Coffee Production Trends and Adoption of Climate-Smart Agroforestry Practices Among Smallholder Farmers in Sidama Ethiopia

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Using a mixed-methods design, data were collected from 360 randomly selected coffee farmers across four districts, complemented by long-term climate data and secondary sources. Descriptive statistics, trend analysis, and a two-limit Tobit model were employed to examine production trends, climate variability, and CSF adoption drivers. Coffee yields declined from 11 quintals/ha (2014) to 8.6 quintals/ha (2024), and total production fell from 456,828 quintals (2015) to 204,829 quintals (2020), despite the expansion of cultivated land. Climate data show moderately stable rainfall (1,185–1,294 mm) and temperatures (24.2–26.5°C), but still suboptimal for Arabica coffee. Farmers report unseasonal rains, erratic patterns, and temperature shifts as key threats. Under adverse weather, average household coffee yield dropped by 4.9%. Adoption of CSF practices is moderately high (index = 0.71), especially for weed control (89.8%), intercropping (89.1%), and shade management (83.8%), but lower for site-specific planting (26.1%) and soil moisture management (38.8%). The Tobit model (pseudo R² = 0.923) shows adoption is positively influenced by male headship, education, extension access, and cooperative membership, while livestock ownership has a slight negative effect. These findings call for improved advisory services, farmer education, and targeted interventions to enhance resilience and secure coffee-based livelihoods under changing climate conditions. Climate variability Coffee production Climate-smart farming Smallholder farmers Agroforestry systems Figures Figure 1 Figure 2 Figure 3 1. Introduction Ethiopia, the birthplace of Coffea arabica, relies heavily on coffee as a cornerstone of its economy and rural livelihoods. Coffee contributes approximately 30–35% of the country’s foreign exchange earnings and supports the livelihoods of over five million smallholder farmers, influencing the well-being of more than 15 million people along its value chain (USDA, 2025). Most Ethiopian coffee is cultivated under traditional agroforestry systems, where coffee is intercropped with food crops and shade trees (SCFCU, 2025). These systems not only support coffee production but also enhance ecosystem services such as biodiversity conservation, soil fertility, and water regulation (ICO, 2024). Among Ethiopia’s coffee-producing regions, the Sidama National Regional State is one of the most prominent, producing a significant portion of the country’s high-quality Arabica coffee. Sidama’s unique microclimates, fertile soils, and altitudinal range between 1,500 and 2,200 meters above sea level create ideal growing conditions, enabling the slow ripening of coffee cherries and contributing to the region’s international reputation for flavor-rich specialty coffee (Perfect Daily Grind, 2020 ). Coffee in Sidama is primarily grown by smallholder farmers operating on less than five hectares of land, often using semi-traditional methods and supported by cooperative networks such as the Sidama Coffee Farmers’ Cooperative Union (SCFCU) (SCFCU, 2024). However, climate variability and change pose serious threats to the sustainability of coffee production in the region. Increasing temperatures, erratic rainfall patterns, and prolonged dry spells are disrupting flowering, fruit development, and yields, while also intensifying the incidence of pests and diseases. Recent data indicate a decline in coffee yields across Ethiopia from 0.92 to 0.68 tons per hectare over the past two decades, reflecting a broader vulnerability within the Arabica coffee sector, which is highly sensitive to temperature fluctuations above 23°C and irregular rainfall (Nigussie, 2025 ; Zinash, 2023 ). These climatic stressors threaten the stability of rural livelihoods and the economic viability of Ethiopia’s coffee sector (World Coffee Research, 2024 ). To mitigate these risks, climate-smart agriculture (CSA) has emerged as a critical strategy. CSA promotes practices that simultaneously enhance productivity, build resilience, and reduce greenhouse gas emissions. In the context of coffee agroforestry, CSA includes interventions such as the use of improved coffee varieties, shade tree management, intercropping, soil and water conservation, mulching, and integrated pest management. These practices can buffer the adverse impacts of climate variability, improve ecological resilience, and help sustain coffee yields. However, despite their potential, the adoption of CSA practices remains uneven. In Sidama, the effects of climate change are already visible, with coffee productivity declining from 1.05 to 0.95 tons per hectare over the past eight years. Yet, CSA adoption remains modest among smallholder farmers, who often lack the resources, knowledge, and institutional support necessary for widespread implementation. Understanding how farmers perceive climate risks, the factors influencing their decision-making, and the extent to which CSA is being adopted is critical for designing effective adaptation strategies and policy interventions. This study aims to examine the interlinked dynamics of climate variability, coffee production, and the adoption of climate-smart practices among smallholder farmers in the Sidama Region. Specifically, the study seeks to: (1) analyze trends in the area harvested, production, and yield of smallholder coffee; (2) examine trends and variability in rainfall and temperature within coffee-based agroforestry systems; (3) assess the probability and severity of climate-related risks affecting coffee production; (4) investigate the adoption status and variability of CSA practices; and (5) identify the key socio-economic and institutional determinants influencing CSA adoption. The findings are expected to inform evidence-based strategies for enhancing the resilience and sustainability of coffee production in climate-vulnerable regions of Ethiopia and beyond. 