Factors Affecting Climate Change Adaptation Strategies and Existing Barriers among Stallholder Farmers in Southern Ethiopia

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To assist farmers in dealing with climate change, it is critical to understand what factors influence their decision-making on how to adapt. Thus, the main aim of this study was to identify factors influencing farm households' adaptation choices in southern Ethiopia. To acquire quantitative data, a cross-sectional survey design was utilized with 371 randomly selected households. Focus group discussions, key informant interviews, and field observations were employed to gather qualitative data, complemented by a thorough review of relevant academic publications, and reports. This study employed descriptive statistics and the MVP model to investigate the factors driving adaptation attempts and the barriers impeding them. Among several climate change adaptation choices, seasonal migration was the most common among households, accounting for 52.6%. Other strategies, such as conservation tillage (51.5%) and drought-resistant crops (49.3%), were also commonly utilized, with shifting planting dates and irrigation following closely behind (48.2% and 38.3%). However, socioeconomic, environmental, and institutional factors play major roles in influencing the adoption of different climate change adaptation approaches. Thus, policies and programs aimed at reducing the effects of climate change should consider the important roles of these factors. adaptation options climate change multivariate probit model Figures Figure 1 Figure 2 1. Introduction The global climate has significantly changed over recent decades and continues to change at an unprecedented rate; climate change is firmly recognized as the most critical environmental concern confronting today's world [ 1 ]. There is mounting evidence that catastrophic climate change events are common and have adversely impacted smallholder farmers in developing nations who rely primarily on rain-fed farming. As a developing country, Ethiopia's agriculture, is predominantly rainfed, generates approximatly 45% of coutry’s GDP and 90% of its exports, and employs 85% of its population; Ethiopia has been seriously distressed by climate change[ 2 ]. Smallholder farmers with limited resources confront unique challenges in addressing these constraints. Climate change affects almost all societies and their activities in one or other ways, so societies must address and respond to its unanticipated deviations [ 3 – 4 ]. Farmholds in Ethiopia have already begun to adapt interventions to the negative consequences of climate change, but these efforts are still at a relatively early stage. It is more reasonable to state that the attempts were fragmentary and restricted[ 5 – 6 ]. Much of the real effort to adapt to climate change occurs through inappropriate approaches in the context of unfitting practices, inadequate institutional frameworks, and implementation strategies[ 7 – 8 ]. Moving beyond a general understanding, this study delves into the unique challenges faced by smallholder farmers in adapting to climate change. This deep context-specific analysis identifies key obstacles and translates them into actionable knowledge to empower them in the face of these challenges. Due to the uneven distribution of climate change impacts across geographical regions, response mechanisms must vary based on the specific types and extent of local climatic changes [ 9 – 11 ]. Adapting to the site-specific effects of climate change necessitates in-depth knowledge of local conditions, requiring a clear understanding of the situation at the site or household level [ 7 – 11 ]. Local-level evidence across agro-ecologies is crucial for refining interventions and ensuring effective and efficient adaptation options to address the adverse effects of climate change. Existing research in the study area, including [ 3 ], has explored farm household adaptation strategies in response to climate change on low land areas. Similarly, [ 6 ], examined the adaptation strategies employed by smallholder farmers in the Hobicha (lowland) district. In contrast, [ 11 ]; investigated the adoption of climate change adaptation strategies among maize-dependent smallholders in southern Ethiopia, offering valuable insights into the specific context and challenges faced by this population. However, these studies often lack a comparative analysis across multiple dimensions. Thus, this study examined the interplay between various adaptation options, influencing factors, and geographical contexts, leading to a richer understanding of climate resilience in agricultural communities. Significant shifts in climatic patterns can profoundly impact natural processes within watershed ecosystems, reshaping the spatial distribution and flow of water across landscapes [ 12 ]. The poorest and most vulnerable communities are often the most severly affected by climate change. They often live in areas that are already prone to drought or flooding, and they may lack the resources to adapt to changing conditions, such as the area where this study was conducted [ 13 ]. Therefore, water-related climate change adaptation has a pivotal role in achieving sustainable development [ 14 ]. Research in the Wolaita Zone has made valuable contributions to understanding climate change adaptation, but its focus has typically been narrow; and often confined to specific areas, such as highlands[ 15 ], lowlands[ 16 ], or livelihoods [ 17 ]. Notably, the crucial role of effective water management across different watersheds within the zone has largely not been explored. This study delves into these understudied areas, revealing their unique challenges and opportunities for building climate resilience through effective water management practices. Recognizing the complexity of climate change, farmers strategically combine diverse strategies, leveraging complementary benefits and exploring alternatives to navigate its challenges [ 18 ]. The selection of subsequent adaptation strategies by farmers may be partially influenced by the knowledge and experience gained from previously implemented strategies, creating a path-dependent decision-making process[ 1 , 19 ]. Previous studies have offered valuable insights into individual adaptation strategies and their influencing factors[ 4 , 16 , 20 ]. However, a critical gap remains: understanding how these strategies interact with and influence each other's effectiveness. This study delves deeper by examining the interconnectedness of various adaptation options, providing a more holistic perspective. Many studies across the nation have explored various measures for adapting to climate change and the factors that impact their adoption[ 1 , 5 , 21 ]. However, these studies often paint a one-sided picture, focusing solely on either the positive or negative effects of factors influencing adaptation choices. Going beyond existing research[ 22 – 23 ], this study uniquely fills a critical gap by identifying area-specific factors in addition to constraints that impede the adoption of effective adaptation techniques in Ethiopia and this specific study area. This information can inform targeted interventions and policies to boost adaptation efforts. Likewise, other studies have been conducted to assess the influence of climate change on Ethiopian agriculture and water resources[ 24 – 26 ]. However, these studies examined the monetary or yield impacts of climate change and acclaimed adaptation techniques but did not identify the factors influencing the selection of the suggested adaptation methods. As a result, adaptation techniques to climate change used by smallholder farmers and their determinant factors have not been effectively recognized and documented. To bridge the knowledge gap in this area, this study embarked on an investigation into the primary factors influencing smallholder farmers' selection of adaptation methods to climate change, along with the barriers hindering their implementation. Furthermore, there is a growing recognition that enhancing farmers' ability to confront and cope with the risks and challenges of a changing climate cannot be achieved merely by sound technology protocols or local practices[ 27 – 28 ]. The merging of reliable technical solutions and local practices is increasingly recognized as essential for progress. Current research offers valuable insights into potential adaptation approaches, but a significant knowledge gap exists concerning how farmers translate these approaches into tangible changes in their day-to-day operations as the climate rapidly shifts. Thus, our study aims to equip policymakers with actionable knowledge by identifying the primary factors influencing farm households' adaptation choices, thereby supporting the development of successful agricultural adaptation measures. 2. Materials and Methods 2.1 Study Area Description 2.1.1 Geographic Location The Wolaita zone, one of the zones in the southe region, is located 390 kilometers southwest of Addis Ababa, Ethiopia, along the bustling main road connecting Shashamane and Arba Minch [ 22 ]. Astronomically, the Wolaita Zone is situated between 6.40° and 7.10° north latitude and 37.40° and 38.20° east longitude, placing it at the heart of Ethiopia's diverse landscape (Fig. 1 ). The zone's diverse topography, ranging from highlands to lowlands, has fostered a rich cultural tapestry and a variety of agricultural techniques tailored to the specific microclimates of each location [ 10 ]. 2.1.2 Demographic characteristics According to [ 29 ], the total size of the population in the zone is estimated to be 2,326,307, while the total area of the zone is 451,170 hectares or 4511.7 km2. Of the total population, 1,013,516 (44%) were male and 1,312,791 (56%) were female. Concerning the distribution of the population, the vast majority of the population in the zone (78%) or 1,817,429 resided in rural areas and the remaining 426,650 (22%) were urban dwellers (Table 6). Of the total households, the average family size was 4.84 persons per a household. The data confirmed that the demographic structure of the zone is dominated by young people, with a high population growth rate (2.9% annually). Regarding population density, the Wolaita zone ranks among Ethiopia's most densely populated areas [ 30 ]. While the Zone has a significant population overall, the density varies considerably across its districts and agro-ecological zones. Among the study districts within the Wolaita Zone, Damote Gale has the highest population density, reaching 706 inhabitants per square kilometer. Damote Woyde is closly behind, with a density of 606 inhabitants per square kilometer [ 29 ]. In addition to the overall dependency ratio of 92% for the study area, there are notable differences between districts. Deguna Fango exhibited the highest dependency ratio at 116%, indicating a larger nonworking-age population relative to the working-age population. This information can be crucial for strategizing resource allocation and developing targeted programs to support different communities within the study area. 2.1.3 Climate and Agro ecologies of the Study Area Acording to the agroecological zone classification of Ethiopia, the study area is predominantly characterized by mid to high elevation regions (1500–2300 m.a.s.l.) agroecology (Table 1 ). However, the study area is generally classified into three agroecological zones; among them, Waina-Dega (midland) comprises approximatly 56% of the total area; the remaing 35% and 9% are described as Kola (lowland); and Dega (highland) respectively[ 31 ]. Each district within the zone has distinct challenges and opportunities, requiring tailored approaches to land management and sustainable development. Table 1 Agroecology zone classification in the study area No AEZs Climate Alt. (m) RF (mm/yr.) M.A.To (oC) 1 Kola (lowlands) Warm semiarid 500–1500 200–800 27.5–20 2 Woynadega (midlands) Cool sub- humid 1500–2300 800–1200 16.5/17.5–20 3 Dega (highlands) Cool & humid 2300–3200 1200–2200 11.5–16/17.5 Source: [ 25 ] The climatic characteristics of agroecological zones (AEZs) include geographical areas with similar climatic characteristics that shape their ability to support rainfed agriculture [ 32 ]. The rainfall in the area is inconsistent and unpredictable, and occurring in two distinct seasons. The first rainy season (Belg) lasts from March to May, while the second (Kremt) lasts from July to October, peaking between mid-June and August. Despite substantial differences in rainfall over the years, the area is primarily reliant on Belg rains, which last from the end of February until early April [ 28 ]. The overall mean annual rainfall in the zone ranges from 1000 mm to 1270 mm, with the maximum rainfall recorded in August [ 19 ]. The year with late and below normal Belg rains resulted in very poor prospects for June and July crops. Light showery rainfalls in November and December; this rain is vital for the growth of root crops such as cassava, sweet potato, and Irish potato in the zone, is currently absent [ 18 ]. This led to low production of these commonly utilized root crops in many parts of the zone; and which are generally important means of filling food shortfall. Furthermore, erratic rainfall has recently been a serious hindrance to farming productivity, particularly in the lowlands of the Wolaita zone. The temperature trends in the study area are generally high, with minimal fluctuations across seasons. The zone's average yearly maximum and minimum temperatures vary from 15.20°C to 31.40°C [ 32 ]. Thus, an increase in temperature determines the rate of evaporation, soil moisture content, and atmospheric humidity. High and unpredictable temperature patterns cause epidemics of human, animal, and crop diseases, losses in crop output and productivity; and unemployment, resulting in temporal migration in the area. Therefore, the negative effects of climate change have compelled them to implement adaptation strategies. 2.1.4 Livelihoods and adaptation to climate change Climate change poses a significant risk to the livelihoods and well-being of communities across the globe, and the Wolaita Zone in Ethiopia is no exception. Subsistence farmers, who rely on rain-fed agriculture for a living, are at the forefront of the climate crisis. They face a complex and interconnected set of challenges that threaten their food security and entire way of life. Supporting subsistence farmers through targeted adaptation efforts is not simply an act of charity; it is a strategic investment in a more resilient and food-secure future. By empowering these farmers, we unlock a cascade of positive outcomes, paving the way for a brighter tomorrow. Agriculture is the primary source of living in the study area, with crop production and livestock rearing taking precedence. The Wolaita zone was sub-divided into two livelihood zones: maize and root crops and ginger and coffee [ 18 ]. The majority of the zone's midland and dry midland terrains are covered by maize and root crops, while root crops and perennials are commonly grown in the highland [ 9 ]. While livestock enjoy the freedom of open grazing, the convenience of stall feeding, and tethered plots near homes, these practices might quietly change the local climate. Overgrazing, mismanagement of farmland, and the weight of an expanding population, aggravated by traditional livestock practices, contribute to a worrying shift in the climate, raising fears about the area's future. To respond to the problems caused by climate change and variability, farmers in the Wolaita Zone have developed a diverse arsenal of adaptation strategies[ 23 – 35 ]. These strategies encompass various approaches to land management, income generation, and resource utilization, allowing farmers to navigate a changing environment and ensure their long-term sustainability. This study has the potential to inform policymakers, development practitioners, and agricultural extension services to tailor their support and interventions to the specific needs of farmers in the country in general and the study area in particular. By bridging the gap between traditional knowledge and modern technology, this study can also play a crucial role in building a more resilient and sustainable agricultural sector in the face of climate change. Therefore, this study aimed to examine the alternative adaptation strategies they have practiced, the determinants of the choice of adaptation options, and the constraints of applying adaptation options to climate change adversity. 2.2 Research Approaches and Design The choice of research design is determined by the study's objectives and related research questions, not the researcher's preferences. A survey or cross-sectional study design was used to gather relevant data and provide useful information for this investigation. The cross-sectional research design requires data collection on multiple cases at the same time to compile a set of quantitative or qualitative data involving two or more variables, which is subsqently investigated to discover patterns or links [ 36 ]. In this regard, many of the household factors were used to analyse the association with alternative adaptation approaches and identify the limits. This research integrated qualitative and quantitative data within a mixed methods framework, grounded in specific philosophical principles [ 37 ]. A mixed research approach provides a greater grasp of a study problem than does using a quantitative or qualitative approach alone [ 36 ]. The advantage of utilizing a mixed research technique was that the strengths of one approach compensated for the weaknesses of the other, and a broad base of information was required to address the stated objectives. While the quantitative approach held greater weight due to the study's focus, both the quantitative and qualitative analyses enriched the research, offering complementary insights. The qualitative data were mainly used to supplement the quantitative findings and reach valid conclusions. The study opted for a pragmatic philosophical foundation, aligning with its mixed research approach and promoting the integration of different research methods. 2.3 Site Selection, Sampling Methods, and Sample Size Determination The success of any research project hinges on two crucial decisions: selecting the appropriate research area and determining the optimal sample size [ 38 ]. Both decisions go hand-in-hand, ensuring that a study yields reliable and meaningful results. Both probability and nonprobability sampling procedures were employed to select the sample households. To ensure that the study was representative of the entire zone, multistage sampling procedures were utilized to choose the districts /AEZs/ within the zone, the Kebeles in the chosen district, and farm households in each Kebeles for data collection. In the first stage of multistage sampling, the research district/AEZs/in the Bilate Wolaita subwatershed within the Wolaita zone was purposefully chosen. The Bilate Wolaita subwatershed was then subdivided into three AEZs to represent three AEZs in the study area: Dega (Damote Gale), Woyna Dega (Damote Woyde), and Kola (Duguna Fango). When selecting the study district /AEZs/, two main reasons were considered: The first reason was that the Bilate Wolaita subwatershed is high vulnerability to climate-related threats, including drought and flooding, as well as because of its location as a hotspot area in the zone. The second was the agro-ecological location, which included Dega, Woyna-Dega, and Kola, following Ethiopia's traditional AEZ classification. In this situation, the dominating agroecology of the district was used as a criterion. The second stage was the choice of study Kebeles, based on the characteristics of each AEZ of the Kebeles, which was used to stratify them within their respective AEZs (Dega, Woyna Dega, and Kola). To ensure representation across the different agroecological zones, three Kebeles were randomly chosen from each of the Dega, Woyna Dega, and Kola AEZs. This simple random sampling technique yielded a total of nine Kebeles for the study. Study participants were then recruited at the final stage of the multistage sampling process. Participants were selected by using systematic sampling from the ordered list of eligible individuals within each chosen kebele. To account for the varying sizes of Kebeles within each AEZ, the research employed probability proportional to size (PPS) sampling to select the respondents. The process of selecting households follows four key steps: First, a comprehensive list of all households in each AEZ is acquired from Kebele administrators. Second, the sample size (n) was calculated based on the total population (N) in each AEZ. Third, every household on the list is assigned a unique sequence number. Finally, systematic sampling with a fixed interval is used to select the final sample of households [ 39 ]. To ensure a representative sample for this heterogeneous and sizable population, we employed the (Kothari, 2004) sampling size rule, detailed in Equations 1 and 2. $$\:\mathbf{N}=1+\frac{\varvec{N}\varvec{*}\varvec{p}\varvec{*}\varvec{q}\varvec{*}{\varvec{Z}}^{2}}{{\varvec{e}}^{2}\left(\varvec{N}-1\right)+{\varvec{Z}}^{2}\varvec{*}\varvec{p}\varvec{*}\varvec{q}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:\mathbf{n}=1+\frac{9546\text{*}0.5\text{*}0.5\text{*}{1.96}^{2}}{{(0.05)}^{2}\left(9546-1\right)+{1.96}^{2}\text{*}0.5\text{*}0.5}=371\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ where: n: Sample size Z: Confidence level z score (95% = 1.96) p: Assumed population proportion (50%=0.5) q: Nonoccurrence probability (1 - p) N: Population size e: Margin of error (0.05) A total of 371 households were randomly selected for the study. This sample represents 38% of Dega (D/Gale District), 36% of Woyna Dega (D/Woyde District), and 26% of Kola (D/Fango District). 2.4 Data Required and Ways of Collection Having defined the target population, established the sampling frame, chosen a sampling process, and determined the size, the next critical step was to embark on the data collection. This phase involved gathering data from both primary and secondary sources to ensure a robust and multifaceted understanding of the research topic. The primary data came from a cross-sectional survey of 371 households drawn from the three study districts. Primary data collection relied on a strategic mix of instruments: standardized questionnaires administered to households, in-depth interviews with knowledgeable individuals, facilitated group discussions with community members, and direct observation of everyday practices and contexts. Primary data formed the core of the study, encompassing key aspects such as demographics, socioeconomic indicators, institutional structures, and environmental factors. To enrich the analysis, these primary data were complemented by relevant secondary data from reliable sources. 