Determinants of smallholder farmers choice of adaptation strategies in response to the impacts of climate variability in the Ayehu watershed, Northwest Ethiopia

Determinants of smallholder farmers choice of adaptation strategies in response to the impacts of climate variability in the Ayehu watershed, Northwest Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of smallholder farmers choice of adaptation strategies in response to the impacts of climate variability in the Ayehu watershed, Northwest Ethiopia Abebe Biresaw Bitew, Amare Sewnet Minale This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4509680/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Adapting to climate variability is crucial for sustainable livelihoods in developing countries like Ethiopia, where rain-fed agriculture underpins the economy. This study aims to evaluate both indigenous and introduced adaptation measures across different agroecological zones, along with their determining factors. Data was collected from 338 farm households using structured and semi-structured questionnaires. The Weighted Average Index (WAI) was used to identify the most significant adaptation methods employed by farm households in various agroecological zones, while the Problem Confrontation Index (PCI) assessed the barriers hindering the implementation of these strategies. The multinomial logit model (MNL) was utilized to investigate the factors affecting farmers' choices of adaptation strategies. The results indicated that the most popular indigenous adaptation strategies were planting local crop varieties (WAI = 2.22), crop diversification (WAI = 2.15), and adjusting planting dates (WAI = 2.14). The introduced adaptation strategies included using inorganic fertilizers (WAI = 2.64), applying improved crop varieties (WAI = 2.41), and using pesticides and herbicides (WAI = 2.24). PCI results revealed that the major barriers to adapting to climate variability were limited farm size (PCI = 694), lack of access to climate information (PCI = 641), poor soil quality (PCI = 639), lack of irrigation facilities (PCI = 623), and high input costs (PCI = 610). The logit model identified several significant factors influencing farmers' preferences for adaptation measures, such as crop failure, credit availability, recurrent drought, climate variability perception, agroecological location, and household income. The study underscores the importance of understanding local-level factors that influence farmers' adaptation strategies to enhance their resilience to climate variability. Climate variability Determinants Indigenous Introduced Multinominal logit model Figures Figure 1 1. Introduction Climate variability has become one of the primary challenges confronting human beings across the globe (Belay et al., 2022 ; Kiddle et al., 2021 ). Scientific evidence suggests that the Earth’s climate has rapidly shifted because of increased greenhouse gas emissions since the industrial revolution (Ghebrezgabher et al., 2016 ; Megersa et al., 2022 ). These changes manifested in higher average temperatures and alterations in global rainfall patterns, significantly impacting smallholder farmers in developing nations reliant on rained agriculture (Belay et al., 2017 ; IPCC, 2014 ). The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) presents compelling evidence that the increasing trend of global mean temperature in the 21st century has contributed to global warming (Gemeda et al., 2023 ; IPCC, 2021 ). Agriculture is widely acknowledged as the cornerstone for achieving rural development, food security, and adequate nutrition in developing countries (Myeni & Moeletsi, 2020 ; Von Loeper et al., 2016 ). However, the agricultural sector in these nations is significantly susceptible to climate variability and change, primarily due to its heavy dependence on climatic factors such as rainfall and temperature (Belay et al., 2017 ; Saguye, 2016 ). Sub-Saharan Africa (SSA), where smallholder farmers predominantly engage in agriculture, is a global hotspot for climate change-induced impacts (Adeniyi, 2016 ; Matewos, 2019 ). In this region, agriculture directly employs approximately 175 million small-scale farmers who cultivate degraded lands with limited access to reliable water for irrigation (Matewos, 2019 ). Farmers in this region face heightened vulnerability to the effects of climate fluctuations due to their reliance on rain-fed agriculture, limited use of irrigation, and weak adaptive capacity (Erdaw, 2023 ). Consequently, there is an urgent need to implement adaptation strategies tailored to current and projected climate shifts in this region (Limantol et al., 2016 ; Usmail et al., 2023 ). Agriculture holds a central position in Ethiopia’s economy, contributing 52% of the gross domestic product (GDP), employing 80% of the workforce, and generating 80.2% of foreign exchange earnings (Belay et al., 2022 ; Deressa et al., 2011 ). The agricultural sector, primarily characterized by small-scale mixed cropping and marked by low livestock productivity, wrestles with challenges like inadequate extension services (Tessema & Simane, 2021 ). Several factors contribute to the sector’s low productivity, including traditional farming methods, severe land degradation due to deforestation and overgrazing, insufficient institutional support, and climatic extremes like droughts and floods (Etana et al., 2020 ; Tesfahunegn et al., 2016 ). Understanding location-specific adaptation measures is pivotal for tailoring appropriate policy responses, as these need to be based on the unique vulnerability and sensitivity levels of each area (Asrat & Simane, 2017 ; Kahsay et al., 2019 ; Marie et al., 2020 ; Simane et al., 2016 ). Previous studies have identified various adaptation measures employed by farm households in Ethiopia to counteract the hostile impacts of climate variability. The most commonly cited adaptation strategies include crop diversification, soil and water conservation, irrigation use, agroforestry, adjusting planting dates, irrigation practices, seasonal migration, and crop rotation (Abera & Tesema, 2019 ; Addis & Abirdew, 2021b; Alemayehu & Bewket, 2017 ; Alemayehu et al., 2022 ; Bekuma et al., 2023 ; Hirpha et al., 2020 ; Mavhura et al., 2021 ; Megersa et al., 2022 ; Sertse et al., 2021 ; Tesfaye et al., 2016 ; Teshome et al., 2021 ). However, there is a significant variation in the actual implementation of these adaptation options by smallholder farmers across different regions of the country (Alemayehu et al., 2022 ). This variation arises due to the constantly changing biophysical, socioeconomic, and institutional contexts in which these adaptation strategies are implemented (Alemayehu et al., 2022 ; Bawakyillenuo et al., 2016 ). Previous studies have identified significant factors that influence farmers’ choices of adaptation strategies. For instance, Jawo et al. ( 2023 ) found that sex, literacy level, farm experience, household size, accessibility of extension services, and credit access were the most significant factors influencing the choice of adaptation strategies. Megabia et al. ( 2022 ) highlighted that household age, educational level, on/off-farm income, landholding size, extension services, and climate information were influential factors affecting farmers decisions to adopt adaptation options. According to Bekuma et al. ( 2023 ) factors like availability of extension services, farm experience, market information, and household age notably influenced smallholder farmers' adoption of various indigenous and improved adaptation strategies. Furthermore, Sertse et al. ( 2021 ) emphasized household age, educational level, credit access, availability of extension services, and farm water accessibility as critical factors. Thus, there is an urgent need for comprehensive studies on indigenous and introduced adaptation measures among smallholder farmers that incorporate socio-economic, biophysical, and institutional factors. Although numerous studies have explored options for adapting to climate variability among smallholder farmers in various regions of the country, none of the previous studies have comprehensively addressed both indigenous and introduced adaptation strategies across diverse agro-ecological zones. Agro-ecological based investigation of adaptation measures is crucial for developing and implementing effective measures to mitigate the adverse effects of climate fluctuations and alterations (Marie et al., 2020 ). Moreover, the study area lacks comprehensive documentation regarding the specific indigenous and introduced climate variability adaptation options adopted by small-scale farm households, as well as the factors influencing their choices. Therefore, this study aimed to explore the factors influencing the adoption of indigenous practices and introduced adaptation strategies to mitigate climate variability-related risks in the Ayehu watershed. Specifically, the study focused on: (1) identifying various indigenous and introduced adaptation strategies against the consequences of climate variability; (2) investigating determinant factors affecting farm households' choice of indigenous and introduced adaptation strategies in the Ayehu watershed, Northwest Ethiopia. 2. Methods and Materials 2.1. Description of investigation area This study took place in the Ayehu watershed in the northwestern region of Ethiopia. It lies 137 km southwest of Bahir Dar, the capital of the Amhara National Regional State, and 450 km west of Addis Ababa, the capital of Ethiopia. Geographically, the watershed is positioned between 10° 30' 0"-11° 0' 00" N and 36° 40' 0"-37° 0' 0" E. The landscape features rolling terrain, including rough hills, towering mountains, and smooth plains (Fig. 1 ). The research site includes three conventional agroecological regions: Highland (2300–3200 m), Midland (1500–2300 m), and low land (500–1500 m). Highland agroecology constitutes the largest part of the watershed (47.6%) followed by Midland (33.1%) agroecology (33.1%). Lowland agroecology occupies the remaining portion of the watershed (19.3%). The watershed experiences a distinctive bimodal rainfall pattern, known locally as Kiremt (the major rainy season) and Belg (the minor rainy season). Kiremt typically occurs from June to September, whereas Belg occurs from March to May. Annual rainfall varies between 1127.27 mm and 1680.70 mm, with an average annual rainfall of 1391.65 mm. The yearly low and high temperatures fluctuate between 12.2°C and 26.2°C. Agriculture serves as the primary means of sustenance and livelihood for both rural and urban households in the study watershed. Diversified agro-ecological systems, characterized by distinct climatic, soil, and altitude variations, support the cultivation of various crops, including cereals, oilseeds, pulses, and vegetables. Mixed farming, which involves both crop cultivation and livestock production, is the predominant farming system in the area (Tessema, 2019 ). The total population of the watershed is 217,665. From this, 8.2% of the population resides in urban areas, while the remaining 91.8% are in rural areas (CSA, 2016 ). 2.2. Research methods 2.2.1. Sampling strategy and sample size This study used a multistage sampling procedure to select sample Kebeles (the lowest-level administrative units) and household heads. Firstly, the study watershed was purposefully selected because it is the most severely affected by climate variability and related risks and is characterized by three distinct agroecological zones: Highland, Midland, and Lowland (Tessema, 2019 ). Secondly, three Kebeles (one from each agroecological zone) were randomly selected based on the assumption that farmers in different zones may exhibit variations in their indigenous knowledge and adaptation strategies (Table 1 ). These differences may lead to varying adaptive capacities among smallholder farmers across different agroecological zones. Additionally, the impact of climate variability is expected to differ across these zones, leading to different adaptation strategies by smallholder farmers (Belay et al., 2017 ; Likinaw et al., 2023 ). Thirdly, a list of households for each Kebele was obtained from the rural Kebele administrative offices. Given that the population was homogeneous in terms of livelihood, a random sampling method was employed to select respondent household heads from these lists, proportional to the size of the Kebele sample. Consequently, 338 randomly selected households were chosen to obtain the necessary quantitative data using the Kothari ( 2004 ) formula. $$\text{n}=\frac{{Z}^{2}P.Q.N}{{e}^{2}\left(N-1\right)+ {Z}^{2}.P.Q}$$ 1 Where: n = the sample size; N = total number of households (2821); p = sample proportion (0.5); q = 1-p; e = the margin of error/acceptable error considered (5%); Z = 1.96 is the critical value at 95% confidence interval. Table 1 Samples taken from each agroecology in the study watershed Agro-ecology Sample Kebeles Total household size Sample size Dega (High land) Bekafta 1002 120 Woina Dega (Mid-land) Sostu Segno 986 118 Kolla (Lowland) Dikuna Dereb 833 100 Total 2821 338 2.2.2. Sources of data and collection methods This study gathered data from both primary and secondary sources. Primary data collection involved household surveys, focus group discussions (FGDs), and key informant interviews (KIIs), whereas secondary sources included a systematic review of relevant published research articles. A structured survey questionnaire, featuring both closed- and open-ended questions, was developed to explore farmers' adoption of indigenous and introduced adaptation strategies, factors influencing their selection of strategies, and barriers to implementation. To ensure simplicity and comprehension during primary data collection, the questionnaire was initially drafted in English and then translated into Amharic and Awigna. Before data collection, the questionnaire underwent a pretest with households from non-sampled Kebeles. Subsequently, the questionnaire was administered to 338 household heads through face-to-face interviews conducted by six trained enumerators. These interviews were arranged on convenient days near the farmers' villages based on scheduled appointments. Distinct focus group conversations were held with elders, youth, and women in each Kebele , with each group consisting of 6–8 participants. Likewise, key informant interviews were conducted with knowledgeable community members, including agricultural staff, government administrators, and NGO representatives. These discussions and interviews gathered additional qualitative information and validated the quantitative data obtained through the household survey. Semi-structured interview guides were used for both group discussions and interviews with key informants. Data collection for the survey occurred from August to October 2023. 2.2.3. Analytical Methods Importance of climate variability adaptation strategies The Weighted Average Index (WAI) was used to rank the importance of climate variability adaptation measures in the research area. Fourteen adaptation strategies were developed using a 4-point Likert scale, and households were subsequently interviewed to assess their relative importance. Thus, the relative importance of each climate variability adaptation strategy was calculated by the weighted average index (WAI) using Eq. ( 2 ), as reported in previous studies (Fagariba et al., 2018 ; Gemeda et al., 2023 ; Simotwo et al., 2018 ; Williams et al., 2019 ). $$WAI=\frac{\varSigma FiWi}{\varSigma Fi}$$ 2 Where F = frequency of response; W = weight of each score; and i = score (0 = not important; 1 = less important; 2 = moderately important; 3 = highly important). Barriers of climate variability adaptation strategies To investigate the key barriers that hinder the implementation of climate variability adaptation measures, a ranking was performed using the Problem Confrontation Index (PCI). PCI serves as a crucial instrument for prioritizing the most pressing obstacles that impede the execution of strategies for adapting to climate change (Masud et al., 2017 ; Popoola et al., 2020 ). The research utilized a 4-point Likert scale to gauge problem-confrontation scores. Farmers were tasked with providing responses to 10 climate-related issues as part of the adaptation process. Each problem was assigned scores of 3, 2, 1, and 0 to indicate a high problem, medium problem, low problem, or no problem at all, respectively. The utilization of PCI is fitting as it facilitates the identification and analysis of the most crucial challenges confronting the implementation of adaptation strategies (Gemeda et al., 2023 ; Masud et al., 2017 ; Pickson & He, 2021 ). The PCI is estimated as follows: $$PCI=\left(\text{P}\text{N} \text{x} 0\right) + \left(\text{P}\text{L} \text{x} 1\right) + \left(\text{P}\text{M} \text{x} 2\right) + \left(\text{P}\text{H} \text{x} 3\right)$$ 3 where PCI = problem confrontation index, P H = number of farmers having high problem, P M = number of farmers having medium problem, P L = number of farmers having low problem, and P N = number of farmers having no problem. Multinomial logit model In this investigation, factors affecting farmers’ choice of climate variability adaptation strategies were estimated using a multinomial logit (MNL) model. The application of the MNL model was grounded in existing literature concerning the factors that influence farmers' adaptation to climate variability (Megersa et al., 2022 ; Teshome et al., 2021 ). This model is well-suited for this type of analysis because it enables the examination of decisions across multiple categories, thereby facilitating the determination of choice probabilities for various categories (Belay et al., 2017 ). The model is specified as follows: Let farmer