agricultural production [\u003cb\u003e6\u003c/b\u003e]. Nonetheless, agriculture continues to be a major contributor to both the national economy as well as local livelihoods. Additionally, the traditional farming practices in Nepal rely heavily on livestock, with cattle, goat, sheep and buffalo being the main animals raised to support several aspects of farming. This sector contributes about 20 percent in agriculture GDP while it contributes 5.58 percent in overall GDP of the country [\u003cb\u003e7\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eClimate change affects the livestock health either directly or indirectly. The direct effects are the deaths, decrease in milk and meat and, illness due to climatic stresses while indirect impacts are related to the incidence and distribution of infectious diseases because the transmission is positively related to temperature, humidity and rainfall [\u003cb\u003e8\u0026ndash;11]\u003c/b\u003e Provided that temperature, humidity, and precipitation have a significant impact on infectious disease transmission, it is probable that climate change is affecting the seasonality as well as distribution of some of the key infectious diseases [\u003cb\u003e12,13\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eLarge number of studies on climate change impact on livestock showed that most farmers observed increased diseases, loss of appetite, and reduced milk and meat production in Nepal [\u003cb\u003e14,15\u003c/b\u003e]. A study in the Gandaki river basin (Myagdi, Mustang, and Chitwan) found that over half of the farmers noticed climate change effects, including more livestock diseases, fodder shortages, infertility, and water scarcity. To adapt, farmers improved feeding and used more veterinary services, but challenges like lack of climate information, labor, money, and market access hindered adaptation [\u003cb\u003e16\u003c/b\u003e]. Climate impacts on livestock include greater susceptibility to vector-borne diseases, reduced forage availability, and water scarcity, particularly in alpine pastures in Nepal [\u003cb\u003e17\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eFarmers are the primary actors to combat with climate change impacts on livestock productivity and health, and adaptations are the key measures of minimizing adverse impacts of climate change. Therefore, recognizing the potential adaptation strategies to deal with climate change impacts on livestock health may be beneficial to formulate effective policy [\u003cb\u003e18\u003c/b\u003e]. As reported by [\u003cb\u003e19\u003c/b\u003e] and [\u003cb\u003e3\u003c/b\u003e], primary strategies for adaptation for livestock farmers included diversifying their mixed-crop livestock, switching up the livestock species, decreasing the size of their herds, conserving water resources, and providing more feed and forage production. Moreover, use of veterinary services, adaptation of stress tolerant breed, modification of shed, livestock diversification, improved feeding, risk management strategies are the key adaptation strategies of livestock farmers in Gandaki Province, Nepal [\u003cb\u003e20\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eMajority of the literatures pertaining to the relationship between climate change and health have focused on vector-borne disease in humans, however, little is known about the way local farmers view the effects of changing climatic conditions on the livestock health and the mitigating measures that they adopt in response. Therefore, existing literature is insufficient to fully evaluate the impact of climate change on livestock health and the coping strategies implemented by farmers in Gandaki Province. Additionally, understanding how farmers address livestock health challenges and manage these issues despite restricted adaptation options is crucial. Therefore, this study attempts to explore the perceptions of farmers on climate change, its impact on the health of their livestock, and the adaptation measures that they employed in Gandaki Province.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThis research was carried out in Gandaki Province of Nepal, which has a total land area of 21,974 km\u0026sup2; and a population of 2,479,745 [\u003cb\u003e21\u003c/b\u003e]. The province features diverse topography, resulting in temperature variations ranging from a sub-tropical climate in the southern plains to an alpine climate northern Himalayan ranges. From administrative point of view, there are 11 districts and 85 local governing bodies, comprising one metropolis, 26 municipalities, and 58 rural municipalities. The province experiences annual maximum temperatures averaging from 0.1\u0026deg;C in Manang to 30\u0026deg;C in Nawalpur, while minimum temperatures range from \u0026minus;\u0026thinsp;5\u0026deg;C in two northern districts i.e. Manang and Mustang to 30\u0026deg;C in the southern district of Nawalpur. Precipitation levels fluctuate annually between 250 mm and 2500 mm, with Mustang, a Himalayan district, receiving the lowest rainfall and Lumle in Kaski district recording the highest [\u003cb\u003e21\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eGandaki Province was chosen for this study due to its diverse livestock holdings, including cattle, buffalo, \u003cem\u003eyak\u003c/em\u003e/\u003cem\u003enac/chauri\u003c/em\u003e, sheep, goats, and pigs. According to recent livestock statistics, the province houses 521,498 buffaloes, 363,806 cattle, 1,673,925 goats, 99,869 sheep, 170,855 pigs, 45,043 ducks, and 6,670,523 chickens, averaging 3.5 livestock per person [\u003cb\u003e7\u003c/b\u003e] .