3. Model specification, variable description and data analysis 3.1 Model specifications For decision-making scenarios with more than two possible choices, the suitable regression models were multinomial logit (MNL), multinomial probit (MNP), or multivariate probit (MVP) models, depending on the specific characteristics of the data and the research question[ 1 , 41 , 42 ]. The multinomial logit (MNL) model exhibits robustness and computational efficiency but operates under the assumptions of independence between choice outcomes and mutual exclusivity of choice variables[ 43 ]. Consequently, the MNL model might not be suitable for scenarios where choices are potentially nonindependent or overlapping. Although the multinomial probit (MNP) model offers flexibility by relaxing the independence of irrelevant alternatives (IIA) assumption, accurately accounting for the simultaneous effects of explanatory variables on each possible outcome variable is challenging[ 44 ]. This limitation proved problematic because farmers' local adaptation options often displayed complex interdependencies, acting as either substitutes or complements for one another. The MVP model had an advantage over the MNL model in that it relaxed the IIA assumption, which is in many situations ideal[ 45 – 46 ]. To overcome limitations with independence assumptions, the study opted for a multivariate probit (MVP) model, efficiently analyzing interlinked choice effects and accounting for shared influences. The descriptive measures complemented the multivariate probit model, offering valuable insights into the data's characteristics and patterns using statistical software (STATA version 16). The MVP model stands out as a valuable tool for analysing farmers' complex adaptation choices, as it enables researchers to assess the interconnectedness of different adaptation measures and identify the factors driving their adoption[ 18 , 23 , 47 ]. To formulate a multivariate probit model, six dummy dependent variables were defined: change planning date, practice of small-scale irrigation; application of conservation tillage; diversification to nonfarm income sources (seasonal migration); mixed crop and legume production; and switching to drought resistanant crop varieties. The MVP model assumes that each subject has J distinct binary responses and unobserved latent variable Z. Let \(\:i=1...n\) be the independent observations, \(\:j=1...j\) be the available options for binary responses, and \(\:{X}_{i}\) be a matrix of covariates composed of any discrete or continuous variables. Let \(\:{Y}_{\varvec{i}\varvec{j}}={(Y}_{I1\dots\:}{Y}_{IJ})\) denote the J-dimensional vector of observed binary responses taking values of (0, 1) on the \(\:{i}^{th}\) household and \(\:{Z}_{ij}={(Z}_{I1},{\dots\:,Z}_{IJ})\) denoted a J-variate normal vector of latent variables computed in Eq. 3: $$\:\:Zij=xi\beta\:+\epsilon\:i,\:i,\dots\:,n\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ where: \(\:\:\beta\:=\left(\overrightarrow{\beta\:}1,\dots\:,\overrightarrow{\beta\:}j\right)\) is a matrix of unknown regression coefficients; εi is a vector of residual errors distributed as a multivariate normal distribution with zero means and univariance;and \(\:\epsilon\:i\:\\∼N(0,\:\:\epsilon\:)\) where \(\:\epsilon\:\) is the variance covariance matrix.The off-diagonal elements in the correlation matrix \(\:{\text{P}}_{\text{k}\text{j}}={\text{P}}_{\text{j}\text{k}}\) represent the unobserved correlation between the stochastic components of \(\:{K}^{th}\:and\:{J}^{th}\) options [ 48 ]. The relationship between \(\:{\text{Z}}_{\text{i}\text{j}}{\:\text{a}\text{n}\text{d}\:\text{Y}}_{\text{i}\text{j}}\) was computed via Eq. 4: $$\:{y}_{ij}=\left\{1if{z}_{ij}>0;\:0\:otherwise\right\}\:i=1,\dots\:,n\:and\:j=1,\dots\:,j\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ Then, the likelihood of the observed discrete variable was obtained by integrating the latent variables as indicated in Eq. 5. where, Ai1 is the interval (0, ꝏ) if \(\:{Y}_{ij}=1\) and the interval (-ꝏ, 1) otherwise and Ai1𝜱т \(\:(Z{Y}_{ij}=1\) ⃒Xi, β, ∑) \(\:{dz}_{ij}\) is the probability density function of the standard normal distribution. To interpret the effect of the explanatory variables on probability, the marginal effect was generally inferred as computed in Eq. 6: $$\:{\delta\:}_{ij}=\frac{{\partial\:p}_{ij}}{{\partial\:x}_{i}}={p}_{ji}\left[{\beta\:}_{j}-{\sum\:}_{k=0}^{j}{p}_{ik\:}{\beta\:}_{k}\right]={p}_{ij}\left[{\beta\:}_{j}-{\beta\:}^{\to\:}\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ where, \(\:{\delta\:}_{ij}\) denotes the marginal effect, of the explanatory variable on the probability that alternative j was chosen. The marginal effect is a key measure in this analysis, quantifying the expected change in the probability of making a particular choice for every one-unit increase in a specific explanatory variable (Mihiretu et al., 2019). This allows us to understand how much, on average, each factor influences the likelihood of selecting different options. 3.2 Basic definitions of the dependent and explanatory variables Dependent variables Dependent variables are the measured behaviors of households on adaptation strategies. In this study, six dependent variables mixed crops with legumes, adjusted planting dates, engaging in seasonal migration, shifting to drought tolerant crops, utilizing conservation tillage, and small-scale irrigation were selected based on their importance and prioritized by the farming communities from the existing strategies for further analysis. Crop with legume production Crop with legume production refers to the practice of integrating legumes, such as common beans, peas, chickpeas, cowpeas, lentils, pigeon peas, peanuts, and grass peas, into a cropping system [ 50 ]. This can be achieved through either rotation or intercropping with other perennial crops, forage grasses, or vegetable crops. Owing to their diverse benefits, legumes are valuable for both humans and animals as food, wood, and soil enhancers within agricultural and agroforestry systems. These benefits, particularly their ability to thrive in challenging conditions, make them a suitable adaptation option and thus, one of the dependent variables analysed in this study. Adjusting planting dates Adjusting planting and harvest times is a key strategy for farmers adapting to a changing climate. Studies suggest that strategically modifying growing seasons could significantly reduce yield losses in the face of future climate scenarios [ 51 ]. Due to its widespread adoption as an adaptation strategy in the study area, adjusting the planting date was included as one of the dependent variables analysed in this study. Seasonal migration Seasonal migration helps households build resilience before escalating climate impacts deplete resources, making local adaptation impossible and large-scale migration difficult [ 42 ]. Economic advantages often drive migration, allowing individuals to diversify their income sources and reduce risk by spreading their financial dependence across multiple locations. Given its widespread adoption as a climate change adaptation strategy, seasonal migration was chosen as the dependent variable for this analysis. Drought-tolerant crops Drought, a constant environmental threat, significantly reduces plant growth, biomass, quality, and energy production. This stress, caused by temperature fluctuations, light intensity, and low rainfall, subtly disrupts plant morphology, physiology, biochemistry, and ultimately, photosynthesis. Plants have evolved remarkable resilience, utilizing both resistance and adaptation strategies to persist in harsh conditions. Drought-tolerant crops, also known as water-efficient varieties, thrive with minimal water, even during periods of scarcity or limited irrigation [ 52 ]. Due to its prevalence as an adaptation strategy, the adoption of drought-tolerant crop varieties served as the dependent variable in this analysis. Conservation tillage Conservation tillage practices involve leaving crop residues such as corn stalks, stems, leaves, and legume seed pods on the field after harvest[ 49 ]. This study considered various practices under the umbrella of conservation tillage, including utilizing crop residue, grasses, and animal manure as soil mulch. These practices aim to minimize soil disturbance and maximize soil cover, promoting soil health and resilience. Due to its cost-effectiveness and minimal technological requirements, conservation tillage is the most widely adopted adaptation strategy, making it a suitable dependent variable for analysing household responses to climate change. Irrigation In the face of climate change, irrigation has emerged as a powerful adaptation strategy for agriculture-based livelihoods. By ensuring a consistent water supply, irrigation empowers farmers to cultivate a wider range of crops and extend their growing seasons, leading to long-term food security despite the challenges posed by unpredictable weather and drought[ 54 ]. Given its effectiveness as an adaptation strategy to climate change, irrigation was included as one of the dependent variables analysed in this study. Explanatory variables This variable can also be termed an independent variable. Independent variables can be used to predict the most important climate adaptation strategies identified. The 15 explanatory variables included socio-economic variables, namely, age, sex, education level, dependency ratio, farm size, farm experience, and livestock ownership. The Institutional variables considered in this study include access to and utilization of credit services, agricultural extension services, proximity to the nearest market, access to and utilization of climate information, and farmers' group membership. Finally, the environmental factors considered in this study encompass the diverse agro-ecologies of the study area, including kola (lowland), dega (highland), and woyna dega (midland). Age of household head (age) Age is a continuous explanatory variable measured in years. It is hypothesized that the age structure of a household (young, middle-aged, or elderly dominant) will influence the adoption of climate adaptation strategies, with the potential for both positive and negative impacts[ 11 ]. Therefore, age composition served as an independent variable for predicting these dependent variables (adaptation strategies). Gender of household head (sex) This variable indicates the sex of the person who manages the household and is usually responsible for its economic well-being. A dummy variable is coded as 1 if the household head is male; and 0 if the household head is femal. Due to cultural norms assigning domestic tasks to women and limiting their access to crucial resources such as land, cash, and labor; female-headed households often face greater challenges in adopting adaptation measures to climate change[ 49 ]. It is hypothesized that gender plays a role in climate change adaptation, with male-headed farmers exhibiting a greater tendency to adopt such strategies. Educational level (Continuous Variable) This variable measures the educational attainment of the household head, represented by the number of years of schooling completed. Higher levels of education are generally associated with the ability to utilize more diverse adaptation strategies[ 3 ]. Therefore, this study hypothesizes that there is a positive correlation between household head education and household adoption of different climate change adaptation strategies. Dependency Ratio (Continuous Variable) This variable represents the proportion of individuals within the household who are considered economically inactive (children under 15 and adults 65 years and older) compared to those in the active working age range (15–64 years old). Households with a greater proportion of working-age members are generally expected to have a greater capacity to adopt various climate change adaptation strategies [ 55 ]. Therefore, this study hypothesizes that there is a negative correlation between the dependency ratio and the adoption of adaptation strategies. Farm Size (Continuous Variable) This variable represents the total area of land owned by a household. Larger farm sizes generally provide greater resources and opportunities for implementing diverse adaptation strategies in response to climate change impacts[ 56 ]. Therefore, this study hypothesized that thee is a positive correlation between farm size and the adoption of such strategies in response to climate change. Farming experience (Continuous Variable) This variable represents years of farming experience, and was measured as a continuous variable. Farmers with more years of experience are more likely to adopt climate change adaptation strategies because they have better knowledge about changing weather patterns[ 57 ]. While age often correlates with experience, the specific circumstances within this community suggest that this relationship may not hold true. Information sharing and technology access allow some younger farmers to accumulate more relevant agricultural knowledge than older farmers. Farmers with greater experience are expected to be more likely to adopt climate change adaptation strategies. Livestock Ownership (Continuous Variable) Total livestock ownership, measured in Tropical Livestock Units (TLU), reflects a household's dependence on natural resources such as grazing land and water. Different livestock types have varying vulnerabilities to climate change impacts such as droughts and floods. Therefore, farmers with larger livestock holdings are expected to be more likely to adopt climate change adaptation strategies[ 3 ]. Credit access and use Access to and use of credit, represented by a dummy variable (1 if the household has accessed credit, 0 otherwise), facilitates access to alternative income opportunities[ 58 ]. This financial resource enables investment in adaptation strategies, potentially leading to a positive correlation between credit utilization and the adoption of climate change adaptation practices. Access to and use of agricultural extension This is a dummy variable taking a value of 1 if the household has accessed and utilized agricultural advisory services, and 0 otherwise. Utilizing agricultural extension services equips farmers with valuable, climate-smart knowledge and practices[ 59 ]. This empowers them to adapt their agricultural practices to a changing climate, fostering resilience and increasing their chances of success in a dynamic environment. Therefore, a positive correlation is expected between access to and use of agricultural extension and the adoption of climate change adaptation strategies. Market proximity This continuous variable, measured in walking hours, represents the accessibility of markets for livestock, petty trade, and other products. Proximity to markets is expected to positively influence farmers' decisions to adopt climate change adaptation strategies[ 56 ]. This is because easier access to markets facilitates the sale of agricultural products, potentially generating income that can be reinvested in adaptation measures. Therefore, a shorter distance to the nearest market is hypothesized to be positive correlated with the adoption of climate change adaptation strategies. Access to and use of climate information This is a dummy variable, that takes a value of 1 if the household accesses climate information through radio or agricultural extension services, and 0 otherwise. Access to such information allows farmers to make informed decisions about adapting their agricultural practices to changing climate conditions[ 60 ]. Therefore, a positive correlation is expected between access to climate information and the adoption of climate change adaptation strategies. Farmer group membership This is a dummy variable, that takes a value of 1 if the household participates in farmer organizations, and 0 otherwise. Although farmer-to-farmer knowledge sharing within these groups could be a powerful tool for promoting climate adaptation strategies, their current focus seems to be primarily on local political and social networking issues. Therefore, an uncertain correlation between farmers' group membership and the adoption of climate change adaptation strategies is expected (Marie et al., 2020). It could be either positive or negative depending on the nature of the information and knowledge shared within the group. Local agroecology (kola) This is a dummy variable representing the agroecological setting based on Ethiopia's traditional classification. A value of 1 indicates that the farm household resides in the "kola" agroecological zone (lowland), while 0 indicates residence in another agroecological zone. Due to their high exposure to climate extremes, farmers residing in "kola" agroecology areas (lowlands) often adopt various adaptation strategies suitable for their specific conditions[ 3 ]. Therefore, being located in the Kola agroecology is expected to be positivily correlated with the adoption of climate change adaptation strategies. Local agroecology (Dega) This dummy variable, based on Ethiopia's traditional agroecological zone classification, pinpoints a farm household's location within the "Dega" (highland) agroecology. The distinct climate challenges faced by residents in this zone necessitate the adoption of various adaptation strategies. Therefore, residing in the Dega agroecology area is expected to positively correlate with the adoption of climate change adaptation practices[ 20 ]. Local agroecology (Woyna Dega) This dummy variable represents residence in the "Woyna Dega" (midland) agroecology zone based on Ethiopia's traditional classification. Compared to "kola" (lowland) agroecology, Woyna Dega (midland) experiences fewer extreme climate events. This leads to the expectation that farmers residing there may adopt fewer climate-specific adaptation strategies. Therefore, this study hypothesizes an uncertain correlation between Woyna Dega's residence and the adoption of climate change adaptation practices[ 62 ]. The specific direction of the correlation (positive or negative) could depend on the types of adaptation strategies considered and the overall approach to climate change management within the Woyna Dega agroecology. Table 2 Summary of dependent and independent variables for MVP model regression Variables Description Measurement Mean SD Dependent variables Change planting date Dummy (1 = yes, 0 = no) 0.48 0.50 Adopt drought-resistant crop Dummy (1 = yes, 0 = no) 0.49 0.50 Nonfarm income(migration) Dummy (1 = yes, 0 = no) 0.53 0.50 Crop with legume production Dummy (1 = yes, 0 = no) 0.49 0.50 Irrigation Dummy (1 = yes, 0 = no) 0.38 0.49 Conservation tillage Dummy (1 = yes, 0 = no) 0.51 0.50 Independent(explanatory) variables Age Age of the HHHs Continuous (year) 40.04 7.46 sex Sex of HHHs Dummy (1 = male, 0 = female) 0.95 0.24 Education Education status of HHHs Continuous (years) 1.45 2.40 Dependency ratio Number dependent family members Continuous (number) 2.05 0.97 Farmland size Size of farmland owned by the HHs Continuous (Ha) 0.77 0.64 Farming experience Number of years of farming Continuous (years) 17.74 5.58 Livestock Livestock owned by HHHs Continuous (TLU) 2.8 0.88 Credit Access to credit services Dummy (1 = yes, 0 = no) 0.40 0.49 Extension Access to Extension services Dummy (1 = yes, 0 = no) 0.52 0.50 Market Access to market Dummy (1 = yes, 0 = no) 0.48 0.50 Information Access to climate information Dummy (1 = yes, 0 = no) 0.50 0.50 Farmer group membership Belongs to a farmer’s group Dummy (1 = yes, 0 = no) 0.44 0.49 Kola Local agro-ecology kola Dummy (1 = yes, 0 = no) 0.50 0.50 Dega Local agro-ecology kola Dummy (1 = yes, 0 = no) 0.49 0.50 W/Dega Local agro-ecology woyna dega Dummy (1 = yes, 0 = no) 0.23 0.42 Source: Authors’ computation, 2024 4. Results and Discussion 4.1 Statistical Findings To reduce the negative impacts of climate change and control its future trends, smallholder farmers in the study area have adopted a variety of adaptation techniques. The majority of farmers in the study area who adopted adaptation strategies opted for adopting seasonal migration (52.6%), and conservation tillage (51.5%). Other widely implemented practices included adopting drought-resistant crops (49.3%). As confirmed by key informants, conservation tillage plays a crucial role in maintaining soil health and fertility. By leaving crop residues on the surface, plants protect the soil from erosion, promote moisture retention, and encourage beneficial microbial activity. This translates to better yields in the long run and contributes to a more sustainable farming system. Proactive migration can be a lifeline in the face of climate change, safeguarding lives from sudden disasters and building resilience through income diversification [ 63 ]. These findings further confirmed that growing crop types resistant to drought is an effective way to address the challenges posed by climate change, particularly in the hotter district of Deguna Fango (kola agroecology) in the study area. Our findings align with thos of [ 64 ], who reported observation in developing countries that growing drought-resistant crops is a key strategy for sustainable agricultural growth and food security in the face of climate change. Moreover, climate change adaptation research is built up on with the recurring themes of drought-resistant crops, seasonal migration, and conservation agriculture[ 65 – 67 ]. As indicated in Table 3 , among those smallholder farmers who employed adaptation measures, other specific adaptation techniques included mixed crop with legume production (48.50%), adjusting planting date (48.2%), and adopting small-scale irrigation (38.3%). Discussions with the Focus group confirmed that low adoption of irrigation in Dega and Woyna Dega agro ecologies stemmed from three interconnected challenges: insufficient water sources, scarce irrigation infrastructure, and knowledge gaps among farmers. Table 3 Adaptation Strategies Employed by Farm Households (N = 371) S/no Adaptation strategies Respondents Percentage 1 Irrigation 142 38.3 2 Mixed crop and legume production 180 48.5 3 Adjusting planting date 179 48.2 4 Seasonal migration 195 52.6 5 Shift to drought tolerance crop 183 49.3 6 Conservation tillage 191 51.5 Source: Authors’ computation, 2024 Farmers, on the other hand, may employ a complicated strategy to manage the varied challenges posed by climate change, combining or overlapping several adaptation techniques to preserve resilience [ 68 – 69 ]. The multivariate probit model's likelihood ratio test confirmed its overall significance (p < 0.01, 0.05, 0.10), indicating that the choices of climate change adaptation strategies are not independent. The results from the model further revealed positive correlations among several adaptation strategies, suggesting their mutual interdependence in building resilience. Irrigation and crop-legume production exhibited a positive relationship, highlighting their potential value in addressing water scarcity and soil health. The dense root systems of legumes help retain moisture in the soil, minimizing water evaporation and runoff. This natural water conservation allows more efficient irrigation use [ 70 ]. Similarly, adopting drought-resistant crops was positively associated with conservation tillage, suggesting the use of a combined approach to enhance drought tolerance and soil conservation. Combining legume intercropping with flexible planting based on seasonal factors offers a double win for farmers. Legumes boost soil health and nitrogen levels, while adaptable planting schedules capitalize on optimal climate and pest control, as research shows [ 71 ]. This flexibility further strengthens the link between seasonal migration and agricultural success. Precise planting timing around migration periods allows farmers to secure additional income without compromising the critical window for optimal crop growth. Furthermore, adjusting planting dates and practising conservation tillage displayed a positive interdependence, demonstrating their synergy in optimizing sowing periods and safeguarding soil health. Seasonal migration might be negatively associated with strategies such as conservation tillage and mixed cropping with legumes (Table 4 ). Thus, developing alternative income sources during nonmigration periods can lessen the reliance on migration earnings and create conditions for adopting sustainable agricultural practices. 