i decides to use the j th adaptation option if the perceived benefit from option j is greater than the utility obtained from other available options (for example, k ) depicted as: $${U}_{ij}=\left({\beta {\prime }}_{j}{X}_{i}+{\epsilon }_{j}\right)>{U}_{ik } \left({\beta {\prime }}_{k}{X}_{i}+{\epsilon }_{k}\right), k\ne j$$ 4 In this context, U ij and U ik denote the perceived utility by farmer i of adaptation options j and k, respectively. Xi represents a vector of explanatory variables that influence the choice of the adaptation option. βj and βk are parameters to be estimated, while εj and εk serve as the error terms. To illustrate the MNL model, let Y represent a random variable with values ranging from 1 to M, where M is a positive integer, and let X represent a set of conditioning variables. In this context, Y signifies adaptation options or categories, while X encompasses various household, institutional, and environmental attributes. The objective is to ascertain how alterations in the elements of X influence response probabilities P (Y = j|X), where j ranges from 1 to M, while holding other factors constant. Since the probabilities must sum to one, determining P (Y = j|X) relies on knowing the probabilities for j = 2 to M. Let X be a 1 × K vector with the first element being unity. Consequently, the probability that a household i with characteristics X selects adaptation option j is delineated as follows: $$P\left({\gamma }_{i}=j|\chi \right)= \frac{\text{exp}\left({X}_{j}{\beta }_{j}\right)}{ \left[1+{\sum }_{j=1}^{M }exp\left({X}_{j} {\beta }_{j}\right)\right] }$$ 5 In this equation, P represents probability, j denotes adaptation options, X signifies explanatory variables, and βj = k × 1 represents coefficients, where j ranges from 1 to M. The variance inflation factor (VIF) technique was utilized to identify multicollinearity issues among continuous explanatory variables, calculated as follows: $$VIF = \frac{ 1}{1-{{{R}_{j}}^{2}}_{ }}$$ 6 In addition, the contingency coefficient (CC) was used to check the presence of multicollinearity among dummy/categorical variables. The value of CC ranges between zero and one, where zero indicates no multicollinearity between the variables, whereas a value close to one indicates the presence of multicollinearity between the predictor variables. The formula for the contingency coefficient is as follows: $$CC=\sqrt{\frac{{x}^{2}}{\text{n}-{x}^{2}}}$$ 7 In this formula, CC represents the contingency coefficient, x² denotes the chi-square test, and ‘n’ signifies the total sample size. Variables the study This study included several explanatory and dependent variables. Smallholder farmers’ selection of different adaptation options was a dependent variable. The independent variables were factors determining the choice of both indigenous and introduced adaptation measures to negative effects climate variability. Thus, age of household heads, educational status, family size, farm size, farm experience, household income, crop failure, recurrent drought, access to credit, agroecological location, and climate perception were predictor variables used in this study (Table 2 ). Table 2 Description of explanatory variables and their hypothesized effect Explanatory variables Description and measurement Variable type Expected sign Age of household Age of household heads (HH) in years Continuous ± Educational level 1. illiterate, 2. read & write, 3. primary, 4. secondary Categorical + Family size Number of HH living in one house Continuous ± Household income Total annual income in Ethiopian Birr (ETB) Continuous + Crop failure 1, if HH has experienced crop failure; 0, otherwise Dummy + Access to credit 1, if HH has access to credit; 0, otherwise Dummy + Farm experience Total number of years that HH spent in farming Continuous + Recurrent drought 1, if HH aware of drought occurrence; 0, otherwise Dummy + Farm size Size of farm land owned by HH in hectare Continuous + Agroecology 1. Dega , 2. Woina Dega , 3. Kolla Categorical ± Climate perception 1, if HH perceived climate; 0, otherwise Dummy + 3. Results and Discussion 3.1. Households’ adoption of indigenous adaptation measures The result of the investigation revealed that farm households in the research area employed various indigenous adaptation strategies to lessen the adversative effects of climate fluctuations. Hence, the adoption of traditional irrigation (WAI = 2.23), adjusting planting dates (WAI = 2.20), and crop diversification (WAI = 2.18) were the three popular indigenous adaptation options implemented by smallholder farmers in Dega agroecology. The abundance of perennial rivers facilitated traditional irrigation's prominence in the Dega zone. Planting local crop varieties (WAI = 2.34), adjusting planting dates (WAI = 2.24), and traditional irrigation (WAI = 2.10) were the predominant indigenous adaptation practices in Woina Dega agroecology. Crop diversification (WAI = 2.41), planting local crop varieties (WAI = 2.33), and using organic fertilizers (WAI = 2.29) were the major indigenous adaptation practices in Kolla agroecology. Based on the overall WAI results, planting local crop varieties (WAI = 2.22), crop diversification (WAI = 2.15), and adjusting planting dates (WAI = 2.14) were the main indigenous adaptation options across the three agroecological zones. On the contrary, seasonal migration, reducing social and religious festivals, and decreasing the livestock population were identified as the least utilized adaptation strategies across all agroecological zones (Table 3 ). In line with this, Aidoo et al. ( 2021 ) found that shifting planting dates and crop diversification were the foremost adaptation mechanisms operated by farmers. Likewise, Bekuma et al. ( 2023 ) reported that mixed cropping, reducing social and religious ceremonies, and traditional mixed farming were the most important indigenous adaptation options employed by small-scale farmers to alleviate the impacts of climate change. Myeni and Moeletsi ( 2020 ) stated that water harvesting was the most popular adaptation tactic used by farmers. Furthermore, previous studies have indicated that aligning planting schedules with the rainy season emerged as the most preferred coping strategy by farm households (Antwi-Agyei & Frimpong, 2021 ; Chisale et al., 2024 ). According to Kom et al. ( 2023 ), indigenous knowledge has played a significant role in helping rural farmer households overcome difficulties presented by climate stressors and improve decision-making for adaptation. A one-way ANOVA was conducted to assess the variation in the mean scores of indigenous adaptation strategies across different agroecological zones. Thus, the results indicated statistically significant differences in the adoption of planting local crop varieties, traditional irrigation, use of organic fertilizers, crop diversification, and seasonal migration at p < 0.01. Similarly, reducing social and religious festivals and decreasing livestock numbers exhibited significant differences at p < 0.05. However, adjusting planting dates did not show significant variation, indicating homogeneity among farm households in adopting this adaptation measure across all agroecological zones (Table 3 ). Table 3. Indigenous adaptation strategies (n=338) Indigenous adaptation strategies Agro-ecological zones (WAI) Dega (n=120) Woina Dega (n=118) Kolla (n=100) Total (n=338) F-value WAI Rank WAI Rank WAI Rank WAI Rank Planting local crop varieties 1.93 5 th 2.34 1 st 2.33 2 nd 2.22 1 st 5.124*** Adjusting planting dates 2.20 2 nd 2.24 2 nd 2.04 5 th 2.14 3 rd 1.302 Ns Traditional irrigation 2.23 1 st 2.10 3 th 1.27 8 th 1.86 4 th 34.54*** Reducing social and religious festivals 1.52 6 th 1.77 7 th 1.83 7 th 1.67 7 th 3.87** Using organic fertilizers 1.96 4 th 1.98 5 th 2.29 3 th 2.10 5 th 21.95*** Reducing number of livestock 1.20 8 th 1.95 6 th 2.25 4 th 1.82 6 th 3.42** Crop diversification 2.18 3 rd 2.01 4 th 2.41 1 st 2.15 2 nd 6.64*** Seasonal migration 1.48 7 th 1.33 8 th 1.99 6 th 1.58 8 th 12.19*** Grand mean 1.83 1.97 2.05 1.95 14.51*** *** and ** show levels of significance at 1% and 5%, respectively, WAI= Weighted Average Index, and Ns=Not significant 3.2. Households’ adoption of introduced adaptation measures The result shows that the chief significant adaptation strategies introduced to lessen the influences of climate variability in the Dega zone include the application of inorganic fertilizers (WAI = 2.60), soil and water conservation practices (WAI = 2.38), and the use of improved crop varieties (WAI = 2.32). Application of inorganic fertilizers (WAI = 2.67), application of improved mixed farming (WAI = 2.42), and use of improved crop varieties (WAI = 2.39) were reported as the leading introduced adaptation strategies in the Woina Dega agroecology. Likewise, the survey results revealed that the application of inorganic fertilizers (WAI = 2.66), pesticides and herbicides (WAI = 2.55), and improved crop varieties (WAI = 2.54) were the dominant adaptation strategies introduced in Kolla agroecology. The results indicate that the application of inorganic fertilizers and the use of improved crop varieties were ranked similarly across the three agroecological zones (Table 4). The application of agroforestry was least utilized by smallholder farmers across all agroecological zones. Contrary to this, studies by Ullah et al. ( 2023 ) and Kandel et al. ( 2023 ) found that agroforestry is the most commonly adopted climate change adaptation strategy among farmers in mountainous areas. Overall, the weighted average importance (WAI) result indicated that the application of inorganic fertilizers (WAI = 2.64), adoption of improved crop varieties (WAI = 2.41), and use of pesticides and herbicides (WAI = 2.28) emerged as the three major adaptation strategies. In contrast, the adoption of agroforestry appeared as the least applied adaptation strategy. The F-test result further showed that there were no significant mean differences in most introduced adaptation strategies among the different agroecology groups. However, statistically significant mean differences were observed concerning the adoption of pesticides and herbicides and improved mixed farming at p < 0.01 and 0.05 levels of significance, respectively (Table 4). A study by Aidoo et al. ( 2021 ) reported that weather forecasting and growing resistant varieties were frequently employed as adaptation strategies by farm households. The findings of Bekuma et al. ( 2023 ) also found that smallholder farmers were more predisposed to adopt improved adaptation options than to use indigenous and improved adaptation strategies. Tabe 4. Introduced adaptation strategies (n=338) Introduced adaptation strategies Agro-ecological zones (WAI) Dega (n=120) Woina Dega (n=118) Kolla (n=100) Total (n=338) F-value WAI Rank WAI Rank WAI Rank WAI Rank Soil and water conservation practice 2.38 2 nd 2.24 5 th 2.11 4 th 2.26 4 th 2.00 Ns Using improved crop varieties 2.32 3 rd 2.39 3 rd 2.54 3 rd 2.41 2 nd 1.606 Ns Application of inorganic fertilizers 2.60 1 st 2.67 1 st 2.66 1 st 2.64 1 st .260 Ns Application of pesticides and herbicides 2.13 5 th 2.26 4 th 2.55 2 nd 2.28 3 rd 6.05*** Application of improved mixed farming 2.27 4 th 2.42 2 nd 2.10 5 th 2.25 5 th 3.06** Agro-forestry 1.33 6 th 1.50 6 th 1.61 6 th 1.47 6 th 1.28 Ns Grand mean 2.17 2.25 2.26 2.23 2.38 Ns *** and ** show significance levels at 1% and 5%, respectively, WAI= Weighted Average Index, and Ns=Not significant 3.3. Constraints of adaptation strategies Problem confrontation index (PCI) serves as a crucial method utilized in this investigation to prioritize the most significant barriers hindering the execution of adaptation strategies against climate variability (Gemeda et al., 2023 ; Masud et al., 2017 ; Popoola et al., 2020 ). The implementation of different adaptation strategies encounters several challenges, such as limited farm size (PCI = 694), insufficient access to climate data (PCI = 641), poor soil fertility (PCI = 639), absence of irrigation infrastructure (PCI = 623), and the elevated expenses of farm inputs (PCI = 610). This study identified that limited farm size, poor access to meteorological information, and infertile soil were the top three critical barriers to adaptation strategies. Meanwhile, lack of credit facilities and literacy levels were identified as minor impediments to adaptation approaches in the research area (Table 5 ). The findings of Gemeda et al. ( 2023 ) indicated that lack of irrigation facilities, high cost of farm inputs, and soil infertility were the main critical barriers to adaptation strategies. Similarly, Pickson and He ( 2021 ) reported that unpredictable weather, limited farm size, and inadequate farm labor were the major barriers that impeded the actual implementation of adaptation actions by small-scale farmers. Furthermore, Megersa et al. ( 2022 ) identified that a shortage of funds, small farm sizes, unpredictable weather patterns, insufficient access to crop season and weather forecasts, and limited availability of irrigation water constituted the primary impediments to implementing adaptation strategies. Table 5 Constraints of adaptation strategies (n = 338) Constraints Extent of problem confrontation PCI Rank No problem (0) Less problem (1) Moderately Problem (2) Highly Problem (3) Low literacy 122 96 64 56 392 9 Poor access to climate information 43 71 102 122 641 2 Lack of knowledge to adaptation options 52 78 93 115 609 6 Lack of credit services 130 85 79 44 375 10 Lack of irrigation facilities 34 55 83 134 623 4 Poor institutional support 54 68 116 100 600 7 High cost of farm inputs 36 92 112 98 610 5 Limited farm size 21 82 93 142 694 1 Lack of agricultural subsidies 140 70 34 94 420 8 Infertile soil 50 61 103 124 639 3 3.4. Determinants of indigenous adaptation strategies The multinomial logit model (MNL) was used to assess the factors affecting farmers’ choice of indigenous adaptation tactics against climate variability hazards. Table 6 displays the estimated coefficients of the MNL model along with their respective significance levels. Multicollinearity was checked using the variance inflation factor (VIF) and contingency coefficients (CC). The results of variance inflation factors for all continuous variables are less than 10, which indicates that all continuous explanatory variables have no serious multicollinearity problem. Similarly, the CC values showed no multicollinearity problem among the dummy/categorical variables. The chi-square statistics presented by the likelihood ratio test were highly significant (p < 0.01), suggesting that the model possessed substantial explanatory capability. The model outcome showed that the age of the household head is positively and significantly associated with most of the indigenous adaptation strategies. Keeping all other factors constant, an increment in the age of the household head leads to a higher likelihood of adjusting planting dates, engaging in water harvesting, utilizing organic fertilizers, reducing livestock numbers, and diversifying crops (Table 6 ). This trend is attributed to the accumulated experience of older farmers, which enhances their ability to evaluate the risks associated with adaptation choices. This finding aligns with Alemayehu et al. ( 2022 ) and Megersa et al. ( 2022 ), who also found a positive correlation between age and crop diversification. Education is strongly and positively associated with the application of traditional irrigation methods and organic fertilizers. This could be attributed to the fact that most households belong to the illiterate group, which uses traditional irrigation and organic fertilizers rather than modern irrigation and inorganic fertilizers. Thus, the model results found that illiterate farmers are more expected to adopt traditional irrigation and organic fertilizers by coefficients of 2.326 and 1.738, respectively, at P < 0.05. Similarly, the findings of Teshome et al. ( 2021 ) revealed that households with lower levels of education, particularly those who are illiterate, show a preference for utilizing organic fertilizers in crop production compared to educated households. In addition, Abebe and Debebe ( 2019 ) found that illiterate households prefer to use organic sources of fertilizer for crop production. Family size has a favorable and significant impact on adjusting planting dates at p < 0.1, indicating that households with large family sizes were less likely to adjust planting dates. In contrast, family size is positively correlated with the application of organic fertilizers at p < 0.05. This could be due to the large family size encourages the engagement of farmers in labor-intensive adaptation strategies. Likewise, studies by Getahun et al. ( 2021 ), Wodaje et al. ( 2020 ), and Hirpha et al. ( 2020 ), which suggests that households with larger family sizes possess greater labor resources, which enables farmers to effectively implement a range of adaptation strategies. Household income is positively and significantly correlated with several indigenous adaptation strategies. Accordingly, a one-unit rise in household income boosts the likelihood of adopting traditional irrigation methods, adjusting planting schedules, reducing livestock numbers, and expanding crop diversification. Contrary to our initial hypothesis, there is a negative and statistically significant correlation between household income and planting of local crop varieties. This suggests that households with higher income levels are less likely to engage in planting local crop varieties. This might be because households with higher incomes are more likely to plant improved crop varieties than local crop varieties. The findings of Adimassu and Kessler ( 2016 ) and Asayehegn et al. ( 2017 ) substantiate that households