\u003c/p\u003e \u003cp\u003eFor the purpose of this study, five districts of Province namely Tanahu, Kaski, Gorkha, Mustang, and Nawalpur were purposively chosen considering their substantial livestock populations. Tanahu was included for its high numbers of cattle, goats, and pigs; Kaski for its large buffalo population; and Gorkha for its abundance of sheep. Mustang and Nawalpur were selected as the representatives of the Himalayan and Terai regions, respectively. Together, these districts encompass the province's full topographic range, from the mountainous Himalayas to the plains of the Terai.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eHousehold questionnaire survey was conducted to collect data on knowledge related to climate change, its impact on livestock health and adaptation measures against it.\u003c/p\u003e\n\u003ch3\u003eSampling design\u003c/h3\u003e\n\u003cp\u003eA multistage random sampling technique was used to survey 1,158 households across the five districts. Since no prior data on farmers' perceptions of climate change impacts and livestock adaptation strategies were available for the research area, the proportion (p) and its complement (q) were taken to be 0.50. With a 3.5% margin of error and a 5% significance level, this calculation yielded a required sample size of 772 households. To account for reduced sampling efficiency due to multistage sampling, an average design effect of 1.5 was applied, resulting in a final minimum sample size of 1,158 households (772 \u0026times; 1.5).\u003c/p\u003e \u003cp\u003eThe respondents for the study were chosen using a multistage sampling technique. At first, Gandaki Province was purposively chosen. In the second stage, three districts\u0026mdash;Kaski, Gorkha, and Tanahu\u0026mdash;were selected due to their high livestock populations, while the inclusion of Mustang and Nawalpur districts was to ensure the representations of the mountainous and Terai regions, respectively. The second stage was followed up by the third one which included the random selection of, municipalities and rural municipalities, followed by a random selection of wards. The 2011 population census provided a comprehensive list of all the households in the selected wards [\u003cb\u003e22\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eThe selection of the households from the selected wards was carried out by adopting random route sampling technique. Household heads (or members) who were 45 years of age or older, had lived in the area for at least 15 years, and had at least one primary livestock at the time of the survey were the target respondents of the survey. In case the household head was unavailable, another responsible household member meeting the same criteria was invited to participate.\u003c/p\u003e\n\u003ch3\u003eData collection and analysis\u003c/h3\u003e\n\u003cp\u003eStructured questionnaire was developed. To ensure the validity of the survey instrument, the tool was thoroughly reviewed at the beginning and discussed with subject experts. For reliability, the draft questionnaire was pre-tested in a similar setting (10% of the total sample) to identify and fix any unclear or poorly worded items. It showed high degree of reliability, with a Cronbach\u0026rsquo;s Alpha exceeding 0.92. The original questionnaire was prepared in English, then translated into Nepali using a back-to-back method. The quality of the translation was subsequently evaluated and confirmed during a roundtable discussion prior to finalization.\u003c/p\u003e \u003cp\u003eA structured questionnaire was used to collect data, focusing on farmers' knowledge of climate change, their perceptions of its impact on livestock health, and the adaptation measures they adopt. A facto face interview was conducted with the livestock farmers. The factors influencing adaptation measures were determined using descriptive statistics (percentage, mean and standard deviation) and an ordinal logistic regression model. All the assumptions (independence, linearity, multi-collinearity) were fulfilled before running ordinal logistic regression analysis. Initially, composite index (adding all five livestock health related adaptation measures) was constructed. This composite score (5\u0026ndash;25) (adaptation measure) was further categorized into five groups: very low (5\u0026ndash;8), low (9\u0026ndash;12), moderate (13\u0026ndash;16), high (17\u0026ndash;20) and very high (21\u0026ndash;25) based on standard Likert scaling practices. SPSS 24.0 was used to analyze the data. For statistical significance, p values less than 0.05 were considered.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eTribhuvan University, research directorate funded and granted permission to conduct this study. Center for Research and Innovation (CRI), Prithvi Narayan Campus waived the ethical clearance to conduct this study (Ref. No 12/2022). However, all the issues of ethical consideration (confidentiality, quality, safety and privacy) were strictly followed during the interview. Finally, verbal consent was taken from respondents before conducting the final interview.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUnderstanding the socio-demographic characteristics of farmers is important to knowing how they observe and react to climate change. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that socio-demographic characteristics of livestock farmers. In average, the farmers were of 54.56 years in age, while the mean household size was found to be 5.19, exceeding the national average of 4.32 (23) NSO, 2021). The farmers' educational attainment was predominantly at the basic level, with over 12 percent being illiterate, 33.1 percent having informal education, 29.0 percent completing basic education, and around 25.0 percent achieving secondary or higher education. On average, farmers had food availability for 8.38 months annually. The mean agricultural landholding was 0.33 hectares, while land allocated for livestock farming was 0.13 hectares, with female farmers having access to less land than males. Less than half of the respondents considered agriculture their primary occupation. Moreover, a majority of the farmers belonged to upper-caste groups and lived in nuclear families.