4.2 Barriers to climate change adaptation A multitude of barriers prevent farmers in the study area from effectively combating climate change challenges through adaptation strategies. These include knowledge gaps on adaptation alternatives, a lack of information about the potential consequences of climate change, a lack of access to water resources, a lack of irrigation infrastructure, high-cost farm inputs, a shortage of farm size, inadequate institutional support, and a lack of motivation. Figure 2 reveals that the greatest hurdles to adapting to climate change are a lack of awareness about options (17%), water scarcity (15%), and limited access to climate information (14%). However, farmers were less concerned with high input costs (9%), lack of motivation (10%), and land shortages (11%) as barriers to adaptation. Our findings align with those of [ 72 , 50 ]; who identified a lack of readily available climate information, coupled with water scarcity and knowledge gaps, as significant constraints on farmers' ability to cope with climate change-induced hazards. This underscores the need for comprehensive interventions addressing information access, resource management, and skill development to bolster farmers' climate resilience. 4.3 Determinants of smallholder farmers' adaptive strategies The multivariate probit model, as detailed in the methodology, proved instrumental in identifying the complex interplay of factors shaping farm households’ choice of adaptation strategies in the face of climate change. This model allows researchers to explore the relationship between a set of independent variables and multiple dependent variables simultaneously, which is particularly useful when studying complex phenomena such as farmers' adaptation strategies to climate change [ 73 ]. By using this method, the researchers were able to analyse how various factors influence farmers' decisions to adopt specific adaptation strategies. As indicated in the study's findings, smallholder farmers in the study area frequently used six key adaptation measures to combat the challenges of climate change: irrigation, mixed crops with legumes, adjusting planting dates, seasonal migration, shifting to drought tolerant crops and conservation tillage. The study also investigated how a variety of factors, including demographic characteristics, socioeconomic settings, institutional context, and availability of natural resources influence the adaptation strategies chosen by farmers (Table 4 ). Table 4 Multivariate probit parameter estimates for factors influencing climate change adaption Variables Irrigation legume production Adjusting planting date Seasonal migration Drought tolerance crop Conservation tillage Coef Std. Err. Coef Std. Err. Coef Std. Err. Coef Std. Err. Coef Std. Err. Coef Std. Err. Age -0.0171219* 0.0096735 -0.0041438 0.0091904 -0.0027007 0.0091049 0.0008089 0.0091269 0.0002953 0.0091646 -0.0007773 0.0091148 Sex 0.7503416** 0.342489 -0.1499492 0.2841586 -0.1326087 0.2864626 0.0445032 0.2781472 0.0192449 0.2798095 0.0510346 0.2973209 Education 0.016837 0.0297274 0.019503 0.028146 0.053674** 0.0284506 0.0219046 0.0280855 -0.0174568 0.0279303 0.0156053 0.0280536 D/ratio -0.1246347* 0.0749314 0.0084857 0.0707039 -0.0147756 0.0708747 -0.0401078 0.0703671 0.028311 0.0693062 -0.0384639 0.071489 Farm size 0.4930842*** 0.1218711 0.1863465* 0.1108907 -0.0858456 0.1076629 -0.0533822 0.1074704 -0.1291842 0.1092024 -0.0880682 0.1056002 Experience 0.0442101*** 0.0127496 0.0192095 0.0124268 -0.0026061 0.0124604 -0.0204023 0.0123905 0.0202624 0.0123163 0.0077152 0.0124302 TLU 0.0131702 0.081613 -0.0011831 0.0770905 -0.0092919 0.0777982 -0.0458336 0.0766949 0.0176054 0.0764699 0.167087** 0.0786229 Credit 0.0527264 0.1464825 0.3315724** 0.138972 -0.017992 0.1390819 -0.0909474 0.1383293 0.2703377** 0.1377767 0.3148891** 0.1401041 Extension -0.1397133 0.1411988 -0.202842 0.1341167 -0.0545458 0.1343632 0.1068957 0.1335078 0.3107024** 0.1330601 -0.5010264*** 0.1356523 Information 0.0330016 0.1436651 0.2211665 0.1363602 0.239375* 0.1372302 0.0830627 0.1354476 -0.0613239 0.1347973 -0.0960922 0.1364777 Market 0.0402624 0.1421287 0.2617187** 0.1351987 0.2406723* 0.1356692 -0.2410844* 0.1347042 0.0079196 0.1341821 0.2478563* 0.1368091 FGM -0.3452937** 0.1429497 0.152809 0.1355646 -0.0584141 0.1358805 0.0501195 0.1349629 -0.0301526 0.1345276 0.0358389 0.1368667 Kola 0.1961201 0.1422619 0.3469267** 0.1358219 0.217354 0.1362745 -0.0922454 0.1350683 0.0940952 0.1343274 0.3265518** 0.1369557 Dega 0.0524645 0.1426385 0.0254895 0.1352255 0.5065498*** 0.135916 0.2991447** 0.1345404 -0.2092351 0.1340391 -0.0298931 0.1358666 W/Dega 0.1952493 0.1690804 -0.0070473 0.1607432 0.2388351 0.1618797 0.3294452** 0.1634478 0.0653124 0.159943 0.1443841 0.1636869 Const -1.32172** 0.6781722 -0.7716498 0.621872 -0.3028682 0.6227885 0.4319757 0.6193538 -0.5764455 0.6192048 -5938135 0.6270044 Notes: ***, **, and * denote significe at the 1%, 5% and 10% levels respectively. Likelihood ratio test of rho chi2 (15) = 23.3521; prob > chi2 = 0.0000; Number of obs = 371; Wald chi2 (90) = 167.16. Source: Own Computation, 2022 1. Age of the household head Age, serving as a proxy for farming experience, had a significant influence on the adaptation strategies farmers chose in the face of climate change. A farmer's exposure to various agricultural experiences, systems, and seasons is influenced by their age [ 50 ]. However, age commonly serves as a proxy for experience, and some younger farmers, armed with new technologies and scientific insights, also play a crucial role in developing adaptation strategies. Age at which the household head was headed negatively and significantly affected the choice of irrigation adaptation strategy compared to choice of other adaptation strategies at less than a 10% significance level. This finding implies that as the age of the household head increases by one year, the probability of using irrigation as an adaptation strategy decreases by 1.7%. These findings further confirmed the hypothesis and indicated that older household heads were less likely to adopt irrigation for climate change adaptation (Table 4.10). The higher physical demands and skill requirements of irrigation practices could explain why older farmers, despite their extensive experience, are less likely to choose this appraoch as a climate change adaptation strategy [ 70 ]. According to the findings, younger household heads may be better suited to managing irrigation practices due to their greater physical agility and possibly greater risk tolerance. These findings are consistent with those of [ 74 – 75 ]; but diverge from those of [ 50 ]; who limited their view to experiences. Moreover, Triangulation of data from interviews and focus group discussions confirmed that age acts as a key factor influencing irrigation adoption, with younger farmers demonstrating a stronger propensity to embrace this practice. 2. Gender of the household head The gender of the household head plays a key role in shaping farmers' choices of adaptation strategies for climate change. Numerous studies have confirmed that gender plays a significant role in determining the adaptation measures that farmers choose to implement in the face of climate challenges[ 7 , 20 , 76 ]. This study revealed that the gender of the household head positively and significantly affects the choice of irrigation adaptation strategy compared with the choice of other adaptation strategies at the 5% significance level. This finding implies that a household headed by a male has a 75% greater probability of choosing irrigation as an adaptation strategy than other adaptation strategies. This can be attributed to the fact that male-headed households often have greater access to resources such as labor, finance, and technology, making it easier for them to adopt irrigation as an adaptation strategy to climate change. In line with [ 78 ], our findings confirmed that access to technologies and climate information is often skewed toward male-headed households. Similarly, [ 7 ] found that women, unlike men, face restrictions in accessing information, land, and other resources. To fully address the pressing challenge of climate change, dismantling the restrictive walls of traditional gender norms, and unlocking the immense potential of female farmers to lead the way in implementing successful adaptation strategies are critical. 3. Education level Education emerged as a significant factor influencing farmers' climate adaptation choices. Education plays a crucial role in shaping farmers' responses to climate change. It equips them with the knowledge and skills to adopt innovative adaptation strategies, fostering a shift from traditional practices. The study showed that the education level of household heads positively and significantly affects the choice of adjusting planting dates as an adaptation strategy compared to other adaptation strategies at a less than 5% significance level. This finding implies that for every one-year increase in education level, the probability of adopting adjusted planting dates increased by 5.3% relative to that of other adaptation strategies. Farmers with higher education levels tend to have a stronger understanding, more informed beliefs, and clearer interpretations of climate change. This, in turn, influences the actions they take in response to its impacts [ 79 ]. 4. Dependency Ratio Enhanced climate change resilience was observed in households with a greater number of active members [ 23 ]. The household dependency ratio negatively and significantly affects the choice of irrigation as an adaptation strategy compared to other adaptation strategies at a less than 10% significance level. This finding implies that for every one unit increase in a household's dependency ratio, the likelihood of choosing irrigation as an adaptation strategy decreased by 12% compared to that of other options. An increase in irrigation's labor needs becomes an advantage for households with a larger workforce, allowing them to maximize its benefits [ 80 ]. By investing this manpower, they can meticulously manage water application, potentially leading to significantly higher yields than traditional, less labor-intensive methods. This finding aligns with privous findings[ 66 ], reported that households with more working members were more likely to adopt irrigation adaptation strategies to address climate change impacts than others were. 5. Farm size Larger farm sizes emerged as a significant factor that positively influenced the choice of irrigation as an adaptation strategy[ 81 ]. The study showed that the household head's farm size positively and significantly affects the choice of irrigation as an adaptation strategy compared to other adaptation strategies at the 1% significance level. This finding implies that for every additional hectare of land, the likelihood of households adopting irrigation as an adaptation strategy increased by 49.9% compared to that of households adopting other strategies. Similarly, farm size positively and significantly affects the adoption of mixed crops with legume production at 10% significance levels. This suggests that larger farms may have resources or flexibility that facilitate incorporation of legumes into their cropping systems. The additional hectare of farmland increases the likelihood of a household adopting legume integration by 18.6%; compared to that of other adaptation strategies. This aligns with the existing related research, such as [ 20 , 66 ], demonstrated that farm size positively and siginificantly affects the adoption of both irrigation and mixed cropping with legumes as climate change adaptation strategies compared to other alternative strategies. 6. Farm experience One of the significant variables explaining why farmers choose certain adaptation strategies to address climate change is their accumulated farming experience. The study findings revealed that farm experience positively and significantly affects the adoption of irrigation as an adaptation to climate change compared to other strategies at the 1% significance level. This implies that each additional year of experience, farmers are 4.4% more likely to use irrigation in response to changing climate impacts than to other strategies. The results of this study are consistent with those of [ 66 , 82 ] in the context of Ethiopian agriculture, further confirming that farming experience plays a crucial role in promoting the adoption of irrigation and other climate change adaptation measures. 7. Livestock ownership Livestock production plays multifaceted roles in rural communities, contributing to both economic stability and climate change resilience [ 83 ]. It serves as a source of financial security, provides animal power for farm work, enriches the soil with nutrient-rich manure, and promotes essential moisture retention. This study revealed that livestock ownership positively and significantly affects the adoption of conservation tillage as an adaptation strategy to climate change at the 5% significance level. This implies that for each additional unit of livestock, measured in Tropical Livestock Units (TLUs), farmers are 16.7% more likely to implement soil-saving practices such as conservation tillage, translating to potentially significant improvements in soil health and resilience in the face of climate change compared to other strategies. Interviews with key informants also revealed that herd size positively influences adaptability. While larger herds can bring social status and economic benefits, their factual advantage in adapting to climate change lies in enabling the use of conservation tillage practices[ 75 ]. 8. Use of Credit Credit has becomes a powerful tool for farmers, enabling them to adopt resilient methods suh as mixed crop-legume production and conservation tillage [ 85 ]. It empowers them to overcome financial limitations and invest in adaptation strategies that will secure their future. The study showed that using credit positively and significantly affected legume intercroping, druoght tolerance crops and importance of conservation tillage at less than 5% siginificance level. This implies that a one-unit increase in credit use leads to a 33% increase in farmers adopting mixed crop-legume production, a vital strategy for adapting to climate change compared to others methods. Similarly, a unit increase in credit use leads to a 27% and 31% increase in the likelihood of adopting drought tolerance crop variety and implementing conservation tillage as an adaptation strategy, respectively, compared to other strategies. Financial limitations can be a hurdle for farmers adopting practices that address climate change challenges[ 41 ]. The study suggested that improved financial resources can help to overcome this barrier and encourage widespread adoption of climate resilience practices such as conservation tillage, drought tolerant crops and legume intercropping. This sustainable approach not only benefits them today but also fosters long-term profitability, environmental health, and strong communities in the face of climate challenges. Like [ 8 ], our study underscores the catalytic effect of credit access in equipping farmers with the necessary inputs to embrace climate-smart strategies. 9. Participation in Extension Services The positive coefficient of extension delivery highlights its influential role in promoting adaptation strategies. The study highlights how essential these services are for farmers to embrace climate risk-reducing practices. Participation in extension service programs led to increase in farmers adopting climate-smart practices, such as adopting drought resistance crop varieties [ 35 ]. These findings revealed a substantial positive and significant impact of extension programs on crops with druoght resistance at less than 5%. This finding implies that farmers enrolled in extension programs were morethan 31% more likely to adopt drought-resistant crops than were those enrolled in other adaptation strategies. However, the effect of participation in the extension program was not significant for adopting conservation tillage at the 1% significance level. This finding implies that, compared with other strategies, participation in extension programs is less likely to lead to conservation tillage adoption by 50%. These findings suggest that extension programs may currently prioritize promoting mechanization and fertilizer use over traditional conservation tillage practices. By bridging the information gap and offering ongoing support, robust extension services empower farmers to embrace climate-smart practices as demonstrated in previous studies [ 33 ]. Investing in this service empowers farmers to make informed decisions and build resilience in the face of climate change. 10. Information on climate change Information access on the potential impacts of climate change empowers farmers to become proactive agents of climate change [ 87 ]. It transforms weather pattern from passive observers to informed decision makers, strategically adapting their practices to ensure the long-term sustainability of their farms and communities. The findings of this study revealed that access to and use of climate information have positively and siginificantly affected the adoption of adjusting planting dates as an adaptation strategy at the 10% siginifice level. This means that farmers who have access to and use climate information were 23.9% more likely to modify their planting times in response to changing climate conditions than were those who use other strategies. Similarly, key informants were also cliamed that climate information empowers farmers to embrace a range of adaptation strategies, from adjusting planting dates to diversifying income. The authors also highlighted the importance of timely climate information delivery through media such as TV, radio, and early warning systems. This information is essential for implementing successful climate change adaptation strategies. These findings aligns with those of [ 3 ], who found that empowering farmers with climate information helps them navigate changing environments and adopt proactive solutions such as adjusting planting dates. 11. Market Access Market access motivates farmers to produce marketable crops, boosting their income and building their adaptability ta a changing climate [ 33 ]. By connecting to markets, communities gain access to resources and information, empowering them to develop and implement innovative climate change adaptation strategies. The study findings revealed that market access positively and significantly affected the adoption of legume intercropping at less than a 5% significance level; and that planting time, and conservation tillage had significant effects at a 10% significe level. This means that farmers closer to markets were more likely to adopt climate-resilient practices such as mixed cropping (26%), adjusted planting dates (24%), and conservation tillage (24.7%) than other adaptation strategies were. However, market access also had a significant negative impact on seasonal migration patterns at the 10% significance level. This suggests that easier access to markets may have reduced the need for people to migrate seasonally in search of work or income by 24% compared to other adaptation strategies (Table 4.10). As claimed by key informants, legumes such as beans, peas, and lentils offer two benefits. They improve soil health, leading to a more resilient farming system, and the additional income stream from legume sales fosters a more profitable system. This result is consistent with that of [ 11 ], who showed a positive influences of market access on mixed crops with legume production, conservation tillage, and adjusting planting dates as an adaptation strategy to climate change. 12. Membership in farmer-based organizations Farmer-based organizations play a crucial role in climate change adaptation. These groups not only provide valuable information on new practices; but also foster space for farmer-to-farmer learning[ 88 ]. The study findings revealed that farmer-based organizations negatively and significantly affected the adoption of irrigation as an adaptation strategy to climate change at less than a 5% significance level. This suggests that farmers involved in such organizations may seek alternative approaches to managing climate risks. Farmers who were members of farm group were 34.5% less likely to adopt irrigation as a way to adapt to climate change than others were, suggesting that irrigation infrastructures are not easily available in the study area. Farmer-to-farmer knowledge sharing promotes practical water conservation methods that do not require expensive irrigation systems. Moreover, focus group discussions revealed a paradoxical preference for irrigation as a climate adaptation in the study area, particularly for dega and woyna dega agro-ecologies with scarce water resources, poor irrigation infrastructure, and challenging topography. This suggests that alternative solutions, such as improved water management practices and context-specific infrastructure development, are crucial for addressing the water needs of these communities and supporting their adaptation efforts. While this study highlights the value of farmer-based organizations in knowledge sharing and adaptation, it is important to consider potential nuances. Aligned with these findings[ 80 ] farmers based on organizations may, in some cases, discourage the adoption of specific adaptation strategies, such as irrigation. 