3. Research Methodology 3.1 Study area This study was conducted in the Sidama National Regional State of Ethiopia. Agriculture, including both crop and livestock production, is the primary livelihood for rural households in the region. Sidama is renowned as one of Ethiopia's leading coffee-producing areas, significantly contributing both washed and unwashed coffee to both domestic and international markets. The region's production system is predominantly characterized by smallholder garden coffee cultivation. Sidama is composed of 28 coffee-growing districts, but this study specifically focuses on four districts: Shebedino, Dale, Aleta-Wondo, and Bensa. Geographically, the study area lies between latitudes 6°20'0''N and 7°0'0''N, and longitudes 38°20'0''E and 39°0'0''E. The altitude in the region ranges from 500 to 3,500 meters above sea level, with average temperatures between 15°C and 24.9°C. The average annual rainfall varies between 1,200 mm and 1,999 mm. The selected study districts are shown in Fig. 1. 3.2 Data collection methods This study focuses on key aspects such as climate-smart coffee farming practices, the adoption and uptake of technology, trends in rainfall and temperature, and the determinants influencing technology uptake. The target population consists of smallholder farmers in coffee-producing areas, specifically smallholder garden coffee farmers in Sidama, Ethiopia. Primary data were collected from 360 coffee farming households through a semi-structured questionnaire. Additionally, secondary data were gathered from relevant scientific journals, annual reports from local offices, and rainfall and temperature data provided by the National Meteorology Agency of Ethiopia. A multistage sampling technique was used to select the study districts, kebeles (small administrative units), and households. Initially, four representative districts were purposively selected from the 28 coffee-growing districts in consultation with experts from the Sidama Region Coffee and Tea Authority. Next, 12 kebeles were selected from the 118 kebeles in the chosen districts, based on variations in agro-ecology and coffee production potential. Finally, 360 coffee farming households were randomly selected from the sampled kebeles. 3.3 Data analysis The collected data were analyzed using both qualitative and quantitative methods, combining analytical and statistical tools. Qualitative data collected through interviews were analyzed using a narrative analysis approach. This method involved identifying key themes and patterns emerging from participant responses. Descriptive statistics were used to characterize the households and their coffee production, as well as to examine trends in rainfall and temperature. Graphs and coefficients of variation were employed to illustrate trends and the magnitude of changes in rainfall and temperature. A risk prioritization matrix was also utilized to assess the likelihood of risks arising from climate change. For the econometric analysis, a two-limit Tobit model was used to identify factors influencing the adoption of climate-smart agricultural (CSA) coffee farming practices. CSA coffee farming practices were treated as the dependent variable, measured through a sustainable coffee production package index. This index includes various production technologies, such as the use of improved coffee varieties, mulching, site cultivation, windbreaks, stumping and pruning practices, shade tree management, compost application, intercropping, optimal spacing, weed control, and moisture content management. The CSA coffee farming practices index is calculated using the formula: $$\:Inde{x}_{i}=\frac{\sum\:_{i=1}^{n}{x}_{i}}{n}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where Index denotes the CSA coffee farming practices index for the i th farmer, x i ​ represents each specific practice, and n is the total number of practices. Given that the index is a fractional value ranging from zero to one, a double-censored regression model, or two-limit Tobit model, was utilized. This model is particularly suited for scenarios where the dependent variable is censored, allowing for the use of data at both limits to estimate the latent variable y ∗ . The model is expressed as: $$\:{y}^{*}={x}_{i}{\beta\:}_{i}+{ϵ}_{i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 2 In this equation, y ∗ represents the unobserved index, X i is a vector of explanatory variables (), β i is a vector of unknown parameters, and ϵ i is the error term. The two-limit Tobit model defines the observed dependent variable y i for the i th smallholder farmer as follows: $$\:{y}_{i}=\left\{\begin{array}{c}0\:\:\:\:if{y}_{i}^{*}\le\:0\\\:{y}^{*}\:\:if\:01\end{array}\right.<1\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ where 0 is the lower-censoring limit and 1 is the upper-censoring limit. The two-limit Tobit model assumes that the error term is normally distributed; \(\:\in\: \sim N\left(0,{\delta\:}^{2}I\right).