\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 livestock farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of respondents (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.56 (9.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.19 (2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (1\u0026thinsp;=\u0026thinsp;illiterate, 2\u0026thinsp;=\u0026thinsp;literate (informal), 3\u0026thinsp;=\u0026thinsp;basic level (1\u0026ndash;8),\u003c/p\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;secondary (9\u0026ndash;12), and 5\u0026thinsp;=\u0026thinsp;Bachelor and above)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73(1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood sufficiency (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38 (4.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size for agriculture (hectare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size for livestock (hectare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13 (0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.19(2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Off-farm job\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e555 (47.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Family Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Place of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e665(57.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e493(42.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e736 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e422 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Experience of climate change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384(33.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e774(66.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Access to Credit scheme\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e702(60.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456(39.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eNote\u003c/b\u003e: *values are numbers and percentages in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePerception about climate change\u003c/h3\u003e\n\u003cp\u003eLivestock farmers\u0026rsquo; perception about climate change was measured based on the perception about temperature, rainfall, frequency of flood and droughts. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that livestock farmer\u0026rsquo;s perception about climate change in five key dimensions. Each statement is rated on a Likert scale ranging from \"Strongly Disagree coded as 1\" to \"Strongly Agree coded as 5\". More than 66.0 percent of farmers believed that maximum temperature increased while few percent disagreed. Moreover, greater than two thirds of farmers reported that timing and amount of rainfall changed. They further added that the frequency of flood and drought incidents were increased. This indicates that livestock farmers largely acknowledge the reality of climate change, characterized by rising temperatures, altered rainfall patterns, and a rise in the frequency of floods and droughts. However, a small proportion of respondents do not share this perception. Livestock farmers perceived clear changes in climate, especially increase in temperature, altered rainfall patterns, and an increase in extreme weather events (droughts and floods).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePerceived impacts of climate change on livestock health among farmers\u003c/h3\u003e\n\u003cp\u003eFarmers were also requested to report their perceived views on the impacts of climate change across six key livestock health issues. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the perceived impact of climate change on livestock health across different factors. It illustrates that livestock farmers largely perceived climate change as having negative effects on livestock health. Farmers reported an increase in disease incidence, morbidity, and mortality, along with a decline in livestock weight, quality of milk and meat. These findings suggest a widespread belief among farmers that climate variability and extreme weather events contributed to worsening livestock health conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAdaptation measures to climate change impact on livestock health\u003c/h2\u003e \u003cp\u003eThey survey instrument collected various adaptation measures to climate change impacts on livestock farming but only five activities (composition of feed changed, fed vitamins and minerals, received veterinary services, modification of shed and adaption of climatic tolerant breeds) were used as main adaptation measures of livestock farmers that were directly related to the livestock health.