13. Agroecological settings Agroecological variations drive diverse adaptation strategies among households [ 65 ]. Faced with diverse climatic challenges, households across agroecology have crafted different ingenious adaptation tools [90]. Thus, this study investigated the influence of agroecological variations (Kola, Woyna Dega, and Dega) on the adoption of adaptation strategies to climate change challenges. This study revealed kola agroecology positively and significantly affected the adoption of two climate-resilient practices; crop production with legumes and conservation tillage; at less than 5% siginificance level. This finding implies that kola agroecology increased the likelihood of adopting crop production with legumes and conservation tillage as adaptation strategies by 34.6% and 32.6%, respectively, compared to other adaptation strategies. Focus group discussions in Kola agroecology further highlighted the importance of climate-smart practices such as conservation tillage and legume integration due to the challenges of moisture scarcity and infrequent rainfall. These practices can help retain moisture in the soil and improve soil fertility, making them wellsuited for addressing existing challenges. However, dega agroecology positively and significantly affected the adoption of adjusting planting dates and seasonal migration as adaptation strategies by 1% and less than 5%, respectively. This finding implies that, compared with farmers in other adaptation strategies, farmers in dega agroecology were 50.6% more likely to modify planting times and 29.9% more likely to engage in seasonal migration. Moreover, key informant interviews confirmed that dega agroecolgy enjoys relatively stable temperatures, abundant rainfall, and minimal moisture scarcity compared to other [ 65 ],agroecologies. Similarly, the results of this study revealed distinct climate adaptation strategies: dega and woyna dega farmers prioritized seasonal migration and adjusted planting dates, while kola farmers favor drought-resistant crops and adopted conservation tillage. Furthermore, woyna dega agroecology has a substantial positive and significant impact on seasonal migration at the 5% significance level. Taken together,thses findings suggest that,compared with farmers in other adaptation strategies, farmers in woyna dega agroecology increased their adoption of seasonal migration by 32.9%. As key informants, land scarcity and water shortages were found to be key drivers of Woyna dega farmers' preferences for seasonal migration as part of climate change adaptation strategy, suggesting complex interactions between resource limitations and livelihood choices. Consistent with [ 50 ], who found farmers in woyna-dega agroecology, where climate impacts are less severe, they adopted fewer adaptation strategies such as seasonal migration. 5. Conclusion and Policy Implications Climate change is no longer a distant threat; but rather a present truth demanding immediate action. Farming communities across the globe are joining hands, raising challenges with shared purposes.Through collaborative efforts and the adoption of smart adaptation strategies, they are protecting their food security and paving the way for a sustainable future. Under the pressure of a changing climate, farming communities in the study area are actively seeking ways to minimize the impact of climate change. To shed light on how farming communities in the study area fight against a changing climate, this study investigates their diverse adaptation strategies and major drivers, empowering communities to increase their challenges and secure their future. The MVP model served as a powerful tool for predicting how farmers might respond to changing climate conditions based on their specific circumstances. Considering household, institutional, and social factors; helps policymakers and development organizations tailor interventions to promote effective adaptation strategies. The results revealed that; among alternative adaptation strategies, sex, farm size, and farm experience were positively associated with the adoption of irrigation as an adaptation strategy to climate change in the study area. However, the age of the household head, dependency ratio, and farmer group membership all negative correlated with irrigation as an adaptation strategy. Farm size, credit access, market proximity, and kola agroecology all positively influence the adoption of legume crops as climate change adaptation strategies. Education, information access, market proximity, and Dega agroecology positively influence the adoption of adjusting planting dates as an adaptation strategy. Dega and Woyna Dega agro-ecologies positively influenced seasonal migration adoption as a climate adaptation strategy, while market access had a negative impact. Credit and extension access positively influenced the adoption of drought-resistant crops, while extension alone negatively impacted the adoption of conservation tillage for climate change adaptation. Livestock ownership, credit access, market proximity, and kola agroecology all positively influence the adoption of conservation tillage as a climate change adaptation strategy. This implies that the choices of climate change adaptation strategies are not independent; but rather affected by complex interactions among socioeconomic, institutional, and environmental factors. Empowering farm households with vital information, resources, and infrastructure unlocks their ability to implement successful adaptation measures. By partnering with government initiatives, farmers can take ownership of their adaptation strategies and work together to create a more sustainable future. Thus, governments and NGOs should collaborate with indigenous communities by providing financial support, technical training, and formal recognition of traditional wisdom to promote their adaptation strategies. NGOs promoting climate-smart agriculture may bridge the adaptation gap by offering specialized training and support in areas with low adoption rates, ensuring that all farmers have the tools they need to prosper in a changing climate. More experts and extension officers may also use the research findings to help farmers in their local working areas boost agricultural productivity by adapting to climate-friendly adaptation alternatives. Moreover, empowering rural communities requires looking beyond the farm. Building safe net of reliable income through nonfarm activities that thrive in diverse weather conditions strengthens resilience and ensures long-term economic stability. To combat the challenges of tomorrow, future policy must ignite a wave of understanding. Investing in knowledge-sharing platforms such as training, conferences, and seminars equips communities with the tools and skills needed to address climate change adversity. Declarations Conflicts of interest The authors declare that there are no conflicts of interest among the authors regarding the publication of this paper. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7076425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490243973,"identity":"74669e96-563a-4ae0-a051-b870bbded67c","order_by":0,"name":"Daniel Dalle","email":"","orcid":"","institution":"RDAE, Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Dalle","suffix":""},{"id":490243976,"identity":"542d0313-a8b5-40b7-8309-d7785fb98b2d","order_by":1,"name":"Yishak Gecho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACZgY2BoYDQIYEA/uHD0CajZ0ELWyMM0BamAnbg9DCzAMxBD8wOM7+7MGPM7Vy/LObjz22+bVNno+ZgfHDxxw8Wg7zmBv23DhuLHHnWLpxbt9twzZmBmbJmdvwamGT4PlwLHGDRI6BdG7PbUagFjZmXrxa2J9J/vlwrB6sxbLntj0RWhjMpHlu1CQYSOSYSTP8uJ1IUIvkYR4zaZkzBwxn3EhLNuxtuJ3cxszYjNcvfOePP5N8c6xOnn9G8sEHP/7ctp3f3nzww0c8WhQOgKnDEB5jG5hswK0eCOQh0nVQ7h+8ikfBKBgFo2CEAgDQAFPOraPFrwAAAABJRU5ErkJggg==","orcid":"","institution":"RDAE, Wolaita Sodo University","correspondingAuthor":true,"prefix":"","firstName":"Yishak","middleName":"","lastName":"Gecho","suffix":""},{"id":490243977,"identity":"8d0250c5-7b5f-4753-b864-fe2d9aad2db0","order_by":2,"name":"Sisay Belay Bedeke","email":"","orcid":"","institution":"RDAE, Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Sisay","middleName":"Belay","lastName":"Bedeke","suffix":""}],"badges":[],"createdAt":"2025-07-08 15:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7076425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7076425/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-02450-9","type":"published","date":"2025-12-31T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87562393,"identity":"36d324cf-3665-4679-8685-33297e706461","added_by":"auto","created_at":"2025-07-25 08:49:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209915,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7076425/v1/80ec3379f2dafb4c916a2ff1.png"},{"id":87562391,"identity":"4f9ea25e-629c-40ac-a477-d132112e8482","added_by":"auto","created_at":"2025-07-25 08:49:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57428,"visible":true,"origin":"","legend":"\u003cp\u003eConstraints to climate change adaptation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7076425/v1/1d9016bbf46549a183aa27d3.png"},{"id":99545549,"identity":"493254da-5721-4243-a6b7-f242bdbac5ff","added_by":"auto","created_at":"2026-01-05 16:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1933558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7076425/v1/840cf14a-ff52-4b0b-aadb-be75168ea2c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Affecting Climate Change Adaptation Strategies and Existing Barriers among Stallholder Farmers in Southern Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global climate has significantly changed over recent decades and continues to change at an unprecedented rate; climate change is firmly recognized as the most critical environmental concern confronting today's world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There is mounting evidence that catastrophic climate change events are common and have adversely impacted smallholder farmers in developing nations who rely primarily on rain-fed farming. As a developing country, Ethiopia's agriculture, is predominantly rainfed, generates approximatly 45% of coutry\u0026rsquo;s GDP and 90% of its exports, and employs 85% of its population; Ethiopia has been seriously distressed by climate change[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Smallholder farmers with limited resources confront unique challenges in addressing these constraints.\u003c/p\u003e\u003cp\u003eClimate change affects almost all societies and their activities in one or other ways, so societies must address and respond to its unanticipated deviations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Farmholds in Ethiopia have already begun to adapt interventions to the negative consequences of climate change, but these efforts are still at a relatively early stage. It is more reasonable to state that the attempts were fragmentary and restricted[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Much of the real effort to adapt to climate change occurs through inappropriate approaches in the context of unfitting practices, inadequate institutional frameworks, and implementation strategies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moving beyond a general understanding, this study delves into the unique challenges faced by smallholder farmers in adapting to climate change. This deep context-specific analysis identifies key obstacles and translates them into actionable knowledge to empower them in the face of these challenges.\u003c/p\u003e\u003cp\u003eDue to the uneven distribution of climate change impacts across geographical regions, response mechanisms must vary based on the specific types and extent of local climatic changes [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Adapting to the site-specific effects of climate change necessitates in-depth knowledge of local conditions, requiring a clear understanding of the situation at the site or household level [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Local-level evidence across agro-ecologies is crucial for refining interventions and ensuring effective and efficient adaptation options to address the adverse effects of climate change.\u003c/p\u003e\u003cp\u003eExisting research in the study area, including [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], has explored farm household adaptation strategies in response to climate change on low land areas. Similarly, [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], examined the adaptation strategies employed by smallholder farmers in the Hobicha (lowland) district. In contrast, [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; investigated the adoption of climate change adaptation strategies among maize-dependent smallholders in southern Ethiopia, offering valuable insights into the specific context and challenges faced by this population. However, these studies often lack a comparative analysis across multiple dimensions. Thus, this study examined the interplay between various adaptation options, influencing factors, and geographical contexts, leading to a richer understanding of climate resilience in agricultural communities.\u003c/p\u003e\u003cp\u003eSignificant shifts in climatic patterns can profoundly impact natural processes within watershed ecosystems, reshaping the spatial distribution and flow of water across landscapes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The poorest and most vulnerable communities are often the most severly affected by climate change. They often live in areas that are already prone to drought or flooding, and they may lack the resources to adapt to changing conditions, such as the area where this study was conducted [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, water-related climate change adaptation has a pivotal role in achieving sustainable development [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research in the Wolaita Zone has made valuable contributions to understanding climate change adaptation, but its focus has typically been narrow; and often confined to specific areas, such as highlands[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], lowlands[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], or livelihoods [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, the crucial role of effective water management across different watersheds within the zone has largely not been explored. This study delves into these understudied areas, revealing their unique challenges and opportunities for building climate resilience through effective water management practices.\u003c/p\u003e\u003cp\u003eRecognizing the complexity of climate change, farmers strategically combine diverse strategies, leveraging complementary benefits and exploring alternatives to navigate its challenges [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The selection of subsequent adaptation strategies by farmers may be partially influenced by the knowledge and experience gained from previously implemented strategies, creating a path-dependent decision-making process[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have offered valuable insights into individual adaptation strategies and their influencing factors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, a critical gap remains: understanding how these strategies interact with and influence each other's effectiveness. This study delves deeper by examining the interconnectedness of various adaptation options, providing a more holistic perspective.\u003c/p\u003e\u003cp\u003eMany studies across the nation have explored various measures for adapting to climate change and the factors that impact their adoption[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, these studies often paint a one-sided picture, focusing solely on either the positive or negative effects of factors influencing adaptation choices. Going beyond existing research[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], this study uniquely fills a critical gap by identifying area-specific factors in addition to constraints that impede the adoption of effective adaptation techniques in Ethiopia and this specific study area. This information can inform targeted interventions and policies to boost adaptation efforts.\u003c/p\u003e\u003cp\u003eLikewise, other studies have been conducted to assess the influence of climate change on Ethiopian agriculture and water resources[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, these studies examined the monetary or yield impacts of climate change and acclaimed adaptation techniques but did not identify the factors influencing the selection of the suggested adaptation methods. As a result, adaptation techniques to climate change used by smallholder farmers and their determinant factors have not been effectively recognized and documented. To bridge the knowledge gap in this area, this study embarked on an investigation into the primary factors influencing smallholder farmers' selection of adaptation methods to climate change, along with the barriers hindering their implementation.\u003c/p\u003e\u003cp\u003eFurthermore, there is a growing recognition that enhancing farmers' ability to confront and cope with the risks and challenges of a changing climate cannot be achieved merely by sound technology protocols or local practices[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The merging of reliable technical solutions and local practices is increasingly recognized as essential for progress. Current research offers valuable insights into potential adaptation approaches, but a significant knowledge gap exists concerning how farmers translate these approaches into tangible changes in their day-to-day operations as the climate rapidly shifts. Thus, our study aims to equip policymakers with actionable knowledge by identifying the primary factors influencing farm households' adaptation choices, thereby supporting the development of successful agricultural adaptation measures.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area Description\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Geographic Location\u003c/h2\u003e\u003cp\u003eThe Wolaita zone, one of the zones in the southe region, is located 390 kilometers southwest of Addis Ababa, Ethiopia, along the bustling main road connecting Shashamane and Arba Minch [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Astronomically, the Wolaita Zone is situated between 6.40\u0026deg; and 7.10\u0026deg; north latitude and 37.40\u0026deg; and 38.20\u0026deg; east longitude, placing it at the heart of Ethiopia's diverse landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The zone's diverse topography, ranging from highlands to lowlands, has fostered a rich cultural tapestry and a variety of agricultural techniques tailored to the specific microclimates of each location [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Demographic characteristics\u003c/h2\u003e\u003cp\u003eAccording to [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the total size of the population in the zone is estimated to be 2,326,307, while the total area of the zone is 451,170 hectares or 4511.7 km2. Of the total population, 1,013,516 (44%) were male and 1,312,791 (56%) were female. Concerning the distribution of the population, the vast majority of the population in the zone (78%) or 1,817,429 resided in rural areas and the remaining 426,650 (22%) were urban dwellers (Table\u0026nbsp;6). Of the total households, the average family size was 4.84 persons per a household. The data confirmed that the demographic structure of the zone is dominated by young people, with a high population growth rate (2.9% annually).\u003c/p\u003e\u003cp\u003eRegarding population density, the Wolaita zone ranks among Ethiopia's most densely populated areas [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While the Zone has a significant population overall, the density varies considerably across its districts and agro-ecological zones. Among the study districts within the Wolaita Zone, Damote Gale has the highest population density, reaching 706 inhabitants per square kilometer. Damote Woyde is closly behind, with a density of 606 inhabitants per square kilometer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition to the overall dependency ratio of 92% for the study area, there are notable differences between districts. Deguna Fango exhibited the highest dependency ratio at 116%, indicating a larger nonworking-age population relative to the working-age population. This information can be crucial for strategizing resource allocation and developing targeted programs to support different communities within the study area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.1.3 Climate and Agro ecologies of the Study Area\u003c/h2\u003e\u003cp\u003eAcording to the agroecological zone classification of Ethiopia, the study area is predominantly characterized by mid to high elevation regions (1500\u0026ndash;2300 m.a.s.l.) agroecology (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the study area is generally classified into three agroecological zones; among them, Waina-Dega (midland) comprises approximatly 56% of the total area; the remaing 35% and 9% are described as Kola (lowland); and Dega (highland) respectively[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Each district within the zone has distinct challenges and opportunities, requiring tailored approaches to land management and sustainable development.\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\u003eAgroecology zone classification in the study area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAEZs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlt. (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRF (mm/yr.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM.A.To (oC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKola (lowlands)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWarm semiarid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500\u0026ndash;1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e200\u0026ndash;800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.5\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoynadega (midlands)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCool sub- humid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1500\u0026ndash;2300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e800\u0026ndash;1200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.5/17.5\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDega (highlands)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCool \u0026amp; humid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2300\u0026ndash;3200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1200\u0026ndash;2200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.5\u0026ndash;16/17.