with a higher level of income are in a better position to implement improved adaptation options to offset climate variability impacts. The model results indicate that households experiencing crop failure are more likely to implement traditional irrigation (p < 0.1) and crop diversification (p < 0.05). This suggests that households with previous crop failures have a higher likelihood of adopting these adaptive strategies. Conversely, farmers with a history of crop failure are less likely to adopt local crop varieties (p < 0.01) and organic fertilizers (p < 0.05). Previous studies by Addis and Abirdew (2021), Darge et al. ( 2023 ), and Gemeda et al. ( 2023 ) also found that crop failures can strongly motivate households to adopt adaptation measures to mitigate the risk of future failures. Accessibility to credit has an adverse and statistically significant effect on numerous indigenous adaptation strategies. This indicates that households with access to credit are unlikely to plant local crop varieties, adjust planting dates, reduce the number of livestock, and attend social and religious festivals. In contrast, access to credit has a positive and significant influence on crop diversification at p < 0.1. This is because access to credit offsets financial constraints and enables the farmer to purchase different crop varieties. Similarly, previous studies reported that credit accessibility has a significant and positive influence on the practice of adaptation strategies (Mutunga et al., 2018 ; Ndamani & Watanabe, 2016 ; Saguye, 2016 ; Tessema, 2019 ). From the given indigenous adaptation options, farm experience has a positive and significant relationship with planting local crop varieties at p < 0.1. This implied that households with more farm experience were familiar with planting local crop varieties. Conversely, farm experience has a negative and significant association with crop diversification at p < 0.01. Thus, for a unit increase in farm experience, the likelihood of households diversifying crop production decreases by a coefficient of 1.512. This can be attributed to the fact that crop diversification is labor- and cost-intensive; hence, households with more farming experience are in the older age group, so they are unable to diversify crop production. This result is consistent with the findings of Aidoo et al. ( 2021 ), who reported that households with longer years of farm experience are less inspired to use labor-intensive adaptation strategies. However, Trinh et al. ( 2018 ) found that farmers with more farming experience has higher probabilities of changing crop varieties and monitoring weather forecasts to curtail the hostile effects of climate alterations. Farm size plays a vital role in the acceptance of local crop varieties, organic fertilizers, and crop diversification. This implies that as the farm area increases, so does the probability of using local crop varieties, organic fertilizers, and crop diversification by factors of 1.13, 0.67, and 1.707, respectively. This result is consistent with the findings of Alemayehu et al. ( 2022 ). On the other hand, Amare and Simane ( 2017 ) indicated that farmers with larger agricultural holdings were disinclined to embrace measures for climate change adaptation. The agro-ecological context plays a significant role in determining the utilization of various adaptation strategies in response to climate variability. The agro-ecological category ( Dega ) has a positive and significant effect on the adoption of planting local crop varieties and reducing the number of livestock (p < 0.01) compared with households residing in Kolla agroecology. This implies that households in the Dega agroecology are more inclined to use local crop varieties and reduce the number of livestock than those in the Kolla agroecology. In addition, the agro-ecological category ( Woina Dega ) showed a positive and significant relationship with traditional irrigation and crop diversification at p < 0.1 and 0.01, respectively. This indicates that households in Woina Dega agroecology have a tendency to adopt traditional irrigation and crop diversification more than those in the base category ( Kolla ). This result corresponds with previous findings that reported agroecological location is significant in determining the choice and implementation of adaptation strategies (Amare & Simane, 2017 ; Eshetu et al., 2021 ). Unexpectedly, the model outcome presented that household’s climate perception had a negative and significant effect on the adoption of some indigenous adaptation strategies. Accordingly, households that perceived climate attributes were less likely to implement traditional irrigation, reduce social and religious festivals, use organic fertilizers, and diversify crops. A similar finding was reported by Aidoo et al. ( 2021 ), who indicated that farmers’ who perceived climate change intensity were less likely to adopt both indigenous and introduced adaptation strategies. However, the findings of Getahun et al. ( 2021 ) showed that climate perception is the main variable determining the adoption of adaptation measures by farm households. The model estimation revealed that the likelihood of drought was significantly and positively linked with several adaptive agricultural practices: planting local crop varieties, adjusting planting dates, employing traditional irrigation methods, and utilizing organic fertilizers. Consequently, households aware of drought conditions were 4.994 times more likely to plant local crop varieties, 3.755 times more likely to adjust planting dates, 5.610 times more likely to use traditional irrigation, and 6.428 times more likely to apply organic fertilizers. Similarly, previous studies conducted by Darge et al. ( 2023 ), Alemu et al. ( 2019 ), and Khanal et al. ( 2018 ) demonstrated that households' exposure to drought significantly and positively influenced their decisions to adapt to climate change. Table 6 Determinants of indigenous adaptation strategies (n = 338) Explanatory variables Indigenous adaptation strategies PLCV APD TI RSRF UOF RNL CD Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Age of household .840 2.333*** 1.376* .625 3.237*** 1.640*** .965* Education (illiterate) .990 − .890 2.326** -1.277 1.738** − .872 .572 Family size − .345 -1.323* − .461 − .944 1.257** − .661 .544 Household income -1.934*** 2.110*** 2.548*** − .133 − .032 .889* 1.394** Crop failure (yes) -4.350*** − .592 2.548* .534 -2.814** -1.087 3.477** Access to credit (yes) -3.574*** -2.857*** − .544 -3.023*** 1.310 -2.085** 1.984* Farm experience .715* .499 − .205 − .033 − .620 − .173 -1.512*** Farm size 1.136** .354 − .081 .711 .670* .235 1.707*** Agroecology ( Dega ) 1.215 2.889*** 1.580 1.248 1.558 3.118*** .616 Woina Dega -1.375 .548 2.018* − .842 -2.972** .724 3.325*** Climate perception (yes) 1.246 2.945 -3.574** -3.214* -3.939** 1.872 -4.841*** Recurrent drought (yes) 4.994*** 3.755** 5.610*** 3.051 6.428*** .364 1.213 Reference category: Seasonal migration N o of observation = 338 Prob > chi 2 = 0.000 LR Chi 2 (84) = 627.019 Pseudo R 2 = .844 Log likelihood = − 738.736 ***, **, and * show significance levels at 1%, 5%, and 10%, respectively; PLCV—planting local crop varieties; APD—adjusting planting dates; TI—traditional irrigation; RSRF—reducing social and religious festivals; UOF—Using organic fertilizers; RNL—reducing number of livestock; CD—crop diversification; Coeff—coefficient. 3.5. Determinants of introduced adaptation strategies The results of the variance inflation factor (VIF) and contingency coefficients (CC) showed the absence of serious multicollinearity among variables. The likelihood ratio statistics, denoted by chi-square statistics (LR chi-square = 329.844), along with the chi-square statistics, were highly significant (p < 0.01), indicating the robust explanatory capability of the model. The pseudo-R 2 result of Cox and Snell was .623, indicating that the explanatory variables were jointly explained by 62.3% variance in the dependent variables (Table 7 ). The model result shows that household age has significantly and negatively influenced the adoption of inorganic fertilizers (p < 0.01). This indicates that as the household’s age increases, the likelihood of adopting inorganic fertilizers decreases by a factor of 1.080. Likewise, Teshome et al. ( 2021 ) reported that older households prefer to apply organic sources of fertilizer as compared with younger households for maize production. In contrast, household age has a positive and significant consequence on farmers’ probability of adopting herbicides and pesticides at p < 0.05. This shows that older households were more likely to intensify the adoption of pesticides and herbicides than younger farm households. This result is consistent with previous studies that reported a negative association between the age of the household head and the likelihood of using adaptation options to offset the negative impacts of climate variability (Asrat & Simane, 2018 ; Destaw & Fenta, 2021 ). The model outcome disclosed that the educational category (illiterate) has a negative and significant effect on improved crop varieties (p < 0.01), inorganic fertilizers (p < 0.5), and improved mixed farming (p < 0.01). This implies that illiterate household heads are less probable to use improved crop varieties, inorganic fertilizers, and improved mixed farming by a factor of 1.918, 0.960, and 1.758, respectively. Similarly, Abebe and Debebe ( 2019 ) reported that illiterate households are unlikely to apply inorganic sources of fertilizer as compared with educated households for crop production. As anticipated, family size has a significant positive impact on the possibility of using soil and water conservation (p < 0.1) and improved mixed farming (p < 0.01). This infers that for a unit increase in family size, the probability of adopting soil and water conservation and improving mixed farming increases by units of 0.722 and 1.252, respectively. This is because households with a large family size are more likely to implement labor-intensive adaptation measures. Previous studies have also found the significant role of large family sizes in adopting labor-intensive adaptation strategies (Hirpha et al., 2020 ; Marie et al., 2020 ; Teshome et al., 2021 ). Income obtained from farming has a favorable and statistically significant impact on households’ adoption of improved crop varieties (p < 0.05), inorganic fertilizers (p < 0.01), and improved mixed farming (p < 0.01). This indicates that higher income enhances households’ capacity to adapt to climate variability-induced risks. Thus, when a household’s income increases by one unit, the likelihood of implementing improved crop varieties, inorganic fertilizers, and mixed farming increases by 0.891, 1.302, and 1.250 times, respectively. Likewise, previous studies have shown that households with higher income levels are better able to perform adaptation options quickly than those with lower income levels (Adimassu & Kessler, 2016 ; Asayehegn et al., 2017 ; Darge et al., 2023 ). Crop failure shows a positive and statistically significant correlation with soil and water conservation (p < 0.05), improved crop varieties (p < 0.01), and improved mixed farming (p < 0.01). This suggests that crop failure increases the probability of adopting soil and water conservation, inorganic fertilizers, and improved mixed farming, with coefficients of 1.286, 2.114, and 2.276, respectively. This result aligns with earlier findings, which also reported a favorable and significant correlation between crop failure and various adaptation strategies (Addis & Abirdew, 2021; Onyeneke, 2021 ). Furthermore, the model results show that access to credit has a beneficial and noteworthy impact on the selection of all introduced adaptation strategies, except for soil and water conservation, which is statistically insignificant. Thus, households with good credit opportunities were more likely to use improved crop varieties, inorganic fertilizers, pesticides, and herbicides, as well as improved mixed farming. This is because the accessibility of credit allows farmers to purchase improved crop varieties and farm inputs. Likewise, a study by Trinh et al. ( 2018 ) found that households with access to credit sources are more prone to adapting to climate change. Contrary to our prior expectations, an increase in farming experience for a year decreases households’ likelihood of adopting soil and water conservation practices and inorganic fertilizers. This might be due to households with more farm experience being found in the category of older people, who tend to opt for organic fertilizers over inorganic fertilizers. However, previous studies have reported that experienced farmers have more knowledge and awareness of previous climate events and may consequently decide to use adaptation strategies as a response to climate change (Diallo et al., 2020 ; Sadiq et al., 2019 ). Farm size has a positive and significant impact on the adoption of inorganic fertilizers, pesticides, and, herbicides. This shows that as the farm size increases by one hectare, there is a greater likelihood of implementing inorganic fertilizer, pesticides, and herbicides. This result aligns with the conclusions drawn from earlier empirical studies that disclosed a positive association between farm size and climate change adaptation measures (Abera & Tesema, 2019 ; Destaw & Fenta, 2021 ; Negera et al., 2022 ). Households residing in different agroecologies employ varying adaptation strategies, likely because of differences in the rate of temperature and rainfall changes and their subsequent impacts across these zones. Specifically, the agroecological category ( Dega ) had a detrimental and statistically significant impact on the adoption of enhanced crop varieties, pesticides and herbicides, and improved mixed farming, with a coefficient of 2.456, 3.045, and 1.419, respectively. This suggests that households in the Dega agroecology are less inclined to adopt the abovementioned adaptation measures compared with households in the Kolla agroecology. In contrast, the use of inorganic fertilizers has a positive and significant association with Dega agroecology, indicating a greater tendency among households in this region to use such fertilizers compared with those in Kolla agroecology, with a significance level of p < 0.05. Moreover, the agroecological classification ( Woina Dega ) exhibited a notable and positive correlation with soil and water conservation practices and improved mixed farming. This suggests that households in the Woina Dega zone are more motivated to adopt measures for conserving soil and water and enhancing mixed farming techniques. The probability of embracing soil and water conservation methods and enhanced mixed farming in the Woina Dega zone increases by factors of 0.299 and 0.362, respectively, compared with the Kolla zone. Similarly, Eshetu et al. ( 2021 ) reported that farmers residing in different agroecological zones tend to select their own adaptation options, influenced by variations in the rate of temperature and rainfall changes. Climate perception positively and substantially affects the adoption of all introduced adaptation options, except for the application of pesticides and herbicides. Therefore, farm households that perceived climate fluctuations were more inclined to adopt soil and water conservation, improved crop varieties, inorganic fertilizers, and improved mixed farming. These results reveal that climate perception is the main prerequisite for smallholder farmers to adopt adaptation strategies. This result is consistent with prior research findings (Adeagbo et al., 2021 ; Tessema & Simane, 2021 ). Recurrent drought has become a significant factor in prompting the adoption of various adaptation strategies. The analysis indicates that households aware of drought occurrences over the past three decades are more likely to implement various adaptive measures. Specifically, households with greater awareness of drought are more likely to engage in climate variability adaptations such as using improved crop varieties (p < 0.05), undertaking soil and water conservation activities (p < 0.1), applying inorganic fertilizers (p < 0.01), and practicing improved mixed farming (p < 0.05). This is because these households understand the recurring nature of droughts, have higher risk aversion tendencies, can plan for future risks, and recognize the benefits of these adaptation options. Consequently, early knowledge of droughts significantly influences farmers' preferences for adaptation techniques, aligning with the previous findings (Dasmani et al., 2020 ; Mulwa et al., 2017 ; Onyeneke, 2021 ). Table 7 Determinants of introduced adaptation strategies (n = 338) Introduced adaptation strategies Explanatory variables SWC UICV AIF APH AIMF Coeff. Coeff. Coeff. Coeff. Coeff. Age of household .037 .240 -1.080*** 1.036** .144 Educational status (illiterate) − .586 -1.918*** − .960* .899 -1.758*** Family size .722* − .915** .264 − .936** 1.252*** Household income .517 .891** 1.302*** .391 1.250*** Crop failure (yes) 1.286* 2.114*** − .071 1.203 2.276*** Access to credit (yes) .408 1.971*** 1.078* 2.110*** 1.836*** Farm experience − .652*** − .287 -1.015*** .058 − .392 Farm size .110 .106 .361** 1.596*** .285 Agroecology ( Dega ) − .994 -2.456*** 1.490** -3.045*** -1.419* Woina Dega .299*** .001 .988 − .456 .362** Climate perception (yes) 1.333** 1.286* 1.670** .225 1.147* Recurrent drought (yes) .948* 1.320** 1.580*** .876 1.578** Reference category: Agroforestry N o of observation = 338 Prob > chi 2 = 0.000 LR Chi 2 (60) = 329.844 Pseudo R 2 = .623 Loglikelihood= -855.093 ***, **, and * show significance levels at 1%, 5%, and 10%; SWC—soil and water conservation; UICV—using improved crop varieties; AIF—application of inorganic fertilizers; APH—application of pesticides and herbicides; AIMF—application of improved mixed farming; Coeff—coefficient. 