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of adaptation measures implemented by livestock farmers in response to climate change, categorized by their frequency of adoption. The data include the number and percentage of farmers reporting each adaptation measure as No, too little, little, moderate, or as needed (coded as 1, 2, 3, 4 and 5 respectively) along with the corresponding mean and standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" height=\"318\" width=\"584\"\u003e\u003c/p\u003e\n\u003cp\u003eVeterinary service was the least used adaption measure (1.90 mean score, with 45.3% of farmers not using it) while use of vitamin and mineral supplementation was the highest adapted strategy (2.73 mean score). Changing feed composition (2.28), modifying sheds (2.51), and adopting disease-tolerant breeds (2.11) showed low to moderate adoption strategies among livestock farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDeterminants of adaptation measures to climate change impacts on livestock health\u003c/h2\u003e \u003cp\u003eOrdinal logistic regression analysis was run to investigate the relationship between different level of adaptation measure (measured on ordinal scale) and set of predictors. The overall fit of model was statistically significant (Chi-Square\u0026thinsp;=\u0026thinsp;95.62, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) indicating that the model was appropriate in differentiating the different levels of adaptation based on selected predictors. The Pseudo R-square value (Cox and Snell and Nagelkerke\u0026thinsp;\u0026gt;\u0026thinsp;0.22) suggesting an acceptable level of variation of selected independent variables on adaptation measures. Likewise, goodness of fit statistics was assessed using Pearson\u0026rsquo;s statistics (Chi-Square\u0026thinsp;=\u0026thinsp;399.3, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) that showed no significant difference between observed data and fitted model. Moreover, test of parallel lines also showed the odds of falling into the higher (vs. lower) categories on the dependent variable were same across the categories. It indicated that the odds of predictors falling into the categories of dependent variable were the same across the response categories. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of ordinal logistic regression model showing the relationship between adaptation measure and selected background characteristics.\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\u003e\u003cb\u003eOrdinal logistic regression model results for adaptation measure of livestock health. Dependent variable is the adaptation measures: very low, low, moderate, high and very high (base line)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdapt\u0026thinsp;=\u0026thinsp;very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdapt\u0026thinsp;=\u0026thinsp;low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdapt\u0026thinsp;=\u0026thinsp;moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdapt\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood sufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size for livestock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\u0026thinsp;=\u0026thinsp;female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\u0026thinsp;=\u0026thinsp;Male (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily type\u0026thinsp;=\u0026thinsp;Nuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily type\u0026thinsp;=\u0026thinsp;Joint (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-farm job\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-farm Job\u0026thinsp;=\u0026thinsp;no (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp. of CC\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp. of CC\u0026thinsp;=\u0026thinsp;no (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRes\u0026thinsp;=\u0026thinsp;urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRes\u0026thinsp;=\u0026thinsp;rural (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdu\u0026thinsp;=\u0026thinsp;formal edu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdu\u0026thinsp;=\u0026thinsp;literate (informal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;illiterate (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit scheme\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit scheme\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOff-farm jobs, experience of climate change, residence (rural-urban), education level, sufficiency of food were found significantly related to the adaptation measures among livestock farmers. Increased food sufficiency is less likely related (aOR\u0026thinsp;=\u0026thinsp;0.972, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to the high level of adaptation measure towards livestock health. Moreover, farmers engaged in off-farm jobs were more likely (aOR\u0026thinsp;=\u0026thinsp;1.235, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) to adopt as compared to those farmers not having off-farm jobs. Similarly, there was increased odds (aOR\u0026thinsp;=\u0026thinsp;2.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of high adaptation among farmers who were aware about climate change. Farmers of urban areas had higher odds (aOR\u0026thinsp;=\u0026thinsp;2.179, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of adaptation towards livestock health as compare to rural farmers. Similarly, high levels of education were significantly related with the low level of adaptation as compared to farmers having low level of education (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study revealed that over two-thirds of the farmers perceived changes in climate, particularly in relation to maximum temperature, rainfall patterns, precipitation levels, droughts, and flood events. This shows that livestock farmers were aware about climate change. Majority of the Nepalese studies are consistent with the finding that maximum temperature and frequency of landslides are increasing, and rainfall is erratic in the last thirty years [\u003cb\u003e17\u003c/b\u003e]. These studies further confirm that consistency between meteorological and perception data in the context of Nepal [\u003cb\u003e24,25\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eIn developing countries like Nepal, the effects of climate change on livestock production are not exactly known or confirmed. While climate change might bring some benefits to livestock health and productivity, the negative impacts tend to outweigh those advantages [\u003cb\u003e13,26\u0026ndash;28, 29\u003c/b\u003e]. In this study, farmers\u0026rsquo; reports of increased disease incidence, mortality, and reduced livestock productivity and quality are consistent with global evidence linking heat stress and erratic rainfall to vector-borne diseases and malnutrition in livestock in Nepal [\u003cb\u003e17,30\u003c/b\u003e], Tanzania [\u003cb\u003e4,31\u0026ndash;33] [34\u003c/b\u003e], Brazil [\u003cb\u003e35\u003c/b\u003e] and Ethiopia [\u003cb\u003e19\u003c/b\u003e]. For instance, the decline in milk and meat quality aligns with findings from sub-Saharan Africa, where prolonged droughts reduced fodder availability in Kenya [\u003cb\u003e18\u003c/b\u003e]. However, the reliance on subjective perceptions introduces potential biases; for example, farmers may conflate climate impacts with poor management practices. Further studies should integrate veterinary records to validate these perceptions.