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe climatic characteristics of agroecological zones (AEZs) include geographical areas with similar climatic characteristics that shape their ability to support rainfed agriculture [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The rainfall in the area is inconsistent and unpredictable, and occurring in two distinct seasons. The first rainy season (Belg) lasts from March to May, while the second (Kremt) lasts from July to October, peaking between mid-June and August. Despite substantial differences in rainfall over the years, the area is primarily reliant on Belg rains, which last from the end of February until early April [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe overall mean annual rainfall in the zone ranges from 1000 mm to 1270 mm, with the maximum rainfall recorded in August [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The year with late and below normal Belg rains resulted in very poor prospects for June and July crops. Light showery rainfalls in November and December; this rain is vital for the growth of root crops such as cassava, sweet potato, and Irish potato in the zone, is currently absent [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This led to low production of these commonly utilized root crops in many parts of the zone; and which are generally important means of filling food shortfall. Furthermore, erratic rainfall has recently been a serious hindrance to farming productivity, particularly in the lowlands of the Wolaita zone.\u003c/p\u003e\u003cp\u003eThe temperature trends in the study area are generally high, with minimal fluctuations across seasons. The zone's average yearly maximum and minimum temperatures vary from 15.20\u0026deg;C to 31.40\u0026deg;C [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, an increase in temperature determines the rate of evaporation, soil moisture content, and atmospheric humidity. High and unpredictable temperature patterns cause epidemics of human, animal, and crop diseases, losses in crop output and productivity; and unemployment, resulting in temporal migration in the area. Therefore, the negative effects of climate change have compelled them to implement adaptation strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.1.4 Livelihoods and adaptation to climate change\u003c/h2\u003e\u003cp\u003eClimate change poses a significant risk to the livelihoods and well-being of communities across the globe, and the Wolaita Zone in Ethiopia is no exception. Subsistence farmers, who rely on rain-fed agriculture for a living, are at the forefront of the climate crisis. They face a complex and interconnected set of challenges that threaten their food security and entire way of life. Supporting subsistence farmers through targeted adaptation efforts is not simply an act of charity; it is a strategic investment in a more resilient and food-secure future. By empowering these farmers, we unlock a cascade of positive outcomes, paving the way for a brighter tomorrow.\u003c/p\u003e\u003cp\u003eAgriculture is the primary source of living in the study area, with crop production and livestock rearing taking precedence. The Wolaita zone was sub-divided into two livelihood zones: maize and root crops and ginger and coffee [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The majority of the zone's midland and dry midland terrains are covered by maize and root crops, while root crops and perennials are commonly grown in the highland [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While livestock enjoy the freedom of open grazing, the convenience of stall feeding, and tethered plots near homes, these practices might quietly change the local climate. Overgrazing, mismanagement of farmland, and the weight of an expanding population, aggravated by traditional livestock practices, contribute to a worrying shift in the climate, raising fears about the area's future.\u003c/p\u003e\u003cp\u003eTo respond to the problems caused by climate change and variability, farmers in the Wolaita Zone have developed a diverse arsenal of adaptation strategies[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These strategies encompass various approaches to land management, income generation, and resource utilization, allowing farmers to navigate a changing environment and ensure their long-term sustainability. This study has the potential to inform policymakers, development practitioners, and agricultural extension services to tailor their support and interventions to the specific needs of farmers in the country in general and the study area in particular. By bridging the gap between traditional knowledge and modern technology, this study can also play a crucial role in building a more resilient and sustainable agricultural sector in the face of climate change. Therefore, this study aimed to examine the alternative adaptation strategies they have practiced, the determinants of the choice of adaptation options, and the constraints of applying adaptation options to climate change adversity.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Approaches and Design\u003c/h2\u003e\u003cp\u003eThe choice of research design is determined by the study's objectives and related research questions, not the researcher's preferences. A survey or cross-sectional study design was used to gather relevant data and provide useful information for this investigation. The cross-sectional research design requires data collection on multiple cases at the same time to compile a set of quantitative or qualitative data involving two or more variables, which is subsqently investigated to discover patterns or links [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In this regard, many of the household factors were used to analyse the association with alternative adaptation approaches and identify the limits.\u003c/p\u003e\u003cp\u003eThis research integrated qualitative and quantitative data within a mixed methods framework, grounded in specific philosophical principles [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A mixed research approach provides a greater grasp of a study problem than does using a quantitative or qualitative approach alone [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The advantage of utilizing a mixed research technique was that the strengths of one approach compensated for the weaknesses of the other, and a broad base of information was required to address the stated objectives. While the quantitative approach held greater weight due to the study's focus, both the quantitative and qualitative analyses enriched the research, offering complementary insights. The qualitative data were mainly used to supplement the quantitative findings and reach valid conclusions. The study opted for a pragmatic philosophical foundation, aligning with its mixed research approach and promoting the integration of different research methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Site Selection, Sampling Methods, and Sample Size Determination\u003c/h2\u003e\u003cp\u003eThe success of any research project hinges on two crucial decisions: selecting the appropriate research area and determining the optimal sample size [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Both decisions go hand-in-hand, ensuring that a study yields reliable and meaningful results. Both probability and nonprobability sampling procedures were employed to select the sample households. To ensure that the study was representative of the entire zone, multistage sampling procedures were utilized to choose the districts /AEZs/ within the zone, the Kebeles in the chosen district, and farm households in each Kebeles for data collection.\u003c/p\u003e\u003cp\u003eIn the first stage of multistage sampling, the research district/AEZs/in the Bilate Wolaita subwatershed within the Wolaita zone was purposefully chosen. The Bilate Wolaita subwatershed was then subdivided into three AEZs to represent three AEZs in the study area: Dega (Damote Gale), Woyna Dega (Damote Woyde), and Kola (Duguna Fango). When selecting the study district /AEZs/, two main reasons were considered: The first reason was that the Bilate Wolaita subwatershed is high vulnerability to climate-related threats, including drought and flooding, as well as because of its location as a hotspot area in the zone. The second was the agro-ecological location, which included Dega, Woyna-Dega, and Kola, following Ethiopia's traditional AEZ classification. In this situation, the dominating agroecology of the district was used as a criterion.\u003c/p\u003e\u003cp\u003eThe second stage was the choice of study Kebeles, based on the characteristics of each AEZ of the Kebeles, which was used to stratify them within their respective AEZs (Dega, Woyna Dega, and Kola). To ensure representation across the different agroecological zones, three Kebeles were randomly chosen from each of the Dega, Woyna Dega, and Kola AEZs. This simple random sampling technique yielded a total of nine Kebeles for the study.\u003c/p\u003e\u003cp\u003eStudy participants were then recruited at the final stage of the multistage sampling process. Participants were selected by using systematic sampling from the ordered list of eligible individuals within each chosen kebele. To account for the varying sizes of Kebeles within each AEZ, the research employed probability proportional to size (PPS) sampling to select the respondents. The process of selecting households follows four key steps: First, a comprehensive list of all households in each AEZ is acquired from Kebele administrators. Second, the sample size (n) was calculated based on the total population (N) in each AEZ. Third, every household on the list is assigned a unique sequence number. Finally, systematic sampling with a fixed interval is used to select the final sample of households [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To ensure a representative sample for this heterogeneous and sizable population, we employed the (Kothari, 2004) sampling size rule, detailed in Equations 1 and 2.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{N}=1+\\frac{\\varvec{N}\\varvec{*}\\varvec{p}\\varvec{*}\\varvec{q}\\varvec{*}{\\varvec{Z}}^{2}}{{\\varvec{e}}^{2}\\left(\\varvec{N}-1\\right)+{\\varvec{Z}}^{2}\\varvec{*}\\varvec{p}\\varvec{*}\\varvec{q}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{n}=1+\\frac{9546\\text{*}0.5\\text{*}0.5\\text{*}{1.96}^{2}}{{(0.05)}^{2}\\left(9546-1\\right)+{1.96}^{2}\\text{*}0.5\\text{*}0.5}=371\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere:\u003c/p\u003e\u003cp\u003en: Sample size\u003c/p\u003e\u003cp\u003eZ: Confidence level z score (95% = 1.96)\u003c/p\u003e\u003cp\u003ep: Assumed population proportion (50%=0.5)\u003c/p\u003e\u003cp\u003eq: Nonoccurrence probability (1 - p)\u003c/p\u003e\u003cp\u003eN: Population size\u003c/p\u003e\u003cp\u003ee: Margin of error (0.05)\u003c/p\u003e\u003cp\u003eA total of 371 households were randomly selected for the study. This sample represents 38% of Dega (D/Gale District), 36% of Woyna Dega (D/Woyde District), and 26% of Kola (D/Fango District).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data Required and Ways of Collection\u003c/h2\u003e\u003cp\u003eHaving defined the target population, established the sampling frame, chosen a sampling process, and determined the size, the next critical step was to embark on the data collection. This phase involved gathering data from both primary and secondary sources to ensure a robust and multifaceted understanding of the research topic. The primary data came from a cross-sectional survey of 371 households drawn from the three study districts. Primary data collection relied on a strategic mix of instruments: standardized questionnaires administered to households, in-depth interviews with knowledgeable individuals, facilitated group discussions with community members, and direct observation of everyday practices and contexts. Primary data formed the core of the study, encompassing key aspects such as demographics, socioeconomic indicators, institutional structures, and environmental factors. To enrich the analysis, these primary data were complemented by relevant secondary data from reliable sources.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Model specification, variable description and data analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Model specifications\u003c/h2\u003e\u003cp\u003eFor decision-making scenarios with more than two possible choices, the suitable regression models were multinomial logit (MNL), multinomial probit (MNP), or multivariate probit (MVP) models, depending on the specific characteristics of the data and the research question[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The multinomial logit (MNL) model exhibits robustness and computational efficiency but operates under the assumptions of independence between choice outcomes and mutual exclusivity of choice variables[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Consequently, the MNL model might not be suitable for scenarios where choices are potentially nonindependent or overlapping.\u003c/p\u003e\u003cp\u003eAlthough the multinomial probit (MNP) model offers flexibility by relaxing the independence of irrelevant alternatives (IIA) assumption, accurately accounting for the simultaneous effects of explanatory variables on each possible outcome variable is challenging[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This limitation proved problematic because farmers' local adaptation options often displayed complex interdependencies, acting as either substitutes or complements for one another.\u003c/p\u003e\u003cp\u003eThe MVP model had an advantage over the MNL model in that it relaxed the IIA assumption, which is in many situations ideal[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. To overcome limitations with independence assumptions, the study opted for a multivariate probit (MVP) model, efficiently analyzing interlinked choice effects and accounting for shared influences. The descriptive measures complemented the multivariate probit model, offering valuable insights into the data's characteristics and patterns using statistical software (STATA version 16).\u003c/p\u003e\u003cp\u003eThe MVP model stands out as a valuable tool for analysing farmers' complex adaptation choices, as it enables researchers to assess the interconnectedness of different adaptation measures and identify the factors driving their adoption[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. To formulate a multivariate probit model, six dummy dependent variables were defined: change planning date, practice of small-scale irrigation; application of conservation tillage; diversification to nonfarm income sources (seasonal migration); mixed crop and legume production; and switching to drought resistanant crop varieties.\u003c/p\u003e\u003cp\u003eThe MVP model assumes that each subject has J distinct binary responses and unobserved latent variable Z. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i=1...n\\)\u003c/span\u003e\u003c/span\u003e be the independent observations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j=1...j\\)\u003c/span\u003e\u003c/span\u003e be the available options for binary responses, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e be a matrix of covariates composed of any discrete or continuous variables. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{\\varvec{i}\\varvec{j}}={(Y}_{I1\\dots\\:}{Y}_{IJ})\\)\u003c/span\u003e\u003c/span\u003e denote the J-dimensional vector of observed binary responses taking values of (0, 1) on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{i}^{th}\\)\u003c/span\u003e\u003c/span\u003e household and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{ij}={(Z}_{I1},{\\dots\\:,Z}_{IJ})\\)\u003c/span\u003e\u003c/span\u003e denoted a J-variate normal vector of latent variables computed in Eq.\u0026nbsp;3:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\:Zij=xi\\beta\\:+\\epsilon\\:i,\\:i,\\dots\\:,n\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\beta\\:=\\left(\\overrightarrow{\\beta\\:}1,\\dots\\:,\\overrightarrow{\\beta\\:}j\\right)\\)\u003c/span\u003e\u003c/span\u003e is a matrix of unknown regression coefficients; εi is a vector of residual errors distributed as a multivariate normal distribution with zero means and univariance;and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:i\\:\\\\\u0026sim;N(0,\\:\\:\\epsilon\\:)\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e is the variance covariance matrix.The off-diagonal elements in the correlation matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{k}\\text{j}}={\\text{P}}_{\\text{j}\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e represent the unobserved correlation between the stochastic components of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}^{th}\\:and\\:{J}^{th}\\)\u003c/span\u003e\u003c/span\u003e options [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The relationship between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Z}}_{\\text{i}\\text{j}}{\\:\\text{a}\\text{n}\\text{d}\\:\\text{Y}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e was computed via Eq.\u0026nbsp;4:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{y}_{ij}=\\left\\{1if{z}_{ij}\u0026gt;0;\\:0\\:otherwise\\right\\}\\:i=1,\\dots\\:,n\\:and\\:j=1,\\dots\\:,j\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThen, the likelihood of the observed discrete variable was obtained by integrating the latent variables as indicated in Eq.\u0026nbsp;5.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAc8AAAAXCAYAAABtYsTdAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAApJSURBVHhe7ZzLq47fF8C331yuI0nCRIhcUkhRLmFgoCiSUq5loFwiI3cxUa4ZKbnHhFyKgUvukcQAAxm6+wPO9/3ssxfLtp/rOc457/mtTz29z2XvZ++91nrW3nvt53l7tDRwhmEYhmGU5n/h1/jHzJ49O+wZRnXMfgyja2Gdp2EYhmFUxDpPwzAMw6iIdZ6GYRiGURHrPA3DMAyjItZ5GoZhGEZFrPM0arFo0SLXo0cPd+zYsXDGMAzj/wfrPI3K0GGOHTvWXblyxX3//j2cNQzDaD5evHjh9u3bF47KU6vzPHPmjJ916K1v377u/fv3IUWau3fv/pVn69at4epvaMyaNWv8r87DMfDNG8ed/e3b58+fvdBpR2eBTIYNG+blwW+eDkRuWRto2Wdx4sQJN2nSJDdnzhy3cePGcDYbZql1jFMjdaxzH/SEnWG3gIx0u2W7evWqv95RUA9k3dF2jBywWdpcVHYsI71JXuRGO5BzHuiOfG21BcOoirbRuB8aM2aMGzlypE+HbZa2T/5hqCpbtmxpOX36dDhqaZk1a1ZLYxYSjvIh7dGjR/3+8+fP+Xcj/yuwT5pPnz6FM615KFO4c+dOy9ChQ/9I09FQT+pEW8qIkTa0N7S/T58+v3SxcOHClr179/r9FOiIusa6itvAfamv1osm1lke6Ip7tbX9tKuOzlNtof2rV68OR6331vbVEVAHyqUeZWTTVvkJ6AObQR7IBplyLgvkQvp3796FM61ga7pO3I/jIv2QJq88w2hv8I/6eReflEWcPotanaemquPhYdWODEesHyauxw8qzl03lv2yzvtfQ907q/NELlrJ7MvAJAucHs4wdnKc02QZGLop016BOhUZaxZ0MOSjPL3RhrL6p3w90IuhbuPHjw9HHQ/PTxnZtJf9IDstj/h5jJEONi5fOksNbWHLg3sZRkeBv4ptrow/4jrp8mhT55nleCg49RDphvBQ4th0w7Lupx02nUPRA5pF7IT1ViTMLKgz+YuI74/zocOSgUcsizJwTzoYZIlckF3RyB9Z6nLzoD6xYy3bXsBJk56tqnzJS3tklkx+ztE+OgA2qUu86bI4zpIJ52kjMqlLVh1kK7JVrpeRTZwGWXB/0T+ywoaKIA/paTPpy+ShDCkrD7EtDfaDjMmPzel2SBtkI29bdGEYMTxfsa8T38smdhmDbeJj8qj9whCx42XLlrmzZ8+GM8U8evTINR4OH2fu37+/+/Lli7t48WK46tz9+/fdzJkzw9FvhgwZ4hoPoI9FX7p0qdQ6W4pGezO3a9euhVQdw4MHD9zt27fdkSNH/Hrc1KlT3du3b8PVcly/ft2vOyJL7kUb+vXrF66mQZa7d+92u3bt8rH/PBoO2dezDtgHLxNNmTIlnKnG5cuXvc4nTpz4q56snT58+NDb0Pz58/25hjP2+uO38aC4Rsfgevfu7a+RjzZkyWTx4sXu4MGDXiZ1oX2xLemtrq0WgWwbAyZ38uRJ3w7WoQ8fPhyupkEeyOn169detoMHDy7MA9hYw5G4JUuW5K5rihxlvZw13WnTprkNGzZ4WcD06dP9L3z48ME1nJS/1nBg3obbogvDiLl165abPHlyOGpl9OjRvu9hw5fQh8VfDQwfPtzduHEjHGXQMNxaMIIsGonGMMrNG4lzLes6eRkp6FkEaRkhCDIC70jKzsT0iFvDqDwOtZZph57NkZ5RUhV9kJey80jpo2x7yUc6vWli3cUwOqRN8T2odyof51N1zZI7abNm3+TRMyDkmpW2rVCPrDpqUmmoIzJJRQdSM0rKEhmRFxvT7cwDG+P5K5qpUk/KB2Sm07OfslHsP25DbJv/UgdG90XbYxZZzyDPVh61Zp7MAMeNG+dHpFD2bUF6ct7SrMvmzZv/mEUwqmc2InDt8ePH4ehv9BtW8dbRbzxC1ii+qB3w6tWrX6N40vPpCLOQshA1aDjDcNS+MON49uwZlue3hvGGK7/RumNGhPz1TJjRIW+Fkr9h2H5WyT6za63zOlDO+fPn3c6dO/0xb+Hpt5QpQ8+AsHNJG8O9UvYk2798s/Tnz59h70+YDadmlOhEnj/ah93cvHnTHxdBWuRexWaePn3q5s2b5/exdfRJJEHDuREjRnh9a+IoTJ4ODKMzqNx5xo4n9ZE8jjB2GoRymCIPGDAgnEmT+m5QHjzd8XJ/nJOGY9JlIc48tdUN2/748SPsVYM2rV271q1YscKHopGrhLvidsQdCxDiEsi3Z8+eX6FMIH3WgICyDxw44EN9eeBse/XqFY7Ks3//frd06dJw9LeMUrrLgroSnq77PSn5NRzr5QZkR+hcOktkpuUmnaPuXDVtDdvWbRf15tkjnIp85DmU+sb2AjoMhX2Rb8aMGeFM/mv6lMeAbv369eFMGpZmevbsGY5adY/sLly44L5+/eo+fvz465MgsXFkSP2lDdRf16NIB4aRR8r/yCeS2NTx48e9T9Bg74UDxcYDXhoJ3ZBNbxIKEpgCx+cIEZE2NT0WmF6nwpWEcFJ542PCOkVT9PaE8kUe1DEvrKTrSpiK9ISqCJtxDx3eitshaTVSHmWTPy47b8GbsuJQcYpUuVk6EiiXOun7cxyfS+kypTvsSPJnIWUih7i+nMduBcqR+8mm60IdUrbb3lBPXRfKyAtj6zqiV45pF+FM8uu8Oq0gYVopE93G5WXZBeWQN5ZtjNiyIHqRumo7l3rrTfSP/GNb+Bc6MLo/2FLsG7E97B+bwybj5x2w3Sz/KVTqPDuClMNOQZq40Smn0VWoUjedFicSOzlxhHlgMLrTEDCclFGIgxMoN1VnnKvu6OuQpbvYYbYHZQcKAmnjelTR3b+iSh1S+sWGYieiQfdZ1zmfkmFsg+g05YiqkmWfhlEVfCV9SsoX5lHGH3W5v+fj7dtNmzb9FW6L4S1Q/hVCh6eK8nQmVcLCuh2EtOI1vjdv3vi3JfMgrM46lYYQxbp169yhQ4fCmVYIURDCk/SUv2PHDr/OKBDKJB3hYlnHqkuW7uqGwPPYvn27D4tT9zLQPpYWJD11ZH2/sylrP9Qb2xDZSgj65cuXbtCgQX4/BbpPrSkSYmXtcuXKleFMK4RYtX1RLm82Ll++PJypz7dv3/z95DnoKjowmg+WY/Bl27ZtC2eKYTmBfPjePLpc58mLAzhtGlu0xsGr87K+wgM2YcIEv9/MlGnHvXv3/AtCVWmM6P26M5+2sIYkG39PJeC0kD060C9x8PkH6XDM8qJYW9C6ow5Pnjxxc+fOzVxzqwsO/tSpU+7cuXOl/34POQ0cONDv8/nUqFGj/H6zwGdIrC1qWO/kxZwq8PyhJwZW2l7YVq1aFVK1drB0psg5HrDVgXVT3Tk3ow6MrgOTDwb8vBhYBP6HT6jKfMLVg+ln2G9qEBDflMUj5Gaju7SjO8DMh7dDiYbEb4M2AwyEeCO3aATdlWl2HRjdly4386wDMzVCPQsWLAhnmpPu0o7uAE6bGTqz0GZ12tS72TvOZteB0V1x7j/bykUabFnetAAAAABJRU5ErkJggg==\" width=\"463\" height=\"23\"\u003e\u003c/p\u003e\u003cp\u003ewhere, Ai1 is the interval (0, ꝏ) if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}=1\\)\u003c/span\u003e\u003c/span\u003e and the interval (-ꝏ, 1) otherwise and Ai1\u0026#120625;т \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(Z{Y}_{ij}=1\\)\u003c/span\u003e\u003c/span\u003e⃒Xi, β, \u0026sum;) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{dz}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the probability density function of the standard normal distribution. To interpret the effect of the explanatory variables on probability, the marginal effect was generally inferred as computed in Eq.\u0026nbsp;6:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{\\delta\\:}_{ij}=\\frac{{\\partial\\:p}_{ij}}{{\\partial\\:x}_{i}}={p}_{ji}\\left[{\\beta\\:}_{j}-{\\sum\\:}_{k=0}^{j}{p}_{ik\\:}{\\beta\\:}_{k}\\right]={p}_{ij}\\left[{\\beta\\:}_{j}-{\\beta\\:}^{\\to\\:}\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e denotes the marginal effect, of the explanatory variable on the probability that alternative j was chosen. The marginal effect is a key measure in this analysis, quantifying the expected change in the probability of making a particular choice for every one-unit increase in a specific explanatory variable (Mihiretu et al., 2019). This allows us to understand how much, on average, each factor influences the likelihood of selecting different options.