4. Conclusions This study examined smallholder farmers’ adaptation options, the obstacles they faced in implementing different adaptation measures, and determinants that affect the choice of adaptation strategies across different agroecological zones in Northwestern Ethiopia. Smallholder farmers have endeavored to lessen the negative effects of temperature and rainfall variability by embracing various indigenous and introduced adaptation measures. The study found that adopting local crop varieties, crop diversification, and adjusting planting dates were the dominant indigenous adaptation strategies. In contrast, the application of inorganic fertilizers, the adoption of improved crop varieties, and the use of pesticides and herbicides were the major adaptation strategies introduced across different agroecological zones. The results indicated statistically significant differences regarding the uptake of various adaptation measures across different agroecological zones. The findings revealed that limited farm size, inaccessibility of climate information, soil infertility, lack of irrigation facilities, and high costs of agricultural inputs were the major barriers to adaptation strategies. Hence, it is crucial for policymakers and government authorities to formulate a comprehensive strategy to address the current barriers to climate variability adaptation options in the research area. The MNL model pointed out that crop failure, access to credit, agroecological conditions, recurrent drought, climate variability perception, and income levels were the most significant factors determining the choice of adaptation measures. Given these findings, the study suggests that policymakers and government bodies should prioritize initiatives to raise awareness among farmers about climate variability adaptation strategies. This can be accomplished by enhancing early warning systems, bolstering awareness of climate variability, and facilitating more accessible and affordable credit facilities. Declarations Authors’ contribution statement Abebe Biresaw Bitew: Conducted material preparation, data collection, drafted the initial manuscript, and performed the analysis. Amare Sewnet Minale : reviewed, provided comments, edited, supervised, and approved the final manuscript. Funding This study did not receive any specific grant from funding agencies. Data availability statement Data will be made available on request. Declaration of competing interests The authors declared that they have no conflict of interest. References Abebe, G., & Debebe, S. (2019). Factors affecting use of organic fertilizer among smallholder farmers in Sekela district of Amhara region, Northwestern Ethiopia. Cogent Food & Agriculture , 5 (1), 1669398. https://doi.org/10.1080/23311932.2019.1669398 Abera, N., & Tesema, D. (2019). 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G., Jaleta, M., Tesfaye, K., Getnet, M., Tana, T., & Lakew, B. (2022). Perceived climate change and determinants of adaptation responses by smallholder farmers in Central Ethiopia. Sustainability , 14 (11), 6590. https://doi.org/10.3390/su14116590 Mulwa, C., Marenya, P., & Kassie, M. (2017). Response to climate risks among smallholder farmers in Malawi: A multivariate probit assessment of the role of information, household demographics, and farm characteristics. Climate Risk Management , 16 , 208-221. https://doi.org/10.1016/j.crm.2017.01.002 Mutunga, E. J., Ndungu, C. K., & Muendo, P. (2018). Factors influencing smallholder farmers’ adaptation to climate variability in kitui county, Kenya. Myeni, L., & Moeletsi, M. E. (2020). Factors determining the adoption of strategies used by smallholder farmers to cope with climate variability in the eastern free state, South Africa. Agriculture , 10 (9), 410. https://doi.org/10.3390/agriculture10090410 Ndamani, F., & Watanabe, T. (2016). Determinants of farmers’ adaptation to climate change: A micro level analysis in Ghana. Scientia Agricola , 73 , 201-208. https://doi.org/10.1590/0103-9016-2015-0163 Negera, M., Alemu, T., Hagos, F., & Haileslassie, A. (2022). Determinants of adoption of climate smart agricultural practices among farmers in Bale-Eco region, Ethiopia. Heliyon , 8 (7). https://doi.org/10.1016/j.heliyon.2022.e09824 Onyeneke, R. U. (2021). Does climate change adaptation lead to increased productivity of rice production? Lessons from Ebonyi State, Nigeria. Renewable Agriculture and Food Systems , 36 (1), 54-68. https://doi.org/10.1017/S1742170519000486 Pickson, R. B., & He, G. (2021). Smallholder farmers’ perceptions, adaptation constraints, and determinants of adaptive capacity to climate change in Chengdu. SAGE Open , 11 (3), 21582440211032638. https://doi.org/10.1177/21582440211032638 Popoola, O. O., Yusuf, S. F. G., & Monde, N. (2020). Information sources and constraints to climate change adaptation amongst smallholder farmers in Amathole District Municipality, Eastern Cape Province, South Africa. Sustainability , 12 (14), 5846. https://doi.org/10.3390/su12145846 Sadiq, M. A., Kuwornu, J. K., Al-Hassan, R. M., & Alhassan, S. I. (2019). Assessing maize farmers’ adaptation strategies to climate change and variability in Ghana. Agriculture , 9 (5), 90. https://doi.org/10.3390/agriculture9050090 Saguye, T. S. (2016). Determinants of smallholder farmers’ adoption of climate change and variability adaptation strategies: evidence from Geze Gofa District, Gamo Gofa Zone, Southern Ethiopia. Gamo Gofa Zone, Southern Ethiopia . Sertse, S. F., Khan, N. A., Shah, A. A., Liu, Y., & Naqvi, S. A. A. (2021). Farm households' perceptions and adaptation strategies to climate change risks and their determinants: Evidence from Raya Azebo district, Ethiopia. International Journal of Disaster Risk Reduction , 60 , 102255. https://doi.org/10.1016/j.ijdrr.2021.102255 Simane, B., Zaitchik, B. F., & Foltz, J. D. (2016). Agroecosystem specific climate vulnerability analysis: application of the livelihood vulnerability index to a tropical highland region. Mitigation and Adaptation Strategies for Global Change , 21 (1), 39-65. https://doi.org/10.1007/s11027-014-9568-1 Simotwo, H. K., Mikalitsa, S. M., & Wambua, B. N. (2018). Climate change adaptive capacity and smallholder farming in Trans-Mara East sub-County, Kenya. Geoenvironmental Disasters , 5 , 1-14. https://doi.org/10.1186/s40677-018-0096-2 Tesfahunegn, G. B., Mekonen, K., & Tekle, A. (2016). Farmers’ perception on causes, indicators and determinants of climate change in northern Ethiopia: Implication for developing adaptation strategies. Applied Geography , 73 , 1-12. https://doi.org/10.1016/j.apgeog.2016.05.009 Tesfaye, K., Seid, J., Getnet, M., & Mamo, G. (2016). Agriculture under a changing climate in Ethiopia: challenges and opportunities for research. Ethiopian J. Agric. Sci . Teshome, H., Tesfaye, K., Dechassa, N., Tana, T., & Huber, M. (2021). Smallholder farmers’ perceptions of climate change and adaptation practices for maize production in Eastern Ethiopia. Sustainability , 13 (17), 9622. https://doi.org/10.3390/su13179622 Tessema, A. K. (2019). Smallholder farmers’ perception and adaptation strategies to climate change and variability in Ankesha Guagusa District of Awi Zone, North Western Ethiopia. Journal of Resources Development and Management , 58 , 15-24. Tessema, I., & Simane, B. (2021). Smallholder Farmers’ perception and adaptation to climate variability and change in Fincha sub-basin of the Upper Blue Nile River Basin of Ethiopia. GeoJournal , 86 (4), 1767-1783. https://doi.org/10.1007/s10708-020-10159-7 Trinh, T. Q., Rañola Jr, R. F., Camacho, L. D., & Simelton, E. (2018). Determinants of farmers’ adaptation to climate change in agricultural production in the central region of Vietnam. Land use policy , 70 , 224-231. https://doi.org/10.1016/j.landusepol.2017.10.023 Ullah, A., Mishra, A. K., & Bavorova, M. (2023). Agroforestry adoption decision in green growth initiative programs: Key lessons from the billion trees afforestation project (BTAP). Environmental Management , 71 (5), 950-964. https://doi.org/10.1007/s00267-023-01797-x Usmail, A. J., Maja, M. M., & Lakew, A. A. (2023). Farmers’ perceptions of climate variability and adaptation strategies in the rural areas of Dire Dawa administration, eastern Ethiopia. Heliyon , 9 (5). https://doi.org/10.1016/j.heliyon.2023.e15868 Von Loeper, W., Musango, J., Brent, A., & Drimie, S. (2016). Analysing challenges facing smallholder farmers and conservation agriculture in South Africa: A system dynamics approach. South African Journal of Economic and Management Sciences , 19 (5), 747-773. https://doi.org/10.4102/sajems.v19i5.1588 Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman‐Morales, J., Bishop, D. A., Balch, J. K., & Lettenmaier, D. P. (2019). Observed impacts of anthropogenic climate change on wildfire in California. Earth's Future , 7 (8), 892-910. https://doi.org/10.1029/2019EF001210 Wodaje, G. G., Eshetu, Z., & Argaw, M. (2020). Local perceptions and adaptation to climate variability and change: In the Bilate watershed. African Journal of Environmental Science and Technology , 14 (11), 374-384. https://doi.org/10.5897/AJEST2020.2854 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Introduction","content":"\u003cp\u003eClimate variability has become one of the primary challenges confronting human beings across the globe (Belay et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kiddle et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Scientific evidence suggests that the Earth\u0026rsquo;s climate has rapidly shifted because of increased greenhouse gas emissions since the industrial revolution (Ghebrezgabher et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Megersa et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These changes manifested in higher average temperatures and alterations in global rainfall patterns, significantly impacting smallholder farmers in developing nations reliant on rained agriculture (Belay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) presents compelling evidence that the increasing trend of global mean temperature in the 21st century has contributed to global warming (Gemeda et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgriculture is widely acknowledged as the cornerstone for achieving rural development, food security, and adequate nutrition in developing countries (Myeni \u0026amp; Moeletsi, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Von Loeper et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the agricultural sector in these nations is significantly susceptible to climate variability and change, primarily due to its heavy dependence on climatic factors such as rainfall and temperature (Belay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saguye, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sub-Saharan Africa (SSA), where smallholder farmers predominantly engage in agriculture, is a global hotspot for climate change-induced impacts (Adeniyi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Matewos, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this region, agriculture directly employs approximately 175\u0026nbsp;million small-scale farmers who cultivate degraded lands with limited access to reliable water for irrigation (Matewos, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Farmers in this region face heightened vulnerability to the effects of climate fluctuations due to their reliance on rain-fed agriculture, limited use of irrigation, and weak adaptive capacity (Erdaw, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, there is an urgent need to implement adaptation strategies tailored to current and projected climate shifts in this region (Limantol et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Usmail et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgriculture holds a central position in Ethiopia\u0026rsquo;s economy, contributing 52% of the gross domestic product (GDP), employing 80% of the workforce, and generating 80.2% of foreign exchange earnings (Belay et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Deressa et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The agricultural sector, primarily characterized by small-scale mixed cropping and marked by low livestock productivity, wrestles with challenges like inadequate extension services (Tessema \u0026amp; Simane, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several factors contribute to the sector\u0026rsquo;s low productivity, including traditional farming methods, severe land degradation due to deforestation and overgrazing, insufficient institutional support, and climatic extremes like droughts and floods (Etana et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tesfahunegn et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding location-specific adaptation measures is pivotal for tailoring appropriate policy responses, as these need to be based on the unique vulnerability and sensitivity levels of each area (Asrat \u0026amp; Simane, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kahsay et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marie et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Simane et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous studies have identified various adaptation measures employed by farm households in Ethiopia to counteract the hostile impacts of climate variability. The most commonly cited adaptation strategies include crop diversification, soil and water conservation, irrigation use, agroforestry, adjusting planting dates, irrigation practices, seasonal migration, and crop rotation (Abera \u0026amp; Tesema, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Addis \u0026amp; Abirdew, 2021b; Alemayehu \u0026amp; Bewket, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alemayehu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bekuma et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hirpha et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mavhura et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Megersa et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sertse et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tesfaye et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Teshome et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there is a significant variation in the actual implementation of these adaptation options by smallholder farmers across different regions of the country (Alemayehu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This variation arises due to the constantly changing biophysical, socioeconomic, and institutional contexts in which these adaptation strategies are implemented (Alemayehu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bawakyillenuo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have identified significant factors that influence farmers\u0026rsquo; choices of adaptation strategies. For instance, Jawo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that sex, literacy level, farm experience, household size, accessibility of extension services, and credit access were the most significant factors influencing the choice of adaptation strategies. Megabia et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlighted that household age, educational level, on/off-farm income, landholding size, extension services, and climate information were influential factors affecting farmers decisions to adopt adaptation options. According to Bekuma et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) factors like availability of extension services, farm experience, market information, and household age notably influenced smallholder farmers' adoption of various indigenous and improved adaptation strategies. Furthermore, Sertse et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized household age, educational level, credit access, availability of extension services, and farm water accessibility as critical factors. Thus, there is an urgent need for comprehensive studies on indigenous and introduced adaptation measures among smallholder farmers that incorporate socio-economic, biophysical, and institutional factors.\u003c/p\u003e \u003cp\u003eAlthough numerous studies have explored options for adapting to climate variability among smallholder farmers in various regions of the country, none of the previous studies have comprehensively addressed both indigenous and introduced adaptation strategies across diverse agro-ecological zones. Agro-ecological based investigation of adaptation measures is crucial for developing and implementing effective measures to mitigate the adverse effects of climate fluctuations and alterations (Marie et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the study area lacks comprehensive documentation regarding the specific indigenous and introduced climate variability adaptation options adopted by small-scale farm households, as well as the factors influencing their choices. Therefore, this study aimed to explore the factors influencing the adoption of indigenous practices and introduced adaptation strategies to mitigate climate variability-related risks in the Ayehu watershed. Specifically, the study focused on: (1) identifying various indigenous and introduced adaptation strategies against the consequences of climate variability; (2) investigating determinant factors affecting farm households' choice of indigenous and introduced adaptation strategies in the Ayehu watershed, Northwest Ethiopia.