\u003c/p\u003e \u003cp\u003eIn this study, vitamin/mineral supplementation (mean\u0026thinsp;=\u0026thinsp;2.73) emerged as the most adopted strategy due to its low cost and immediate benefits, similar to practices in Ethiopia [\u003cb\u003e19\u003c/b\u003e] and, in Egypt and Spain [\u003cb\u003e36\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eAccess to veterinary services is a key way to reduce the harmful effects of climate change on livestock health and improve their wellbeing. However, many developing countries in Asia have been struggling to provide effective veterinary care because of shortages in skilled staff, lack of training, and poor laboratory facilities [\u003cb\u003e37\u003c/b\u003e]. Moreover, effective veterinary services showed a positive significant impact on adaptation strategies among small livestock herders in Pakistan [\u003cb\u003e38\u003c/b\u003e]. In Nepal, accessibility of veterinary services was highly criticized by the participants of FGDs in Hill and Mountain regions. Low utilization of veterinary services (mean\u0026thinsp;=\u0026thinsp;1.90) highlights systemic barriers, such as limited access and high costs. So improvement of animal healthcare services and systems for responding to diseases are the immediate action.\u003c/p\u003e \u003cp\u003eThe adoption of disease-tolerant breeds was low (mean\u0026thinsp;=\u0026thinsp;2.11) among livestock farmers. This may be due to the financial constraints, low level of awareness and accessibility, a common issue in low-income setting. The finding of this study is also consistent with the findings of the study [\u003cb\u003e36\u003c/b\u003e] in Spain and Egypt.\u003c/p\u003e \u003cp\u003eAlthough young people may be more active and can take further risk in adopting new technology [\u003cb\u003e39\u003c/b\u003e], age and farm size did not appear as the significant determinants of adaptation strategies among livestock farmers in this study. The finding of this study is also consistent with a study conducted in Pakistan [\u003cb\u003e40\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eFarmers having experience of climate change were about two times more likely to adapt (aOR\u0026thinsp;=\u0026thinsp;2.012) aligning with the similar study conducted in Latin America [\u003cb\u003e41\u003c/b\u003e]. Other study have shown that farmers in rural areas of Pakistan are more affected by extreme climate events than those living in cities [\u003cb\u003e42\u003c/b\u003e]. In agreement with this, urban livestock farmers of this study showed higher adaptation rates (aOR\u0026thinsp;=\u0026thinsp;2.179), likely due to better access to markets and extension services.\u003c/p\u003e \u003cp\u003eThe income from off-farm jobs of livestock farmers may facilitate them to invest in technology, labor, insurance of livestock, veterinary services and improved feeding system that minimizes the risks so that the likelihood of adaptation would be higher among livestock farmers who have off-farm jobs. The results of this study revealed that off-farm job was one of the significant determinants (aOR\u0026thinsp;=\u0026thinsp;1.235) of adaptation strategies among livestock farmers. This finding is also consistent with a study among Chepang farmers in rural mid hill of Nepal [\u003cb\u003e43\u003c/b\u003e].\u003c/p\u003e \u003cp\u003eFormally educated farmers as compared to others showed lower adaptation levels, possibly due to reliance on off-farm income. However, other studies at global level showed a mixed impact of education on adaptation decisions among farmers. For example; education was a significant positive determinant of adaptation decision of farmers in Pakistan [\u003cb\u003e40\u003c/b\u003e,\u003cb\u003e44\u003c/b\u003e], a significant negative determinant among transhumance in Benin [\u003cb\u003e45\u003c/b\u003e] and education did not appear as a significant determinant in Ethiopia \u003cb\u003e[46, 47]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn Nepal, many small-scale farmers lack the necessary resources to take adaptive measures against climate related risks, making access to credit a vital lifeline during challenging periods. Access to credit emerged as a strong predictor (OR\u0026thinsp;=\u0026thinsp;2.578), showing the role of financial support in overcoming resource constraints. This aligns with the related studies in rural Pakistan [\u003cb\u003e44\u003c/b\u003e] and in Nepal [\u003cb\u003e48\u003c/b\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study revealed important information on livestock farmers\u0026rsquo; perception of climate change, it impacts on livestock and the socioeconomic determinants shaping adaptive responses among livestock farmers in Gandaki Province, Nepal. In this study, the knowledge of climate change is universal among livestock farmers. They also had good knowledge of the impact of climate change on livestock health.