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Basic definitions of the dependent and explanatory variables\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDependent variables\u003c/strong\u003e\u003cp\u003eDependent variables are the measured behaviors of households on adaptation strategies. In this study, six dependent variables mixed crops with legumes, adjusted planting dates, engaging in seasonal migration, shifting to drought tolerant crops, utilizing conservation tillage, and small-scale irrigation were selected based on their importance and prioritized by the farming communities from the existing strategies for further analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCrop with legume production\u003c/strong\u003e\u003cp\u003eCrop with legume production refers to the practice of integrating legumes, such as common beans, peas, chickpeas, cowpeas, lentils, pigeon peas, peanuts, and grass peas, into a cropping system [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This can be achieved through either rotation or intercropping with other perennial crops, forage grasses, or vegetable crops. Owing to their diverse benefits, legumes are valuable for both humans and animals as food, wood, and soil enhancers within agricultural and agroforestry systems. These benefits, particularly their ability to thrive in challenging conditions, make them a suitable adaptation option and thus, one of the dependent variables analysed in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdjusting planting dates\u003c/strong\u003e\u003cp\u003eAdjusting planting and harvest times is a key strategy for farmers adapting to a changing climate. Studies suggest that strategically modifying growing seasons could significantly reduce yield losses in the face of future climate scenarios [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Due to its widespread adoption as an adaptation strategy in the study area, adjusting the planting date was included as one of the dependent variables analysed in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSeasonal migration\u003c/strong\u003e\u003cp\u003eSeasonal migration helps households build resilience before escalating climate impacts deplete resources, making local adaptation impossible and large-scale migration difficult [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Economic advantages often drive migration, allowing individuals to diversify their income sources and reduce risk by spreading their financial dependence across multiple locations. Given its widespread adoption as a climate change adaptation strategy, seasonal migration was chosen as the dependent variable for this analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDrought-tolerant crops\u003c/strong\u003e\u003cp\u003eDrought, a constant environmental threat, significantly reduces plant growth, biomass, quality, and energy production. This stress, caused by temperature fluctuations, light intensity, and low rainfall, subtly disrupts plant morphology, physiology, biochemistry, and ultimately, photosynthesis. Plants have evolved remarkable resilience, utilizing both resistance and adaptation strategies to persist in harsh conditions. Drought-tolerant crops, also known as water-efficient varieties, thrive with minimal water, even during periods of scarcity or limited irrigation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Due to its prevalence as an adaptation strategy, the adoption of drought-tolerant crop varieties served as the dependent variable in this analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConservation tillage\u003c/strong\u003e\u003cp\u003eConservation tillage practices involve leaving crop residues such as corn stalks, stems, leaves, and legume seed pods on the field after harvest[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This study considered various practices under the umbrella of conservation tillage, including utilizing crop residue, grasses, and animal manure as soil mulch. These practices aim to minimize soil disturbance and maximize soil cover, promoting soil health and resilience. Due to its cost-effectiveness and minimal technological requirements, conservation tillage is the most widely adopted adaptation strategy, making it a suitable dependent variable for analysing household responses to climate change.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIrrigation\u003c/strong\u003e\u003cp\u003eIn the face of climate change, irrigation has emerged as a powerful adaptation strategy for agriculture-based livelihoods. By ensuring a consistent water supply, irrigation empowers farmers to cultivate a wider range of crops and extend their growing seasons, leading to long-term food security despite the challenges posed by unpredictable weather and drought[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Given its effectiveness as an adaptation strategy to climate change, irrigation was included as one of the dependent variables analysed in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExplanatory variables\u003c/strong\u003e\u003cp\u003eThis variable can also be termed an independent variable. Independent variables can be used to predict the most important climate adaptation strategies identified. The 15 explanatory variables included socio-economic variables, namely, age, sex, education level, dependency ratio, farm size, farm experience, and livestock ownership. The Institutional variables considered in this study include access to and utilization of credit services, agricultural extension services, proximity to the nearest market, access to and utilization of climate information, and farmers' group membership. Finally, the environmental factors considered in this study encompass the diverse agro-ecologies of the study area, including kola (lowland), dega (highland), and woyna dega (midland).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAge of household head (age)\u003c/strong\u003e\u003cp\u003eAge is a continuous explanatory variable measured in years. It is hypothesized that the age structure of a household (young, middle-aged, or elderly dominant) will influence the adoption of climate adaptation strategies, with the potential for both positive and negative impacts[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, age composition served as an independent variable for predicting these dependent variables (adaptation strategies).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGender of household head (sex)\u003c/strong\u003e\u003cp\u003eThis variable indicates the sex of the person who manages the household and is usually responsible for its economic well-being. A dummy variable is coded as 1 if the household head is male; and 0 if the household head is femal. Due to cultural norms assigning domestic tasks to women and limiting their access to crucial resources such as land, cash, and labor; female-headed households often face greater challenges in adopting adaptation measures to climate change[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. It is hypothesized that gender plays a role in climate change adaptation, with male-headed farmers exhibiting a greater tendency to adopt such strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEducational level (Continuous Variable)\u003c/strong\u003e\u003cp\u003eThis variable measures the educational attainment of the household head, represented by the number of years of schooling completed. Higher levels of education are generally associated with the ability to utilize more diverse adaptation strategies[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, this study hypothesizes that there is a positive correlation between household head education and household adoption of different climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDependency Ratio (Continuous Variable)\u003c/strong\u003e\u003cp\u003eThis variable represents the proportion of individuals within the household who are considered economically inactive (children under 15 and adults 65 years and older) compared to those in the active working age range (15\u0026ndash;64 years old). Households with a greater proportion of working-age members are generally expected to have a greater capacity to adopt various climate change adaptation strategies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, this study hypothesizes that there is a negative correlation between the dependency ratio and the adoption of adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFarm Size (Continuous Variable)\u003c/strong\u003e\u003cp\u003eThis variable represents the total area of land owned by a household. Larger farm sizes generally provide greater resources and opportunities for implementing diverse adaptation strategies in response to climate change impacts[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, this study hypothesized that thee is a positive correlation between farm size and the adoption of such strategies in response to climate change.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFarming experience (Continuous Variable)\u003c/strong\u003e\u003cp\u003eThis variable represents years of farming experience, and was measured as a continuous variable. Farmers with more years of experience are more likely to adopt climate change adaptation strategies because they have better knowledge about changing weather patterns[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. While age often correlates with experience, the specific circumstances within this community suggest that this relationship may not hold true. Information sharing and technology access allow some younger farmers to accumulate more relevant agricultural knowledge than older farmers. Farmers with greater experience are expected to be more likely to adopt climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLivestock Ownership (Continuous Variable)\u003c/strong\u003e\u003cp\u003eTotal livestock ownership, measured in Tropical Livestock Units (TLU), reflects a household's dependence on natural resources such as grazing land and water. Different livestock types have varying vulnerabilities to climate change impacts such as droughts and floods. Therefore, farmers with larger livestock holdings are expected to be more likely to adopt climate change adaptation strategies[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCredit access and use\u003c/strong\u003e\u003cp\u003eAccess to and use of credit, represented by a dummy variable (1 if the household has accessed credit, 0 otherwise), facilitates access to alternative income opportunities[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This financial resource enables investment in adaptation strategies, potentially leading to a positive correlation between credit utilization and the adoption of climate change adaptation practices.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAccess to and use of agricultural extension\u003c/strong\u003e\u003cp\u003eThis is a dummy variable taking a value of 1 if the household has accessed and utilized agricultural advisory services, and 0 otherwise. Utilizing agricultural extension services equips farmers with valuable, climate-smart knowledge and practices[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This empowers them to adapt their agricultural practices to a changing climate, fostering resilience and increasing their chances of success in a dynamic environment. Therefore, a positive correlation is expected between access to and use of agricultural extension and the adoption of climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMarket proximity\u003c/strong\u003e\u003cp\u003eThis continuous variable, measured in walking hours, represents the accessibility of markets for livestock, petty trade, and other products. Proximity to markets is expected to positively influence farmers' decisions to adopt climate change adaptation strategies[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This is because easier access to markets facilitates the sale of agricultural products, potentially generating income that can be reinvested in adaptation measures. Therefore, a shorter distance to the nearest market is hypothesized to be positive correlated with the adoption of climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAccess to and use of climate information\u003c/strong\u003e\u003cp\u003eThis is a dummy variable, that takes a value of 1 if the household accesses climate information through radio or agricultural extension services, and 0 otherwise. Access to such information allows farmers to make informed decisions about adapting their agricultural practices to changing climate conditions[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Therefore, a positive correlation is expected between access to climate information and the adoption of climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFarmer group membership\u003c/strong\u003e\u003cp\u003eThis is a dummy variable, that takes a value of 1 if the household participates in farmer organizations, and 0 otherwise. Although farmer-to-farmer knowledge sharing within these groups could be a powerful tool for promoting climate adaptation strategies, their current focus seems to be primarily on local political and social networking issues. Therefore, an uncertain correlation between farmers' group membership and the adoption of climate change adaptation strategies is expected (Marie et al., 2020). It could be either positive or negative depending on the nature of the information and knowledge shared within the group.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLocal agroecology (kola)\u003c/strong\u003e\u003cp\u003eThis is a dummy variable representing the agroecological setting based on Ethiopia's traditional classification. A value of 1 indicates that the farm household resides in the \"kola\" agroecological zone (lowland), while 0 indicates residence in another agroecological zone. Due to their high exposure to climate extremes, farmers residing in \"kola\" agroecology areas (lowlands) often adopt various adaptation strategies suitable for their specific conditions[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, being located in the Kola agroecology is expected to be positivily correlated with the adoption of climate change adaptation strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLocal agroecology (Dega)\u003c/strong\u003e\u003cp\u003eThis dummy variable, based on Ethiopia's traditional agroecological zone classification, pinpoints a farm household's location within the \"Dega\" (highland) agroecology. The distinct climate challenges faced by residents in this zone necessitate the adoption of various adaptation strategies. Therefore, residing in the Dega agroecology area is expected to positively correlate with the adoption of climate change adaptation practices[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLocal agroecology (Woyna Dega)\u003c/strong\u003e\u003cp\u003eThis dummy variable represents residence in the \"Woyna Dega\" (midland) agroecology zone based on Ethiopia's traditional classification. Compared to \"kola\" (lowland) agroecology, Woyna Dega (midland) experiences fewer extreme climate events. This leads to the expectation that farmers residing there may adopt fewer climate-specific adaptation strategies. Therefore, this study hypothesizes an uncertain correlation between Woyna Dega's residence and the adoption of climate change adaptation practices[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The specific direction of the correlation (positive or negative) could depend on the types of adaptation strategies considered and the overall approach to climate change management within the Woyna Dega agroecology.\u003c/p\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\u003eSummary of dependent and independent variables for MVP model regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDependent variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChange planting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdopt drought-resistant crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonfarm income(migration)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrop with legume production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrrigation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConservation tillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eIndependent(explanatory) variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge of the HHHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex of HHHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation status of HHHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependency ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber dependent family members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (number)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmland size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize of farmland owned by the HHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (Ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarming experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of years of farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLivestock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock owned by HHHs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous (TLU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCredit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccess to credit services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccess to Extension services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccess to market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccess to climate information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer group membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelongs to a farmer\u0026rsquo;s group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKola\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal agro-ecology kola\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDega\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal agro-ecology kola\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eW/Dega\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal agro-ecology woyna dega\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDummy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Authors\u0026rsquo; computation, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Statistical Findings\u003c/h2\u003e\u003cp\u003eTo reduce the negative impacts of climate change and control its future trends, smallholder farmers in the study area have adopted a variety of adaptation techniques. The majority of farmers in the study area who adopted adaptation strategies opted for adopting seasonal migration (52.6%), and conservation tillage (51.5%). Other widely implemented practices included adopting drought-resistant crops (49.3%). As confirmed by key informants, conservation tillage plays a crucial role in maintaining soil health and fertility. By leaving crop residues on the surface, plants protect the soil from erosion, promote moisture retention, and encourage beneficial microbial activity. This translates to better yields in the long run and contributes to a more sustainable farming system. Proactive migration can be a lifeline in the face of climate change, safeguarding lives from sudden disasters and building resilience through income diversification [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings further confirmed that growing crop types resistant to drought is an effective way to address the challenges posed by climate change, particularly in the hotter district of Deguna Fango (kola agroecology) in the study area. Our findings align with thos of [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], who reported observation in developing countries that growing drought-resistant crops is a key strategy for sustainable agricultural growth and food security in the face of climate change. Moreover, climate change adaptation research is built up on with the recurring themes of drought-resistant crops, seasonal migration, and conservation agriculture[\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, among those smallholder farmers who employed adaptation measures, other specific adaptation techniques included mixed crop with legume production (48.50%), adjusting planting date (48.2%), and adopting small-scale irrigation (38.3%). Discussions with the Focus group confirmed that low adoption of irrigation in Dega and Woyna Dega agro ecologies stemmed from three interconnected challenges: insufficient water sources, scarce irrigation infrastructure, and knowledge gaps among farmers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdaptation Strategies Employed by Farm Households (N\u0026thinsp;=\u0026thinsp;371)\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\u003eS/no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdaptation strategies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRespondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIrrigation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed crop and legume production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusting planting date\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal migration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShift to drought tolerance crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConservation tillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Authors\u0026rsquo; computation, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFarmers, on the other hand, may employ a complicated strategy to manage the varied challenges posed by climate change, combining or overlapping several adaptation techniques to preserve resilience [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The multivariate probit model's likelihood ratio test confirmed its overall significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 0.05, 0.10), indicating that the choices of climate change adaptation strategies are not independent.\u003c/p\u003e\u003cp\u003eThe results from the model further revealed positive correlations among several adaptation strategies, suggesting their mutual interdependence in building resilience. Irrigation and crop-legume production exhibited a positive relationship, highlighting their potential value in addressing water scarcity and soil health. The dense root systems of legumes help retain moisture in the soil, minimizing water evaporation and runoff. This natural water conservation allows more efficient irrigation use [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Similarly, adopting drought-resistant crops was positively associated with conservation tillage, suggesting the use of a combined approach to enhance drought tolerance and soil conservation.