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of investigation area\u003c/h2\u003e \u003cp\u003eThis study took place in the Ayehu watershed in the northwestern region of Ethiopia. It lies 137 km southwest of Bahir Dar, the capital of the Amhara National Regional State, and 450 km west of Addis Ababa, the capital of Ethiopia. Geographically, the watershed is positioned between 10\u0026deg; 30' 0\"-11\u0026deg; 0' 00\" N and 36\u0026deg; 40' 0\"-37\u0026deg; 0' 0\" E. The landscape features rolling terrain, including rough hills, towering mountains, and smooth plains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe research site includes three conventional agroecological regions: Highland (2300\u0026ndash;3200 m), Midland (1500\u0026ndash;2300 m), and low land (500\u0026ndash;1500 m). Highland agroecology constitutes the largest part of the watershed (47.6%) followed by \u003cem\u003eMidland\u003c/em\u003e (33.1%) agroecology (33.1%). Lowland agroecology occupies the remaining portion of the watershed (19.3%). The watershed experiences a distinctive bimodal rainfall pattern, known locally as \u003cem\u003eKiremt\u003c/em\u003e (the major rainy season) and \u003cem\u003eBelg\u003c/em\u003e (the minor rainy season). \u003cem\u003eKiremt\u003c/em\u003e typically occurs from June to September, whereas \u003cem\u003eBelg\u003c/em\u003e occurs from March to May. Annual rainfall varies between 1127.27 mm and 1680.70 mm, with an average annual rainfall of 1391.65 mm. The yearly low and high temperatures fluctuate between 12.2\u0026deg;C and 26.2\u0026deg;C.\u003c/p\u003e \u003cp\u003eAgriculture serves as the primary means of sustenance and livelihood for both rural and urban households in the study watershed. Diversified agro-ecological systems, characterized by distinct climatic, soil, and altitude variations, support the cultivation of various crops, including cereals, oilseeds, pulses, and vegetables. Mixed farming, which involves both crop cultivation and livestock production, is the predominant farming system in the area (Tessema, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The total population of the watershed is 217,665. From this, 8.2% of the population resides in urban areas, while the remaining 91.8% are in rural areas (CSA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Research methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Sampling strategy and sample size\u003c/h2\u003e \u003cp\u003eThis study used a multistage sampling procedure to select sample \u003cem\u003eKebeles\u003c/em\u003e (the lowest-level administrative units) and household heads. Firstly, the study watershed was purposefully selected because it is the most severely affected by climate variability and related risks and is characterized by three distinct agroecological zones: Highland, Midland, and Lowland (Tessema, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Secondly, three Kebeles (one from each agroecological zone) were randomly selected based on the assumption that farmers in different zones may exhibit variations in their indigenous knowledge and adaptation strategies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These differences may lead to varying adaptive capacities among smallholder farmers across different agroecological zones. Additionally, the impact of climate variability is expected to differ across these zones, leading to different adaptation strategies by smallholder farmers (Belay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Likinaw et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thirdly, a list of households for each Kebele was obtained from the rural Kebele administrative offices. Given that the population was homogeneous in terms of livelihood, a random sampling method was employed to select respondent household heads from these lists, proportional to the size of the Kebele sample. Consequently, 338 randomly selected households were chosen to obtain the necessary quantitative data using the Kothari (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) formula.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{n}=\\frac{{Z}^{2}P.Q.N}{{e}^{2}\\left(N-1\\right)+ {Z}^{2}.P.Q}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: n\u0026thinsp;=\u0026thinsp;the sample size; N\u0026thinsp;=\u0026thinsp;total number of households (2821); p\u0026thinsp;=\u0026thinsp;sample proportion (0.5); q\u0026thinsp;=\u0026thinsp;1-p; e\u0026thinsp;=\u0026thinsp;the margin of error/acceptable error considered (5%); Z\u0026thinsp;=\u0026thinsp;1.96 is the critical value at 95% confidence interval.\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\u003eSamples taken from each agroecology in the study watershed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro-ecology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample \u003cem\u003eKebeles\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal household size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDega\u003c/em\u003e (High land)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBekafta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWoina Dega\u003c/em\u003e (Mid-land)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSostu Segno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKolla\u003c/em\u003e (Lowland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDikuna Dereb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Sources of data and collection methods\u003c/h2\u003e \u003cp\u003eThis study gathered data from both primary and secondary sources. Primary data collection involved household surveys, focus group discussions (FGDs), and key informant interviews (KIIs), whereas secondary sources included a systematic review of relevant published research articles. A structured survey questionnaire, featuring both closed- and open-ended questions, was developed to explore farmers' adoption of indigenous and introduced adaptation strategies, factors influencing their selection of strategies, and barriers to implementation. To ensure simplicity and comprehension during primary data collection, the questionnaire was initially drafted in English and then translated into Amharic and Awigna. Before data collection, the questionnaire underwent a pretest with households from non-sampled Kebeles. Subsequently, the questionnaire was administered to 338 household heads through face-to-face interviews conducted by six trained enumerators. These interviews were arranged on convenient days near the farmers' villages based on scheduled appointments.\u003c/p\u003e \u003cp\u003eDistinct focus group conversations were held with elders, youth, and women in each \u003cem\u003eKebele\u003c/em\u003e, with each group consisting of 6\u0026ndash;8 participants. Likewise, key informant interviews were conducted with knowledgeable community members, including agricultural staff, government administrators, and NGO representatives. These discussions and interviews gathered additional qualitative information and validated the quantitative data obtained through the household survey. Semi-structured interview guides were used for both group discussions and interviews with key informants. Data collection for the survey occurred from August to October 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Analytical Methods\u003c/h2\u003e \u003cp\u003e \u003cb\u003eImportance of climate variability adaptation strategies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Weighted Average Index (WAI) was used to rank the importance of climate variability adaptation measures in the research area. Fourteen adaptation strategies were developed using a 4-point Likert scale, and households were subsequently interviewed to assess their relative importance. Thus, the relative importance of each climate variability adaptation strategy was calculated by the weighted average index (WAI) using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as reported in previous studies (Fagariba et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gemeda et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Simotwo et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$WAI=\\frac{\\varSigma FiWi}{\\varSigma Fi}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere F\u0026thinsp;=\u0026thinsp;frequency of response; W\u0026thinsp;=\u0026thinsp;weight of each score; and i\u0026thinsp;=\u0026thinsp;score (0\u0026thinsp;=\u0026thinsp;not important; 1\u0026thinsp;=\u0026thinsp;less important; 2\u0026thinsp;=\u0026thinsp;moderately important; 3\u0026thinsp;=\u0026thinsp;highly important).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBarriers of climate variability adaptation strategies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the key barriers that hinder the implementation of climate variability adaptation measures, a ranking was performed using the Problem Confrontation Index (PCI). PCI serves as a crucial instrument for prioritizing the most pressing obstacles that impede the execution of strategies for adapting to climate change (Masud et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Popoola et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The research utilized a 4-point Likert scale to gauge problem-confrontation scores. Farmers were tasked with providing responses to 10 climate-related issues as part of the adaptation process. Each problem was assigned scores of 3, 2, 1, and 0 to indicate a high problem, medium problem, low problem, or no problem at all, respectively. The utilization of PCI is fitting as it facilitates the identification and analysis of the most crucial challenges confronting the implementation of adaptation strategies (Gemeda et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Masud et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pickson \u0026amp; He, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The PCI is estimated as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$PCI=\\left(\\text{P}\\text{N} \\text{x} 0\\right) + \\left(\\text{P}\\text{L} \\text{x} 1\\right) + \\left(\\text{P}\\text{M} \\text{x} 2\\right) + \\left(\\text{P}\\text{H} \\text{x} 3\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ePCI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;problem confrontation index, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u003c/em\u003e\u003c/sub\u003e = number of farmers having high problem, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e = number of farmers having medium problem, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e = number of farmers having low problem, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e = number of farmers having no problem.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultinomial logit model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this investigation, factors affecting farmers\u0026rsquo; choice of climate variability adaptation strategies were estimated using a multinomial logit (MNL) model. The application of the MNL model was grounded in existing literature concerning the factors that influence farmers' adaptation to climate variability (Megersa et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Teshome et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This model is well-suited for this type of analysis because it enables the examination of decisions across multiple categories, thereby facilitating the determination of choice probabilities for various categories (Belay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model is specified as follows:\u003c/p\u003e \u003cp\u003eLet farmer \u003cem\u003ei\u003c/em\u003e decides to use the \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e adaptation option if the perceived benefit from option j is greater than the utility obtained from other available options (for example, \u003cem\u003ek\u003c/em\u003e) depicted as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${U}_{ij}=\\left({\\beta {\\prime }}_{j}{X}_{i}+{\\epsilon }_{j}\\right)\u0026gt;{U}_{ik } \\left({\\beta {\\prime }}_{k}{X}_{i}+{\\epsilon }_{k}\\right), k\\ne j$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this context, U\u003csub\u003eij\u003c/sub\u003e and U\u003csub\u003eik\u003c/sub\u003e denote the perceived utility by farmer i of adaptation options j and k, respectively. Xi represents a vector of explanatory variables that influence the choice of the adaptation option. βj and βk are parameters to be estimated, while εj and εk serve as the error terms.\u003c/p\u003e \u003cp\u003eTo illustrate the MNL model, let Y represent a random variable with values ranging from 1 to M, where M is a positive integer, and let X represent a set of conditioning variables. In this context, Y signifies adaptation options or categories, while X encompasses various household, institutional, and environmental attributes. The objective is to ascertain how alterations in the elements of X influence response probabilities P (Y\u0026thinsp;=\u0026thinsp;j|X), where j ranges from 1 to M, while holding other factors constant. Since the probabilities must sum to one, determining P (Y\u0026thinsp;=\u0026thinsp;j|X) relies on knowing the probabilities for j\u0026thinsp;=\u0026thinsp;2 to M. Let X be a 1 \u0026times; K vector with the first element being unity. Consequently, the probability that a household i with characteristics X selects adaptation option j is delineated as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$P\\left({\\gamma }_{i}=j|\\chi \\right)= \\frac{\\text{exp}\\left({X}_{j}{\\beta }_{j}\\right)}{ \\left[1+{\\sum }_{j=1}^{M }exp\\left({X}_{j} {\\beta }_{j}\\right)\\right] }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this equation, P represents probability, j denotes adaptation options, X signifies explanatory variables, and βj\u0026thinsp;=\u0026thinsp;k \u0026times; 1 represents coefficients, where j ranges from 1 to M.\u003c/p\u003e \u003cp\u003eThe variance inflation factor (VIF) technique was utilized to identify multicollinearity issues among continuous explanatory variables, calculated as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$VIF = \\frac{ 1}{1-{{{R}_{j}}^{2}}_{ }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn addition, the contingency coefficient (CC) was used to check the presence of multicollinearity among dummy/categorical variables. The value of CC ranges between zero and one, where zero indicates no multicollinearity between the variables, whereas a value close to one indicates the presence of multicollinearity between the predictor variables. The formula for the contingency coefficient is as follows:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$CC=\\sqrt{\\frac{{x}^{2}}{\\text{n}-{x}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formula, CC represents the contingency coefficient, x\u0026sup2; denotes the chi-square test, and \u0026lsquo;n\u0026rsquo; signifies the total sample size.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariables the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study included several explanatory and dependent variables. Smallholder farmers\u0026rsquo; selection of different adaptation options was a dependent variable. The independent variables were factors determining the choice of both indigenous and introduced adaptation measures to negative effects climate variability. Thus, age of household heads, educational status, family size, farm size, farm experience, household income, crop failure, recurrent drought, access to credit, agroecological location, and climate perception were predictor variables used in this study (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of explanatory variables and their hypothesized effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription and measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected sign\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 household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of household heads (HH) in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. illiterate, 2. read \u0026amp; write, 3. primary, 4. secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of HH living in one house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal annual income in Ethiopian Birr (ETB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, if HH has experienced crop failure; 0, otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, if HH has access to credit; 0, otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number of years that HH spent in farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrent drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, if HH aware of drought occurrence; 0, otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize of farm land owned by HH in hectare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. \u003cem\u003eDega\u003c/em\u003e, 2. \u003cem\u003eWoina Dega\u003c/em\u003e, 3. \u003cem\u003eKolla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, if HH perceived climate; 0, otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Households\u0026rsquo; adoption of indigenous adaptation measures\u003c/h2\u003e\n \u003cp\u003eThe result of the investigation revealed that farm households in the research area employed various indigenous adaptation strategies to lessen the adversative effects of climate fluctuations. Hence, the adoption of traditional irrigation (WAI\u0026thinsp;=\u0026thinsp;2.23), adjusting planting dates (WAI\u0026thinsp;=\u0026thinsp;2.20), and crop diversification (WAI\u0026thinsp;=\u0026thinsp;2.18) were the three popular indigenous adaptation options implemented by smallholder farmers in \u003cem\u003eDega\u003c/em\u003e agroecology. The abundance of perennial rivers facilitated traditional irrigation\u0026apos;s prominence in \u003cem\u003ethe Dega\u003c/em\u003e zone. Planting local crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.34), adjusting planting dates (WAI\u0026thinsp;=\u0026thinsp;2.24), and traditional irrigation (WAI\u0026thinsp;=\u0026thinsp;2.10) were the predominant indigenous adaptation practices in Woina \u003cem\u003eDega\u003c/em\u003e agroecology. Crop diversification (WAI\u0026thinsp;=\u0026thinsp;2.41), planting local crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.33), and using organic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.29) were the major indigenous adaptation practices in \u003cem\u003eKolla\u003c/em\u003e agroecology. Based on the overall WAI results, planting local crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.22), crop diversification (WAI\u0026thinsp;=\u0026thinsp;2.15), and adjusting planting dates (WAI\u0026thinsp;=\u0026thinsp;2.14) were the main indigenous adaptation options across the three agroecological zones. On the contrary, seasonal migration, reducing social and religious festivals, and decreasing the livestock population were identified as the least utilized adaptation strategies across all agroecological zones (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In line with this, Aidoo et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that shifting planting dates and crop diversification were the foremost adaptation mechanisms operated by farmers. Likewise, Bekuma et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that mixed cropping, reducing social and religious ceremonies, and traditional mixed farming were the most important indigenous adaptation options employed by small-scale farmers to alleviate the impacts of climate change. Myeni and Moeletsi (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) stated that water harvesting was the most popular adaptation tactic used by farmers. Furthermore, previous studies have indicated that aligning planting schedules with the rainy season emerged as the most preferred coping strategy by farm households (Antwi-Agyei \u0026amp; Frimpong, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chisale et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to Kom et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), indigenous knowledge has played a significant role in helping rural farmer households overcome difficulties presented by climate stressors and improve decision-making for adaptation.