\u003c/p\u003e \u003cp\u003eThese perceptions translated into reported declines in livestock health, including increased disease incidence, mortality, and reduced productivity (e.g., lower milk/meat quality and weight loss), emphasizing the urgent need for targeted interventions.\u003c/p\u003e \u003cp\u003eThe ordinal logistic regression showed that farmers who are more aware of climate change, live in urban areas and have off-farm jobs are more likely to take action to adapt. Conversely, formal education and food sufficiency reduced adaptive engagement, likely due to complacency or diversified livelihood reliance. Access to credit emerged as a strong enabler, highlighting the role of financial support in overcoming resource constraints.\u003c/p\u003e \u003cp\u003eThese findings highlight unique challenges in Gandaki Province, such as limited veterinary infrastructure and disparities in rural-urban resource access. To enhance resilience, policymakers must prioritize: strengthening veterinary systems, promoting low cost measures, expanding financial access. Self-report bias may overstate climate impacts; integrating veterinary records could validate perceptions. Longitudinal studies will be helpful to track how farmers adapt over time. In addition, national and sub-national studies will help better understand farmers\u0026rsquo; perception of climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of a national priority research project supported by the Directorate of Research at Tribhuvan University and was approved to be conducted in Gandaki Province. Ethical approval (Reg No: 12/2022) was waived by the Center for Research and Innovation at Prithvi Narayan Campus. However, the investigators strictly followed the research protocol as outlined in the proposal. Before each interview, the purpose of the study was clearly explained, and participants were asked to take part. Verbal consent was obtained from all respondents who agreed to participate in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe partial datasets used and analyzed during the preparation of this manuscript are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Research Directorate, Tribhuvan University, under the national priority project (Award No.:\u0026nbsp;TU-NPAR-077/78-ERG-08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVikash Kumar KC designed the concept, prepared the entire draft, and finalized (introduction, methodology, results and discussion, and conclusion) the manuscript. Ananta Raj Dhungana analyzed the data and Purna Bahadur Khand collected the relevant materials. Rajendra Pd. Upadhyay, Amrit Kumar Bhandari and Madhab Pd Baral analyzed the data and edited initial manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Research Directorate team at Tribhuvan University, Nepal, for providing financial assistance to conduct this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCalvin K, Dasgupta D, Krinner G, Mukherji A, Thorne PW, Trisos C, et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. [Internet]. First. 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Clim Policy. 2022;22(1):132\u0026ndash;46. \u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adaptation, climate change, farmers’ perception, Gandaki province, livestock health","lastPublishedDoi":"10.21203/rs.3.rs-6669581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6669581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClimate change is a global challenge marked by increasing temperatures, unpredictable rainfall, and more frequent extreme weather events. Climate change harms livestock health through heat stress and disease, reducing productivity and increasing illness risks. This study was conducted to examine the farmers\u0026rsquo; perception about climate change, its impact on livestock health and adaptation measures in Gandaki Province, Nepal. Pre-tested closed ended questionnaire was used to collect information from the livestock farmers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLivestock farmers recognized signs of climate change, including rising temperatures, altered rainfall, and increased floods and droughts. Farmers widely perceived that climate change negatively affects livestock health, causing higher disease rates, mortality, and reduced productivity. Adaptation measures such as feeding supplements and modifying sheds were moderately used, while veterinary services and disease-tolerant breeds had low uptake. Ordinal logistic regression revealed that adaptation was significantly associated with factors such as off-farm employment, awareness of climate change, urban residence, and access to credit. Farmers with higher food sufficiency and formal education showed lower odds of adaptation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLivestock farmers in Nepal are conscious of climate change and its adverse effects on animal health, yet uptake of adaptive measures remains low. Adaptation strategies are shaped by socioeconomic factors such as income levels, off-farm jobs, climate awareness, and access to financial resources. Expanding institutional support and resource accessibility is important to enhance adaptive capacity and mitigate climate risks in livestock systems.\u003c/p\u003e","manuscriptTitle":"Farmers' Perception about Climate Change, its Impact on Livestock Health, and Adaptation Measures in Gandaki Province, Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-14 01:47:10","doi":"10.21203/rs.3.rs-6669581/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5de530c-6ad3-461d-98f2-e27bd9ea73ac","owner":[],"postedDate":"June 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T09:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-14 01:47:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6669581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6669581","identity":"rs-6669581","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}