\u003c/p\u003e\u003cp\u003eCombining legume intercropping with flexible planting based on seasonal factors offers a double win for farmers. Legumes boost soil health and nitrogen levels, while adaptable planting schedules capitalize on optimal climate and pest control, as research shows [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This flexibility further strengthens the link between seasonal migration and agricultural success. Precise planting timing around migration periods allows farmers to secure additional income without compromising the critical window for optimal crop growth. Furthermore, adjusting planting dates and practising conservation tillage displayed a positive interdependence, demonstrating their synergy in optimizing sowing periods and safeguarding soil health. Seasonal migration might be negatively associated with strategies such as conservation tillage and mixed cropping with legumes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, developing alternative income sources during nonmigration periods can lessen the reliance on migration earnings and create conditions for adopting sustainable agricultural practices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Barriers to climate change adaptation\u003c/h2\u003e\u003cp\u003eA multitude of barriers prevent farmers in the study area from effectively combating climate change challenges through adaptation strategies. These include knowledge gaps on adaptation alternatives, a lack of information about the potential consequences of climate change, a lack of access to water resources, a lack of irrigation infrastructure, high-cost farm inputs, a shortage of farm size, inadequate institutional support, and a lack of motivation. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that the greatest hurdles to adapting to climate change are a lack of awareness about options (17%), water scarcity (15%), and limited access to climate information (14%). However, farmers were less concerned with high input costs (9%), lack of motivation (10%), and land shortages (11%) as barriers to adaptation. Our findings align with those of [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]; who identified a lack of readily available climate information, coupled with water scarcity and knowledge gaps, as significant constraints on farmers' ability to cope with climate change-induced hazards. This underscores the need for comprehensive interventions addressing information access, resource management, and skill development to bolster farmers' climate resilience.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Determinants of smallholder farmers' adaptive strategies\u003c/h2\u003e\u003cp\u003eThe multivariate probit model, as detailed in the methodology, proved instrumental in identifying the complex interplay of factors shaping farm households\u0026rsquo; choice of adaptation strategies in the face of climate change. This model allows researchers to explore the relationship between a set of independent variables and multiple dependent variables simultaneously, which is particularly useful when studying complex phenomena such as farmers' adaptation strategies to climate change [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. By using this method, the researchers were able to analyse how various factors influence farmers' decisions to adopt specific adaptation strategies.\u003c/p\u003e\u003cp\u003eAs indicated in the study's findings, smallholder farmers in the study area frequently used six key adaptation measures to combat the challenges of climate change: irrigation, mixed crops with legumes, adjusting planting dates, seasonal migration, shifting to drought tolerant crops and conservation tillage. The study also investigated how a variety of factors, including demographic characteristics, socioeconomic settings, institutional context, and availability of natural resources influence the adaptation strategies chosen by farmers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate probit parameter estimates for factors influencing climate change adaption\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eIrrigation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003elegume production\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAdjusting planting date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eSeasonal migration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDrought tolerance crop\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eConservation tillage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0171219*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0096735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0041438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0091904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0027007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0091049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0008089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0091269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0002953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.0091646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.0007773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.0091148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7503416**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.342489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.1499492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2841586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.1326087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2864626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0445032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.2781472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0192449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.2798095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0510346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.2973209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.016837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0297274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.053674**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0284506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0219046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0280855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.0174568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.0279303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0156053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.0280536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD/ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1246347*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0749314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0084857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0707039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0147756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0708747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0401078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0703671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.028311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.0693062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.0384639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.071489\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4930842***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1218711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1863465*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1108907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0858456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1076629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0533822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1074704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.1291842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1092024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.0880682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1056002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0442101***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0127496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0192095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0124268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0026061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0124604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0204023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0123905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0202624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.0123163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0077152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.0124302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0131702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0011831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0770905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0092919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0777982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0458336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0766949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0176054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.0764699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.167087**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.0786229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCredit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0527264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1464825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3315724**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.138972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.017992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1390819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0909474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1383293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.2703377**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1377767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.3148891**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1401041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.1397133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1411988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.202842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1341167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0545458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1343632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1068957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1335078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.3107024**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1330601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.5010264***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1356523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0330016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1436651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2211665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1363602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.239375*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1372302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0830627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c5\"\u003e\u003cp\u003e0.1351987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2406723*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1356692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.2410844*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1347042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0079196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1341821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.2478563*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1368091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFGM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.3452937**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1429497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.152809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1355646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0584141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1358805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0501195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1349629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.0301526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1345276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0358389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1368667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKola\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1961201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1422619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3469267**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1358219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.217354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1362745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.0922454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1350683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0940952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1343274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.3265518**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1369557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDega\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0524645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1426385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0254895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1352255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5065498***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.135916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2991447**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1345404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.2092351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1340391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.0298931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1358666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eW/Dega\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1952493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1690804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0070473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1607432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2388351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1618797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3294452**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.1634478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0653124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.159943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1443841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.1636869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.32172**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6781722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.7716498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.621872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.3028682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.6227885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4319757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.6193538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.5764455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.6192048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-5938135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.6270044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNotes: ***, **, and * denote significe at the 1%, 5% and 10% levels respectively.\u003c/p\u003e\u003cp\u003eLikelihood ratio test of rho chi2 (15)\u0026thinsp;=\u0026thinsp;23.3521; prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0000; Number of obs\u0026thinsp;=\u0026thinsp;371; Wald chi2 (90)\u0026thinsp;=\u0026thinsp;167.16.\u003c/p\u003e\u003cp\u003eSource: Own Computation, 2022\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1. Age of the household head\u003c/h3\u003e\n\u003cp\u003eAge, serving as a proxy for farming experience, had a significant influence on the adaptation strategies farmers chose in the face of climate change. A farmer's exposure to various agricultural experiences, systems, and seasons is influenced by their age [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, age commonly serves as a proxy for experience, and some younger farmers, armed with new technologies and scientific insights, also play a crucial role in developing adaptation strategies. Age at which the household head was headed negatively and significantly affected the choice of irrigation adaptation strategy compared to choice of other adaptation strategies at less than a 10% significance level. This finding implies that as the age of the household head increases by one year, the probability of using irrigation as an adaptation strategy decreases by 1.7%. These findings further confirmed the hypothesis and indicated that older household heads were less likely to adopt irrigation for climate change adaptation (Table\u0026nbsp;4.10).\u003c/p\u003e\u003cp\u003eThe higher physical demands and skill requirements of irrigation practices could explain why older farmers, despite their extensive experience, are less likely to choose this appraoch as a climate change adaptation strategy [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. According to the findings, younger household heads may be better suited to managing irrigation practices due to their greater physical agility and possibly greater risk tolerance. These findings are consistent with those of [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]; but diverge from those of [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]; who limited their view to experiences. Moreover, Triangulation of data from interviews and focus group discussions confirmed that age acts as a key factor influencing irrigation adoption, with younger farmers demonstrating a stronger propensity to embrace this practice.\u003c/p\u003e\n\u003ch3\u003e2. Gender of the household head\u003c/h3\u003e\n\u003cp\u003eThe gender of the household head plays a key role in shaping farmers' choices of adaptation strategies for climate change. Numerous studies have confirmed that gender plays a significant role in determining the adaptation measures that farmers choose to implement in the face of climate challenges[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. This study revealed that the gender of the household head positively and significantly affects the choice of irrigation adaptation strategy compared with the choice of other adaptation strategies at the 5% significance level. This finding implies that a household headed by a male has a 75% greater probability of choosing irrigation as an adaptation strategy than other adaptation strategies.\u003c/p\u003e\u003cp\u003eThis can be attributed to the fact that male-headed households often have greater access to resources such as labor, finance, and technology, making it easier for them to adopt irrigation as an adaptation strategy to climate change. In line with [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], our findings confirmed that access to technologies and climate information is often skewed toward male-headed households. Similarly, [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] found that women, unlike men, face restrictions in accessing information, land, and other resources. To fully address the pressing challenge of climate change, dismantling the restrictive walls of traditional gender norms, and unlocking the immense potential of female farmers to lead the way in implementing successful adaptation strategies are critical.\u003c/p\u003e\n\u003ch3\u003e3. Education level\u003c/h3\u003e\n\u003cp\u003eEducation emerged as a significant factor influencing farmers' climate adaptation choices. Education plays a crucial role in shaping farmers' responses to climate change. It equips them with the knowledge and skills to adopt innovative adaptation strategies, fostering a shift from traditional practices. The study showed that the education level of household heads positively and significantly affects the choice of adjusting planting dates as an adaptation strategy compared to other adaptation strategies at a less than 5% significance level. This finding implies that for every one-year increase in education level, the probability of adopting adjusted planting dates increased by 5.3% relative to that of other adaptation strategies. Farmers with higher education levels tend to have a stronger understanding, more informed beliefs, and clearer interpretations of climate change. This, in turn, influences the actions they take in response to its impacts [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e4. Dependency Ratio\u003c/h3\u003e\n\u003cp\u003eEnhanced climate change resilience was observed in households with a greater number of active members [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The household dependency ratio negatively and significantly affects the choice of irrigation as an adaptation strategy compared to other adaptation strategies at a less than 10% significance level. This finding implies that for every one unit increase in a household's dependency ratio, the likelihood of choosing irrigation as an adaptation strategy decreased by 12% compared to that of other options. An increase in irrigation's labor needs becomes an advantage for households with a larger workforce, allowing them to maximize its benefits [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. By investing this manpower, they can meticulously manage water application, potentially leading to significantly higher yields than traditional, less labor-intensive methods. This finding aligns with privous findings[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], reported that households with more working members were more likely to adopt irrigation adaptation strategies to address climate change impacts than others were.\u003c/p\u003e\n\u003ch3\u003e5. Farm size\u003c/h3\u003e\n\u003cp\u003eLarger farm sizes emerged as a significant factor that positively influenced the choice of irrigation as an adaptation strategy[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The study showed that the household head's farm size positively and significantly affects the choice of irrigation as an adaptation strategy compared to other adaptation strategies at the 1% significance level. This finding implies that for every additional hectare of land, the likelihood of households adopting irrigation as an adaptation strategy increased by 49.9% compared to that of households adopting other strategies.\u003c/p\u003e\u003cp\u003eSimilarly, farm size positively and significantly affects the adoption of mixed crops with legume production at 10% significance levels. This suggests that larger farms may have resources or flexibility that facilitate incorporation of legumes into their cropping systems. The additional hectare of farmland increases the likelihood of a household adopting legume integration by 18.6%; compared to that of other adaptation strategies. This aligns with the existing related research, such as [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], demonstrated that farm size positively and siginificantly affects the adoption of both irrigation and mixed cropping with legumes as climate change adaptation strategies compared to other alternative strategies.