\u003c/p\u003e\n \u003cp\u003eA one-way ANOVA was conducted to assess the variation in the mean scores of indigenous adaptation strategies across different agroecological zones. Thus, the results indicated statistically significant differences in the adoption of planting local crop varieties, traditional irrigation, use of organic fertilizers, crop diversification, and seasonal migration at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Similarly, reducing social and religious festivals and decreasing livestock numbers exhibited significant differences at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. However, adjusting planting dates did not show significant variation, indicating homogeneity among farm households in adopting this adaptation measure across all agroecological zones (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable 3. Indigenous adaptation strategies (n=338)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIndigenous adaptation strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.517615176151764%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Agro-ecological zones (WAI) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.62264150943396%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDega\u0026nbsp;\u003c/em\u003e(n=120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79245283018868%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWoina Dega\u003c/em\u003e (n=118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eKolla\u003c/em\u003e (n=100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.62264150943396%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal (n=338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.555066079295154%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.352422907488986%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.740088105726873%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.555066079295154%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.352422907488986%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.555066079295154%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.352422907488986%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003ePlanting local crop varieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e5.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eAdjusting planting dates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e1.302\u003csup\u003eNs\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eTraditional irrigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e34.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eReducing social and religious festivals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e3.87**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Using organic fertilizers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e21.95***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Reducing number of livestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e3.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eCrop diversification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e6.64***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eSeasonal migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e12.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.18428184281843%\" valign=\"top\"\u003e\n \u003cp\u003eGrand mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.94308943089431%\" valign=\"top\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.723577235772358%\" valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.368563685636857%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.29810298102981%\" valign=\"top\"\u003e\n \u003cp\u003e14.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e***\u0026nbsp;and\u0026nbsp;**\u0026nbsp;show levels of significance at 1% and 5%, respectively, WAI= Weighted Average Index, and\u003cem\u003e\u0026nbsp;\u003c/em\u003eNs=Not significant\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Households\u0026rsquo; adoption of introduced adaptation measures\u003c/h2\u003e\n \u003cp\u003eThe result shows that the chief significant adaptation strategies introduced to lessen the influences of climate variability in the \u003cem\u003eDega\u003c/em\u003e zone include the application of inorganic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.60), soil and water conservation practices (WAI\u0026thinsp;=\u0026thinsp;2.38), and the use of improved crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.32). Application of inorganic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.67), application of improved mixed farming (WAI\u0026thinsp;=\u0026thinsp;2.42), and use of improved crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.39) were reported as the leading introduced adaptation strategies in the \u003cem\u003eWoina Dega\u003c/em\u003e agroecology. Likewise, the survey results revealed that the application of inorganic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.66), pesticides and herbicides (WAI\u0026thinsp;=\u0026thinsp;2.55), and improved crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.54) were the dominant adaptation strategies introduced in \u003cem\u003eKolla\u003c/em\u003e agroecology. The results indicate that the application of inorganic fertilizers and the use of improved crop varieties were ranked similarly across the three agroecological zones (Table 4). The application of agroforestry was least utilized by smallholder farmers across all agroecological zones. Contrary to this, studies by Ullah et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Kandel et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that agroforestry is the most commonly adopted climate change adaptation strategy among farmers in mountainous areas.\u003c/p\u003e\n \u003cp\u003eOverall, the weighted average importance (WAI) result indicated that the application of inorganic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.64), adoption of improved crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.41), and use of pesticides and herbicides (WAI\u0026thinsp;=\u0026thinsp;2.28) emerged as the three major adaptation strategies. In contrast, the adoption of agroforestry appeared as the least applied adaptation strategy. The F-test result further showed that there were no significant mean differences in most introduced adaptation strategies among the different agroecology groups. However, statistically significant mean differences were observed concerning the adoption of pesticides and herbicides and improved mixed farming at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and 0.05 levels of significance, respectively (Table\u0026nbsp;4). A study by Aidoo et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that weather forecasting and growing resistant varieties were frequently employed as adaptation strategies by farm households. The findings of Bekuma et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) also found that smallholder farmers were more predisposed to adopt improved adaptation options than to use indigenous and improved adaptation strategies.\u003c/p\u003e\n \u003cp\u003eTabe 4. Introduced adaptation strategies (n=338)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"728\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.417582417582416%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIntroduced adaptation strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.379120879120876%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Agro-ecological zones (WAI) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.203296703296703%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.13821138211382%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDega\u0026nbsp;\u003c/em\u003e(n=120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.829268292682926%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWoina Dega\u003c/em\u003e (n=118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.308943089430894%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eKolla\u003c/em\u003e (n=100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.10569105691057%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal (n=338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.617886178861788%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.443396226415095%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.084905660377359%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566037735849056%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566037735849056%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.084905660377359%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.084905660377359%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.084905660377359%\" valign=\"top\"\u003e\n \u003cp\u003eWAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.084905660377359%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003eSoil and water conservation practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u003csup\u003eNs\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003eUsing improved crop varieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e1.606\u003csup\u003e\u0026nbsp;Ns\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003eApplication of inorganic fertilizers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e.260\u003csup\u003eNs\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Application of pesticides and herbicides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e6.05***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Application of improved mixed farming \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e3.06**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003eAgro-forestry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e1.28\u003csup\u003eNs\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.46217331499312%\" valign=\"top\"\u003e\n \u003cp\u003eGrand mean \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.8404401650618984%\" valign=\"top\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07840440165062%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.464924346629986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;2.38\u003csup\u003e\u0026nbsp;Ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003e*** and ** show significance levels at 1% and 5%, respectively, WAI= Weighted Average Index, and Ns=Not significant\u003c/p\u003e\n \u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e\n \u003ch2\u003e3.3. Constraints of adaptation strategies\u003c/h2\u003e\n \u003cp\u003eProblem confrontation index (PCI) serves as a crucial method utilized in this investigation to prioritize the most significant barriers hindering the execution of adaptation strategies against climate variability (Gemeda et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Masud et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Popoola et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The implementation of different adaptation strategies encounters several challenges, such as limited farm size (PCI\u0026thinsp;=\u0026thinsp;694), insufficient access to climate data (PCI\u0026thinsp;=\u0026thinsp;641), poor soil fertility (PCI\u0026thinsp;=\u0026thinsp;639), absence of irrigation infrastructure (PCI\u0026thinsp;=\u0026thinsp;623), and the elevated expenses of farm inputs (PCI\u0026thinsp;=\u0026thinsp;610). This study identified that limited farm size, poor access to meteorological information, and infertile soil were the top three critical barriers to adaptation strategies. Meanwhile, lack of credit facilities and literacy levels were identified as minor impediments to adaptation approaches in the research area (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The findings of Gemeda et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that lack of irrigation facilities, high cost of farm inputs, and soil infertility were the main critical barriers to adaptation strategies. Similarly, Pickson and He (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that unpredictable weather, limited farm size, and inadequate farm labor were the major barriers that impeded the actual implementation of adaptation actions by small-scale farmers. Furthermore, Megersa et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified that a shortage of funds, small farm sizes, unpredictable weather patterns, insufficient access to crop season and weather forecasts, and limited availability of irrigation water constituted the primary impediments to implementing adaptation strategies.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConstraints of adaptation strategies (n\u0026thinsp;=\u0026thinsp;338)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eConstraints\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eExtent of problem confrontation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo problem (0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLess problem (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003cp\u003eProblem (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHighly\u003c/p\u003e\n \u003cp\u003eProblem (3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor access to climate information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of knowledge to adaptation options\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of credit services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of irrigation facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor institutional support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh cost of farm inputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited farm size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of agricultural subsidies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfertile soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Determinants of indigenous adaptation strategies\u003c/h2\u003e\n \u003cp\u003eThe multinomial logit model (MNL) was used to assess the factors affecting farmers\u0026rsquo; choice of indigenous adaptation tactics against climate variability hazards. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e displays the estimated coefficients of the MNL model along with their respective significance levels. Multicollinearity was checked using the variance inflation factor (VIF) and contingency coefficients (CC). The results of variance inflation factors for all continuous variables are less than 10, which indicates that all continuous explanatory variables have no serious multicollinearity problem. Similarly, the CC values showed no multicollinearity problem among the dummy/categorical variables. The chi-square statistics presented by the likelihood ratio test were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that the model possessed substantial explanatory capability.\u003c/p\u003e\n \u003cp\u003eThe model outcome showed that the age of the household head is positively and significantly associated with most of the indigenous adaptation strategies. Keeping all other factors constant, an increment in the age of the household head leads to a higher likelihood of adjusting planting dates, engaging in water harvesting, utilizing organic fertilizers, reducing livestock numbers, and diversifying crops (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This trend is attributed to the accumulated experience of older farmers, which enhances their ability to evaluate the risks associated with adaptation choices. This finding aligns with Alemayehu et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Megersa et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), who also found a positive correlation between age and crop diversification.\u003c/p\u003e\n \u003cp\u003eEducation is strongly and positively associated with the application of traditional irrigation methods and organic fertilizers. This could be attributed to the fact that most households belong to the illiterate group, which uses traditional irrigation and organic fertilizers rather than modern irrigation and inorganic fertilizers. Thus, the model results found that illiterate farmers are more expected to adopt traditional irrigation and organic fertilizers by coefficients of 2.326 and 1.738, respectively, at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Similarly, the findings of Teshome et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed that households with lower levels of education, particularly those who are illiterate, show a preference for utilizing organic fertilizers in crop production compared to educated households. In addition, Abebe and Debebe (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that illiterate households prefer to use organic sources of fertilizer for crop production.\u003c/p\u003e\n \u003cp\u003eFamily size has a favorable and significant impact on adjusting planting dates at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, indicating that households with large family sizes were less likely to adjust planting dates. In contrast, family size is positively correlated with the application of organic fertilizers at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This could be due to the large family size encourages the engagement of farmers in labor-intensive adaptation strategies. Likewise, studies by Getahun et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), Wodaje et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Hirpha et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), which suggests that households with larger family sizes possess greater labor resources, which enables farmers to effectively implement a range of adaptation strategies.\u003c/p\u003e\n \u003cp\u003eHousehold income is positively and significantly correlated with several indigenous adaptation strategies. Accordingly, a one-unit rise in household income boosts the likelihood of adopting traditional irrigation methods, adjusting planting schedules, reducing livestock numbers, and expanding crop diversification. Contrary to our initial hypothesis, there is a negative and statistically significant correlation between household income and planting of local crop varieties. This suggests that households with higher income levels are less likely to engage in planting local crop varieties. This might be because households with higher incomes are more likely to plant improved crop varieties than local crop varieties. The findings of Adimassu and Kessler (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Asayehegn et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) substantiate that households with a higher level of income are in a better position to implement improved adaptation options to offset climate variability impacts.\u003c/p\u003e\n \u003cp\u003eThe model results indicate that households experiencing crop failure are more likely to implement traditional irrigation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and crop diversification (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that households with previous crop failures have a higher likelihood of adopting these adaptive strategies. Conversely, farmers with a history of crop failure are less likely to adopt local crop varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and organic fertilizers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Previous studies by Addis and Abirdew (2021), Darge et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Gemeda et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) also found that crop failures can strongly motivate households to adopt adaptation measures to mitigate the risk of future failures.\u003c/p\u003e\n \u003cp\u003eAccessibility to credit has an adverse and statistically significant effect on numerous indigenous adaptation strategies. This indicates that households with access to credit are unlikely to plant local crop varieties, adjust planting dates, reduce the number of livestock, and attend social and religious festivals. In contrast, access to credit has a positive and significant influence on crop diversification at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1. This is because access to credit offsets financial constraints and enables the farmer to purchase different crop varieties. Similarly, previous studies reported that credit accessibility has a significant and positive influence on the practice of adaptation strategies (Mutunga et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ndamani \u0026amp; Watanabe, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Saguye, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tessema, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFrom the given indigenous adaptation options, farm experience has a positive and significant relationship with planting local crop varieties at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1. This implied that households with more farm experience were familiar with planting local crop varieties. Conversely, farm experience has a negative and significant association with crop diversification at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Thus, for a unit increase in farm experience, the likelihood of households diversifying crop production decreases by a coefficient of 1.512. This can be attributed to the fact that crop diversification is labor- and cost-intensive; hence, households with more farming experience are in the older age group, so they are unable to diversify crop production. This result is consistent with the findings of Aidoo et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported that households with longer years of farm experience are less inspired to use labor-intensive adaptation strategies. However, Trinh et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that farmers with more farming experience has higher probabilities of changing crop varieties and monitoring weather forecasts to curtail the hostile effects of climate alterations.\u003c/p\u003e\n \u003cp\u003eFarm size plays a vital role in the acceptance of local crop varieties, organic fertilizers, and crop diversification. This implies that as the farm area increases, so does the probability of using local crop varieties, organic fertilizers, and crop diversification by factors of 1.13, 0.67, and 1.707, respectively. This result is consistent with the findings of Alemayehu et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, Amare and Simane (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicated that farmers with larger agricultural holdings were disinclined to embrace measures for climate change adaptation.\u003c/p\u003e\n \u003cp\u003eThe agro-ecological context plays a significant role in determining the utilization of various adaptation strategies in response to climate variability. The agro-ecological category (\u003cem\u003eDega\u003c/em\u003e) has a positive and significant effect on the adoption of planting local crop varieties and reducing the number of livestock (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared with households residing in \u003cem\u003eKolla\u003c/em\u003e agroecology. This implies that households in the \u003cem\u003eDega\u003c/em\u003e agroecology are more inclined to use local crop varieties and reduce the number of livestock than those in \u003cem\u003ethe Kolla\u003c/em\u003e agroecology. In addition, the agro-ecological category (\u003cem\u003eWoina Dega\u003c/em\u003e) showed a positive and significant relationship with traditional irrigation and crop diversification at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and 0.01, respectively. This indicates that households in \u003cem\u003eWoina Dega\u003c/em\u003e agroecology have a tendency to adopt traditional irrigation and crop diversification more than those in the base category (\u003cem\u003eKolla\u003c/em\u003e). This result corresponds with previous findings that reported agroecological location is significant in determining the choice and implementation of adaptation strategies (Amare \u0026amp; Simane, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Eshetu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eUnexpectedly, the model outcome presented that household\u0026rsquo;s climate perception had a negative and significant effect on the adoption of some indigenous adaptation strategies. Accordingly, households that perceived climate attributes were less likely to implement traditional irrigation, reduce social and religious festivals, use organic fertilizers, and diversify crops. A similar finding was reported by Aidoo et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), who indicated that farmers\u0026rsquo; who perceived climate change intensity were less likely to adopt both indigenous and introduced adaptation strategies. However, the findings of Getahun et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that climate perception is the main variable determining the adoption of adaptation measures by farm households.\u003c/p\u003e\n \u003cp\u003eThe model estimation revealed that the likelihood of drought was significantly and positively linked with several adaptive agricultural practices: planting local crop varieties, adjusting planting dates, employing traditional irrigation methods, and utilizing organic fertilizers. Consequently, households aware of drought conditions were 4.994 times more likely to plant local crop varieties, 3.755 times more likely to adjust planting dates, 5.610 times more likely to use traditional irrigation, and 6.428 times more likely to apply organic fertilizers. Similarly, previous studies conducted by Darge et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Alemu et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Khanal et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that households\u0026apos; exposure to drought significantly and positively influenced their decisions to adapt to climate change.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDeterminants of indigenous adaptation strategies (n\u0026thinsp;=\u0026thinsp;338)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eExplanatory variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eIndigenous adaptation strategies\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLCV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAPD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRSRF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUOF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRNL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.333***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.376*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.237***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.640***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.965*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (illiterate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.326**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.738**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.323*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.257**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.934***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.110***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.548***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.889*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.394**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrop failure (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.350***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.548*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.814**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.477**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to credit (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.574***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.857***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.023***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.085**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.984*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.715*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.512***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.136**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.670*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.707***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgroecology (\u003cem\u003eDega\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.889***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWoina Dega\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.972**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.325***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate perception (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.574**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.214*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.939**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.841***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecurrent drought (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.994***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.610***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.428***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eReference category: Seasonal migration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e of observation\u0026thinsp;=\u0026thinsp;338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR Chi\u003csup\u003e2\u003c/sup\u003e (84)\u0026thinsp;=\u0026thinsp;627.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;738.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e***, **, and * show significance levels at 1%, 5%, and 10%, respectively; PLCV\u0026mdash;planting local crop varieties; APD\u0026mdash;adjusting planting dates; TI\u0026mdash;traditional irrigation; RSRF\u0026mdash;reducing social and religious festivals; UOF\u0026mdash;Using organic fertilizers; RNL\u0026mdash;reducing number of livestock; CD\u0026mdash;crop diversification; Coeff\u0026mdash;coefficient.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Determinants of introduced adaptation strategies\u003c/h2\u003e\n \u003cp\u003eThe results of the variance inflation factor (VIF) and contingency coefficients (CC) showed the absence of serious multicollinearity among variables. The likelihood ratio statistics, denoted by chi-square statistics (LR chi-square\u0026thinsp;=\u0026thinsp;329.844), along with the chi-square statistics, were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating the robust explanatory capability of the model. The pseudo-R\u003csup\u003e2\u003c/sup\u003e result of Cox and Snell was .623, indicating that the explanatory variables were jointly explained by 62.3% variance in the dependent variables (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe model result shows that household age has significantly and negatively influenced the adoption of inorganic fertilizers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that as the household\u0026rsquo;s age increases, the likelihood of adopting inorganic fertilizers decreases by a factor of 1.080. Likewise, Teshome et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that older households prefer to apply organic sources of fertilizer as compared with younger households for maize production. In contrast, household age has a positive and significant consequence on farmers\u0026rsquo; probability of adopting herbicides and pesticides at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This shows that older households were more likely to intensify the adoption of pesticides and herbicides than younger farm households. This result is consistent with previous studies that reported a negative association between the age of the household head and the likelihood of using adaptation options to offset the negative impacts of climate variability (Asrat \u0026amp; Simane, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Destaw \u0026amp; Fenta, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe model outcome disclosed that the educational category (illiterate) has a negative and significant effect on improved crop varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), inorganic fertilizers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.5), and improved mixed farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This implies that illiterate household heads are less probable to use improved crop varieties, inorganic fertilizers, and improved mixed farming by a factor of 1.918, 0.960, and 1.758, respectively. Similarly, Abebe and Debebe (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that illiterate households are unlikely to apply inorganic sources of fertilizer as compared with educated households for crop production.