\u003c/p\u003e\n\u003ch3\u003e6. Farm experience\u003c/h3\u003e\n\u003cp\u003eOne of the significant variables explaining why farmers choose certain adaptation strategies to address climate change is their accumulated farming experience. The study findings revealed that farm experience positively and significantly affects the adoption of irrigation as an adaptation to climate change compared to other strategies at the 1% significance level. This implies that each additional year of experience, farmers are 4.4% more likely to use irrigation in response to changing climate impacts than to other strategies. The results of this study are consistent with those of [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] in the context of Ethiopian agriculture, further confirming that farming experience plays a crucial role in promoting the adoption of irrigation and other climate change adaptation measures.\u003c/p\u003e\n\u003ch3\u003e7. Livestock ownership\u003c/h3\u003e\n\u003cp\u003eLivestock production plays multifaceted roles in rural communities, contributing to both economic stability and climate change resilience [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. It serves as a source of financial security, provides animal power for farm work, enriches the soil with nutrient-rich manure, and promotes essential moisture retention. This study revealed that livestock ownership positively and significantly affects the adoption of conservation tillage as an adaptation strategy to climate change at the 5% significance level. This implies that for each additional unit of livestock, measured in Tropical Livestock Units (TLUs), farmers are 16.7% more likely to implement soil-saving practices such as conservation tillage, translating to potentially significant improvements in soil health and resilience in the face of climate change compared to other strategies. Interviews with key informants also revealed that herd size positively influences adaptability. While larger herds can bring social status and economic benefits, their factual advantage in adapting to climate change lies in enabling the use of conservation tillage practices[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e8. Use of Credit\u003c/h3\u003e\n\u003cp\u003eCredit has becomes a powerful tool for farmers, enabling them to adopt resilient methods suh as mixed crop-legume production and conservation tillage [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. It empowers them to overcome financial limitations and invest in adaptation strategies that will secure their future. The study showed that using credit positively and significantly affected legume intercroping, druoght tolerance crops and importance of conservation tillage at less than 5% siginificance level. This implies that a one-unit increase in credit use leads to a 33% increase in farmers adopting mixed crop-legume production, a vital strategy for adapting to climate change compared to others methods. Similarly, a unit increase in credit use leads to a 27% and 31% increase in the likelihood of adopting drought tolerance crop variety and implementing conservation tillage as an adaptation strategy, respectively, compared to other strategies.\u003c/p\u003e\u003cp\u003eFinancial limitations can be a hurdle for farmers adopting practices that address climate change challenges[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The study suggested that improved financial resources can help to overcome this barrier and encourage widespread adoption of climate resilience practices such as conservation tillage, drought tolerant crops and legume intercropping. This sustainable approach not only benefits them today but also fosters long-term profitability, environmental health, and strong communities in the face of climate challenges. Like [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], our study underscores the catalytic effect of credit access in equipping farmers with the necessary inputs to embrace climate-smart strategies.\u003c/p\u003e\n\u003ch3\u003e9. Participation in Extension Services\u003c/h3\u003e\n\u003cp\u003eThe positive coefficient of extension delivery highlights its influential role in promoting adaptation strategies. The study highlights how essential these services are for farmers to embrace climate risk-reducing practices. Participation in extension service programs led to increase in farmers adopting climate-smart practices, such as adopting drought resistance crop varieties [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These findings revealed a substantial positive and significant impact of extension programs on crops with druoght resistance at less than 5%. This finding implies that farmers enrolled in extension programs were morethan 31% more likely to adopt drought-resistant crops than were those enrolled in other adaptation strategies. However, the effect of participation in the extension program was not significant for adopting conservation tillage at the 1% significance level. This finding implies that, compared with other strategies, participation in extension programs is less likely to lead to conservation tillage adoption by 50%. These findings suggest that extension programs may currently prioritize promoting mechanization and fertilizer use over traditional conservation tillage practices. By bridging the information gap and offering ongoing support, robust extension services empower farmers to embrace climate-smart practices as demonstrated in previous studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Investing in this service empowers farmers to make informed decisions and build resilience in the face of climate change.\u003c/p\u003e\n\u003ch3\u003e10. Information on climate change\u003c/h3\u003e\n\u003cp\u003eInformation access on the potential impacts of climate change empowers farmers to become proactive agents of climate change [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. It transforms weather pattern from passive observers to informed decision makers, strategically adapting their practices to ensure the long-term sustainability of their farms and communities. The findings of this study revealed that access to and use of climate information have positively and siginificantly affected the adoption of adjusting planting dates as an adaptation strategy at the 10% siginifice level. This means that farmers who have access to and use climate information were 23.9% more likely to modify their planting times in response to changing climate conditions than were those who use other strategies.\u003c/p\u003e\u003cp\u003eSimilarly, key informants were also cliamed that climate information empowers farmers to embrace a range of adaptation strategies, from adjusting planting dates to diversifying income. The authors also highlighted the importance of timely climate information delivery through media such as TV, radio, and early warning systems. This information is essential for implementing successful climate change adaptation strategies. These findings aligns with those of [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], who found that empowering farmers with climate information helps them navigate changing environments and adopt proactive solutions such as adjusting planting dates.\u003c/p\u003e\n\u003ch3\u003e11. Market Access\u003c/h3\u003e\n\u003cp\u003eMarket access motivates farmers to produce marketable crops, boosting their income and building their adaptability ta a changing climate [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. By connecting to markets, communities gain access to resources and information, empowering them to develop and implement innovative climate change adaptation strategies. The study findings revealed that market access positively and significantly affected the adoption of legume intercropping at less than a 5% significance level; and that planting time, and conservation tillage had significant effects at a 10% significe level. This means that farmers closer to markets were more likely to adopt climate-resilient practices such as mixed cropping (26%), adjusted planting dates (24%), and conservation tillage (24.7%) than other adaptation strategies were. However, market access also had a significant negative impact on seasonal migration patterns at the 10% significance level. This suggests that easier access to markets may have reduced the need for people to migrate seasonally in search of work or income by 24% compared to other adaptation strategies (Table\u0026nbsp;4.10). As claimed by key informants, legumes such as beans, peas, and lentils offer two benefits. They improve soil health, leading to a more resilient farming system, and the additional income stream from legume sales fosters a more profitable system. This result is consistent with that of [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], who showed a positive influences of market access on mixed crops with legume production, conservation tillage, and adjusting planting dates as an adaptation strategy to climate change.\u003c/p\u003e\n\u003ch3\u003e12. Membership in farmer-based organizations\u003c/h3\u003e\n\u003cp\u003eFarmer-based organizations play a crucial role in climate change adaptation. These groups not only provide valuable information on new practices; but also foster space for farmer-to-farmer learning[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The study findings revealed that farmer-based organizations negatively and significantly affected the adoption of irrigation as an adaptation strategy to climate change at less than a 5% significance level. This suggests that farmers involved in such organizations may seek alternative approaches to managing climate risks. Farmers who were members of farm group were 34.5% less likely to adopt irrigation as a way to adapt to climate change than others were, suggesting that irrigation infrastructures are not easily available in the study area. Farmer-to-farmer knowledge sharing promotes practical water conservation methods that do not require expensive irrigation systems.\u003c/p\u003e\u003cp\u003eMoreover, focus group discussions revealed a paradoxical preference for irrigation as a climate adaptation in the study area, particularly for dega and woyna dega agro-ecologies with scarce water resources, poor irrigation infrastructure, and challenging topography. This suggests that alternative solutions, such as improved water management practices and context-specific infrastructure development, are crucial for addressing the water needs of these communities and supporting their adaptation efforts. While this study highlights the value of farmer-based organizations in knowledge sharing and adaptation, it is important to consider potential nuances. Aligned with these findings[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] farmers based on organizations may, in some cases, discourage the adoption of specific adaptation strategies, such as irrigation.\u003c/p\u003e\n\u003ch3\u003e13. Agroecological settings\u003c/h3\u003e\n\u003cp\u003eAgroecological variations drive diverse adaptation strategies among households [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Faced with diverse climatic challenges, households across agroecology have crafted different ingenious adaptation tools [90]. Thus, this study investigated the influence of agroecological variations (Kola, Woyna Dega, and Dega) on the adoption of adaptation strategies to climate change challenges. This study revealed kola agroecology positively and significantly affected the adoption of two climate-resilient practices; crop production with legumes and conservation tillage; at less than 5% siginificance level. This finding implies that kola agroecology increased the likelihood of adopting crop production with legumes and conservation tillage as adaptation strategies by 34.6% and 32.6%, respectively, compared to other adaptation strategies. Focus group discussions in Kola agroecology further highlighted the importance of climate-smart practices such as conservation tillage and legume integration due to the challenges of moisture scarcity and infrequent rainfall. These practices can help retain moisture in the soil and improve soil fertility, making them wellsuited for addressing existing challenges.\u003c/p\u003e\u003cp\u003eHowever, dega agroecology positively and significantly affected the adoption of adjusting planting dates and seasonal migration as adaptation strategies by 1% and less than 5%, respectively. This finding implies that, compared with farmers in other adaptation strategies, farmers in dega agroecology were 50.6% more likely to modify planting times and 29.9% more likely to engage in seasonal migration. Moreover, key informant interviews confirmed that dega agroecolgy enjoys relatively stable temperatures, abundant rainfall, and minimal moisture scarcity compared to other [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e],agroecologies. Similarly, the results of this study revealed distinct climate adaptation strategies: dega and woyna dega farmers prioritized seasonal migration and adjusted planting dates, while kola farmers favor drought-resistant crops and adopted conservation tillage.\u003c/p\u003e\u003cp\u003eFurthermore, woyna dega agroecology has a substantial positive and significant impact on seasonal migration at the 5% significance level. Taken together,thses findings suggest that,compared with farmers in other adaptation strategies, farmers in woyna dega agroecology increased their adoption of seasonal migration by 32.9%. As key informants, land scarcity and water shortages were found to be key drivers of Woyna dega farmers' preferences for seasonal migration as part of climate change adaptation strategy, suggesting complex interactions between resource limitations and livelihood choices. Consistent with [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], who found farmers in woyna-dega agroecology, where climate impacts are less severe, they adopted fewer adaptation strategies such as seasonal migration.\u003c/p\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eClimate change is no longer a distant threat; but rather a present truth demanding immediate action. Farming communities across the globe are joining hands, raising challenges with shared purposes.Through collaborative efforts and the adoption of smart adaptation strategies, they are protecting their food security and paving the way for a sustainable future. Under the pressure of a changing climate, farming communities in the study area are actively seeking ways to minimize the impact of climate change. To shed light on how farming communities in the study area fight against a changing climate, this study investigates their diverse adaptation strategies and major drivers, empowering communities to increase their challenges and secure their future. The MVP model served as a powerful tool for predicting how farmers might respond to changing climate conditions based on their specific circumstances. Considering household, institutional, and social factors; helps policymakers and development organizations tailor interventions to promote effective adaptation strategies.\u003c/p\u003e\u003cp\u003eThe results revealed that; among alternative adaptation strategies, sex, farm size, and farm experience were positively associated with the adoption of irrigation as an adaptation strategy to climate change in the study area. However, the age of the household head, dependency ratio, and farmer group membership all negative correlated with irrigation as an adaptation strategy. Farm size, credit access, market proximity, and kola agroecology all positively influence the adoption of legume crops as climate change adaptation strategies. Education, information access, market proximity, and Dega agroecology positively influence the adoption of adjusting planting dates as an adaptation strategy. Dega and Woyna Dega agro-ecologies positively influenced seasonal migration adoption as a climate adaptation strategy, while market access had a negative impact. Credit and extension access positively influenced the adoption of drought-resistant crops, while extension alone negatively impacted the adoption of conservation tillage for climate change adaptation. Livestock ownership, credit access, market proximity, and kola agroecology all positively influence the adoption of conservation tillage as a climate change adaptation strategy. This implies that the choices of climate change adaptation strategies are not independent; but rather affected by complex interactions among socioeconomic, institutional, and environmental factors.\u003c/p\u003e\u003cp\u003eEmpowering farm households with vital information, resources, and infrastructure unlocks their ability to implement successful adaptation measures. By partnering with government initiatives, farmers can take ownership of their adaptation strategies and work together to create a more sustainable future. Thus, governments and NGOs should collaborate with indigenous communities by providing financial support, technical training, and formal recognition of traditional wisdom to promote their adaptation strategies. NGOs promoting climate-smart agriculture may bridge the adaptation gap by offering specialized training and support in areas with low adoption rates, ensuring that all farmers have the tools they need to prosper in a changing climate. More experts and extension officers may also use the research findings to help farmers in their local working areas boost agricultural productivity by adapting to climate-friendly adaptation alternatives. Moreover, empowering rural communities requires looking beyond the farm. Building safe net of reliable income through nonfarm activities that thrive in diverse weather conditions strengthens resilience and ensures long-term economic stability. To combat the challenges of tomorrow, future policy must ignite a wave of understanding. Investing in knowledge-sharing platforms such as training, conferences, and seminars equips communities with the tools and skills needed to address climate change adversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no conflicts of interest among the authors regarding the publication of this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to Publish declaration\u003c/h2\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eNo funding is received from any source.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthorship contribution statement1. Daniel Dalle: Methodology, Writing \u0026ndash; review \u0026amp; editing. 2. Yisak Gecho: conceptualization, methodology, writing \u0026ndash; review \u0026amp; editing. 3. Sisay Belay Bedeke: Formal analysis, writing, conceptualization, methodology, writing draft \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Wolaita Sodo University for allowing us to conduct different research studies including this one.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data sets used to support the findings of the study are available from the corresponding author(s) upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. B. Aboye, J. Kinsella, and T. Leza, \u0026ldquo;Major climatic changes experienced by farm households: Evidence from the lowlands of Southern Ethiopia,\u0026rdquo; \u003cem\u003eAdv. Agric. 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Available: https://ccafs.cgiar.org/donors.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"adaptation options, climate change, multivariate probit model","lastPublishedDoi":"10.21203/rs.3.rs-7076425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7076425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change creates enormous hardship, particularly for rural farmers whose livelihoods rely on rain-based agriculture. To assist farmers in dealing with climate change, it is critical to understand what factors influence their decision-making on how to adapt. Thus, the main aim of this study was to identify factors influencing farm households' adaptation choices in southern Ethiopia. To acquire quantitative data, a cross-sectional survey design was utilized with 371 randomly selected households. Focus group discussions, key informant interviews, and field observations were employed to gather qualitative data, complemented by a thorough review of relevant academic publications, and reports. This study employed descriptive statistics and the MVP model to investigate the factors driving adaptation attempts and the barriers impeding them. Among several climate change adaptation choices, seasonal migration was the most common among households, accounting for 52.6%. Other strategies, such as conservation tillage (51.5%) and drought-resistant crops (49.3%), were also commonly utilized, with shifting planting dates and irrigation following closely behind (48.2% and 38.3%). However, socioeconomic, environmental, and institutional factors play major roles in influencing the adoption of different climate change adaptation approaches. Thus, policies and programs aimed at reducing the effects of climate change should consider the important roles of these factors.\u003c/p\u003e","manuscriptTitle":"Factors Affecting Climate Change Adaptation Strategies and Existing Barriers among Stallholder Farmers in Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 08:49:17","doi":"10.21203/rs.3.rs-7076425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-05T10:03:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T04:36:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-02T01:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57888617339033386373246613191176906059","date":"2025-07-30T20:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330469653630871568026394488208085883376","date":"2025-07-27T08:14:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T06:17:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241852428387984124247841150111075774442","date":"2025-07-25T06:13:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317317376519182468010658576309652760640","date":"2025-07-24T09:58:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72815481656321199440652420621553379075","date":"2025-07-23T05:32:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T04:40:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T10:14:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T10:12:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-07-08T15:32:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d3f67f5-bf43-4fa8-a444-a4da4a53738c","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:06:31+00:00","versionOfRecord":{"articleIdentity":"rs-7076425","link":"https://doi.org/10.1007/s43621-025-02450-9","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-12-31 15:58:20","publishedOnDateReadable":"December 31st, 2025"},"versionCreatedAt":"2025-07-25 08:49:17","video":"","vorDoi":"10.1007/s43621-025-02450-9","vorDoiUrl":"https://doi.org/10.1007/s43621-025-02450-9","workflowStages":[]},"version":"v1","identity":"rs-7076425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7076425","identity":"rs-7076425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}