\u003c/p\u003e\n \u003cp\u003eAs anticipated, family size has a significant positive impact on the possibility of using soil and water conservation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and improved mixed farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This infers that for a unit increase in family size, the probability of adopting soil and water conservation and improving mixed farming increases by units of 0.722 and 1.252, respectively. This is because households with a large family size are more likely to implement labor-intensive adaptation measures. Previous studies have also found the significant role of large family sizes in adopting labor-intensive adaptation strategies (Hirpha et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marie et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Teshome et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIncome obtained from farming has a favorable and statistically significant impact on households\u0026rsquo; adoption of improved crop varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), inorganic fertilizers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and improved mixed farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that higher income enhances households\u0026rsquo; capacity to adapt to climate variability-induced risks. Thus, when a household\u0026rsquo;s income increases by one unit, the likelihood of implementing improved crop varieties, inorganic fertilizers, and mixed farming increases by 0.891, 1.302, and 1.250 times, respectively. Likewise, previous studies have shown that households with higher income levels are better able to perform adaptation options quickly than those with lower income levels (Adimassu \u0026amp; Kessler, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Asayehegn et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Darge et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCrop failure shows a positive and statistically significant correlation with soil and water conservation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), improved crop varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and improved mixed farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests that crop failure increases the probability of adopting soil and water conservation, inorganic fertilizers, and improved mixed farming, with coefficients of 1.286, 2.114, and 2.276, respectively. This result aligns with earlier findings, which also reported a favorable and significant correlation between crop failure and various adaptation strategies (Addis \u0026amp; Abirdew, 2021; Onyeneke, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFurthermore, the model results show that access to credit has a beneficial and noteworthy impact on the selection of all introduced adaptation strategies, except for soil and water conservation, which is statistically insignificant. Thus, households with good credit opportunities were more likely to use improved crop varieties, inorganic fertilizers, pesticides, and herbicides, as well as improved mixed farming. This is because the accessibility of credit allows farmers to purchase improved crop varieties and farm inputs. Likewise, a study by Trinh et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that households with access to credit sources are more prone to adapting to climate change.\u003c/p\u003e\n \u003cp\u003eContrary to our prior expectations, an increase in farming experience for a year decreases households\u0026rsquo; likelihood of adopting soil and water conservation practices and inorganic fertilizers. This might be due to households with more farm experience being found in the category of older people, who tend to opt for organic fertilizers over inorganic fertilizers. However, previous studies have reported that experienced farmers have more knowledge and awareness of previous climate events and may consequently decide to use adaptation strategies as a response to climate change (Diallo et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sadiq et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFarm size has a positive and significant impact on the adoption of inorganic fertilizers, pesticides, and, herbicides. This shows that as the farm size increases by one hectare, there is a greater likelihood of implementing inorganic fertilizer, pesticides, and herbicides. This result aligns with the conclusions drawn from earlier empirical studies that disclosed a positive association between farm size and climate change adaptation measures (Abera \u0026amp; Tesema, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Destaw \u0026amp; Fenta, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Negera et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHouseholds residing in different agroecologies employ varying adaptation strategies, likely because of differences in the rate of temperature and rainfall changes and their subsequent impacts across these zones. Specifically, the agroecological category (\u003cem\u003eDega\u003c/em\u003e) had a detrimental and statistically significant impact on the adoption of enhanced crop varieties, pesticides and herbicides, and improved mixed farming, with a coefficient of 2.456, 3.045, and 1.419, respectively. This suggests that households in the \u003cem\u003eDega\u003c/em\u003e agroecology are less inclined to adopt the abovementioned adaptation measures compared with households in the \u003cem\u003eKolla\u003c/em\u003e agroecology. In contrast, the use of inorganic fertilizers has a positive and significant association with \u003cem\u003eDega\u003c/em\u003e agroecology, indicating a greater tendency among households in this region to use such fertilizers compared with those in \u003cem\u003eKolla\u003c/em\u003e agroecology, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Moreover, the agroecological classification (\u003cem\u003eWoina Dega\u003c/em\u003e) exhibited a notable and positive correlation with soil and water conservation practices and improved mixed farming. This suggests that households in the \u003cem\u003eWoina Dega\u003c/em\u003e zone are more motivated to adopt measures for conserving soil and water and enhancing mixed farming techniques. The probability of embracing soil and water conservation methods and enhanced mixed farming in the \u003cem\u003eWoina Dega\u003c/em\u003e zone increases by factors of 0.299 and 0.362, respectively, compared with the \u003cem\u003eKolla\u003c/em\u003e zone. Similarly, Eshetu et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that farmers residing in different agroecological zones tend to select their own adaptation options, influenced by variations in the rate of temperature and rainfall changes.\u003c/p\u003e\n \u003cp\u003eClimate perception positively and substantially affects the adoption of all introduced adaptation options, except for the application of pesticides and herbicides. Therefore, farm households that perceived climate fluctuations were more inclined to adopt soil and water conservation, improved crop varieties, inorganic fertilizers, and improved mixed farming. These results reveal that climate perception is the main prerequisite for smallholder farmers to adopt adaptation strategies. This result is consistent with prior research findings (Adeagbo et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tessema \u0026amp; Simane, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRecurrent drought has become a significant factor in prompting the adoption of various adaptation strategies. The analysis indicates that households aware of drought occurrences over the past three decades are more likely to implement various adaptive measures. Specifically, households with greater awareness of drought are more likely to engage in climate variability adaptations such as using improved crop varieties (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), undertaking soil and water conservation activities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), applying inorganic fertilizers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and practicing improved mixed farming (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This is because these households understand the recurring nature of droughts, have higher risk aversion tendencies, can plan for future risks, and recognize the benefits of these adaptation options. Consequently, early knowledge of droughts significantly influences farmers\u0026apos; preferences for adaptation techniques, aligning with the previous findings (Dasmani et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mulwa et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Onyeneke,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDeterminants of introduced adaptation strategies (n\u0026thinsp;=\u0026thinsp;338)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eIntroduced adaptation strategies\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExplanatory variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUICV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIMF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoeff.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.036**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational status (illiterate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.918***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.960*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.758***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.722*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.915**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.936**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.252***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.891**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.302***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.250***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrop failure (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.286*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.114***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to credit (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.971***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.078*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.110***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.836***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.652***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.361**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.596***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgroecology (\u003cem\u003eDega\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.456***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.490**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.045***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.419*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWoina Dega\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.299***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.362**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate perception (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.333**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.286*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.670**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.147*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecurrent drought (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.948*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.320**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.580***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.578**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eReference category: Agroforestry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e of observation\u0026thinsp;=\u0026thinsp;338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR Chi\u003csup\u003e2\u003c/sup\u003e (60)\u0026thinsp;=\u0026thinsp;329.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoglikelihood= -855.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e***, **, and * show significance levels at 1%, 5%, and 10%; SWC\u0026mdash;soil and water conservation; UICV\u0026mdash;using improved crop varieties; AIF\u0026mdash;application of inorganic fertilizers; APH\u0026mdash;application of pesticides and herbicides; AIMF\u0026mdash;application of improved mixed farming; Coeff\u0026mdash;coefficient.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study examined smallholder farmers\u0026rsquo; adaptation options, the obstacles they faced in implementing different adaptation measures, and determinants that affect the choice of adaptation strategies across different agroecological zones in Northwestern Ethiopia. Smallholder farmers have endeavored to lessen the negative effects of temperature and rainfall variability by embracing various indigenous and introduced adaptation measures. The study found that adopting local crop varieties, crop diversification, and adjusting planting dates were the dominant indigenous adaptation strategies. In contrast, the application of inorganic fertilizers, the adoption of improved crop varieties, and the use of pesticides and herbicides were the major adaptation strategies introduced across different agroecological zones. The results indicated statistically significant differences regarding the uptake of various adaptation measures across different agroecological zones. The findings revealed that limited farm size, inaccessibility of climate information, soil infertility, lack of irrigation facilities, and high costs of agricultural inputs were the major barriers to adaptation strategies. Hence, it is crucial for policymakers and government authorities to formulate a comprehensive strategy to address the current barriers to climate variability adaptation options in the research area. The MNL model pointed out that crop failure, access to credit, agroecological conditions, recurrent drought, climate variability perception, and income levels were the most significant factors determining the choice of adaptation measures. Given these findings, the study suggests that policymakers and government bodies should prioritize initiatives to raise awareness among farmers about climate variability adaptation strategies. This can be accomplished by enhancing early warning systems, bolstering awareness of climate variability, and facilitating more accessible and affordable credit facilities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbebe Biresaw Bitew:\u003c/strong\u003e Conducted material preparation, data collection, drafted the initial manuscript, and performed the analysis. \u003cstrong\u003eAmare Sewnet Minale\u003c/strong\u003e: reviewed, provided comments, edited, supervised, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebe, G., \u0026amp; Debebe, S. 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Local perceptions and adaptation to climate variability and change: In the Bilate watershed. \u003cem\u003eAfrican Journal of Environmental Science and Technology\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(11), 374-384. https://doi.org/10.5897/AJEST2020.2854\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":"Climate variability, Determinants, Indigenous, Introduced, Multinominal logit model","lastPublishedDoi":"10.21203/rs.3.rs-4509680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4509680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdapting to climate variability is crucial for sustainable livelihoods in developing countries like Ethiopia, where rain-fed agriculture underpins the economy. This study aims to evaluate both indigenous and introduced adaptation measures across different agroecological zones, along with their determining factors. Data was collected from 338 farm households using structured and semi-structured questionnaires. The Weighted Average Index (WAI) was used to identify the most significant adaptation methods employed by farm households in various agroecological zones, while the Problem Confrontation Index (PCI) assessed the barriers hindering the implementation of these strategies. The multinomial logit model (MNL) was utilized to investigate the factors affecting farmers' choices of adaptation strategies. The results indicated that the most popular indigenous adaptation strategies were planting local crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.22), crop diversification (WAI\u0026thinsp;=\u0026thinsp;2.15), and adjusting planting dates (WAI\u0026thinsp;=\u0026thinsp;2.14). The introduced adaptation strategies included using inorganic fertilizers (WAI\u0026thinsp;=\u0026thinsp;2.64), applying improved crop varieties (WAI\u0026thinsp;=\u0026thinsp;2.41), and using pesticides and herbicides (WAI\u0026thinsp;=\u0026thinsp;2.24). PCI results revealed that the major barriers to adapting to climate variability were limited farm size (PCI\u0026thinsp;=\u0026thinsp;694), lack of access to climate information (PCI\u0026thinsp;=\u0026thinsp;641), poor soil quality (PCI\u0026thinsp;=\u0026thinsp;639), lack of irrigation facilities (PCI\u0026thinsp;=\u0026thinsp;623), and high input costs (PCI\u0026thinsp;=\u0026thinsp;610). The logit model identified several significant factors influencing farmers' preferences for adaptation measures, such as crop failure, credit availability, recurrent drought, climate variability perception, agroecological location, and household income. The study underscores the importance of understanding local-level factors that influence farmers' adaptation strategies to enhance their resilience to climate variability.\u003c/p\u003e","manuscriptTitle":"Determinants of smallholder farmers choice of adaptation strategies in response to the impacts of climate variability in the Ayehu watershed, Northwest Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 08:38:47","doi":"10.21203/rs.3.rs-4509680/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":"38355115-81ab-44ba-a94a-b8a88cd0a970","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-30T16:36:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 08:38:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4509680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4509680","identity":"rs-4509680","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}