\) 3. Results and Discussions 4.1. Trends in smallholder coffee area harvested, production, and yields The findings reveal that coffee production in the Sidama region is characterized by significant spatial coverage but also notable fluctuations in harvested area, production volume, and yield over time (Fig. 3 &4). The region cultivates approximately 165,253 hectares of coffee, of which 139,028 hectares are productive, yielding a total production of 1,315,205 quintals with an average productivity of 9.46 quintals per hectare. Despite the wide cultivation area, the trend analysis between 2014 and 2020 indicates marked variability. The area under coffee cultivation peaked in 2015 at 197,217.69 hectares, yet the harvested area remained relatively low, averaging 35,681 hectares and reaching its minimum (33,593 hectares) in 2019. Total production followed a similar pattern, peaking at 456,828 quintals in 2015 before declining to 204,829 quintals by 2020. Over the six-year period, average production was 349,550 quintals, with yield per hectare declining from 11 quintals in 2014 to 8.6 quintals in 2024. These findings suggest that expanding coffee area does not necessarily translate into increased production or improved productivity. Instead, they point to biophysical, climatic, and agronomic constraints including erratic rainfall, temperature rise, pest infestations, and soil degradation as contributing factors to declining performance. This trend is consistent with national data from FAOSTAT (2021), which show increasing area under coffee cultivation across Ethiopia without corresponding improvements in yield. National yield peaked at 0.92 tons per hectare in 2006 but has remained volatile, with a low of 0.54 tons in 2003 and a recovery to 0.68 tons in 2020. 3.2. Trends and variability in rainfall and temperature in coffee-based agroforestry systems An analysis of long-term climate data from key coffee-growing districts in the Sidama region; Aleta Wondo, Shebedino, Bensa, and Dale shows that rainfall and temperature patterns have remained relatively stable over the past 30 years. The mean annual rainfall in these districts ranges from 1,185.59 mm in Shebedino to 1,294.14 mm in Aleta Wondo (Table 1 ). The coefficients of variation for rainfall between 8.61% and 10.59% suggest that year-to-year fluctuations have been relatively low, indicating a stable rainfall regime in the area. Temperature patterns display a similar trend of stability. Mean annual maximum temperatures range from 24.22°C in Aleta Wondo to 26.46°C in Shebedino, with a CV of just 0.02%. Likewise, mean annual minimum temperatures fall between 9.50°C in Bensa and 11.29°C in Shebedino, with similarly low variation (coefficients of variations between 0.04% and 0.05%). This consistent temperature profile further reinforces the impression of a relatively stable climate in Sidama’s coffee-producing zones. One possible explanation for this microclimatic stability is the increased use of climate-smart agricultural practices by smallholder farmers. Techniques such as shade management, mulching, intercropping, and improved soil moisture conservation appear to be enhancing the landscape’s capacity to buffer climatic extremes. These practices help farmers not only adapt to long-term climate trends but also reduce vulnerability to short-term weather fluctuations, thereby contributing to a more resilient coffee agroforestry system. The rainfall variability observed in Sidama is consistent with data from other coffee-producing regions in Ethiopia. For example, in the Alwero watershed, average annual rainfall is reported at 1,686.2 mm with a coefficients of variation of 8.9%, according to the National Meteorological Agency (NMA). The NMA classifies a coefficient of variation below 20% as indicative of low climate variability, reinforcing the idea that smallholder farmers in Sidama enjoys relatively stable weather conditions. However, while rainfall and temperature remain stable, their absolute values fall slightly below the optimal thresholds for Arabica coffee cultivation, which requires annual rainfall of 1,600–2,000 mm and a temperature range of 15–25°C. This means that despite favorable trends, current climatic conditions may still present challenges to long-term coffee productivity and sustainability. Continued investment in adaptive strategies will be essential to mitigate these risks and secure the livelihoods of smallholder coffee farmers in the face of changing climate dynamics. The finding of relatively stable rainfall and temperature patterns in Sidama’s coffee-growing districts aligns with several related studies assessing climate variability and adaptation in the region. For instance, Yona et al ( 2024 ) confirm modest fluctuations in rainfall and temperature over recent decades, emphasizing a generally stable microclimate conducive to coffee production. Similarly, Kaske ( 2023 ) highlights that effective dissemination of climate information and adoption of climate-smart practices such as shade management and soil conservation have enhanced farmers’ ability to buffer against climatic extremes. This reinforces the observed microclimatic stability in Sidama and underscores the role of adaptive management in mitigating climate risks. International Growth Centre (IGC), 2024 ) and Olana Jawo et al. ( 2023 ) further report that smallholder farmers’ perceptions of climate variability in Sidama correspond with gradual climatic trends, while their ongoing adoption of diverse adaptation strategies supports resilience in coffee agroforestry systems. However, Girma ( 2023 ) caution that despite these positive developments, absolute levels of rainfall and temperature sometimes fall below the ideal thresholds necessary for optimal Arabica coffee growth, presenting a persistent challenge to long-term sustainability. Table 1 Mean annual rainfall and temperature with coefficients of variation in major coffee-growing districts of Sidama Region (1993–2023). Study Areas Mean Annual Rainfall (in mm) SD Coefficients of Variation (%) Aleta Wondo 1294.14 137.07 10.59 Shebedino 1185.59 116.63 9.84 Bensa 1277.32 109.97 8.61 Dale 1239.24 114.10 9.21 Study Areas Mean Annual Max Temperature (°C) SD Coefficients of Variation (%) Aleta Wondo 24.22 0.53 0.02 Shebedino 26.46 0.54 0.02 Dale 26.15 0.53 0.02 Bensa 24.24 0.54 0.02 Study Areas Mean Annual Min Temperature (°C) SD Coefficients of Variation (%) Aleta Wondo 9.59 0.53 0.05 Shebedino 11.29 0.54 0.04 Dale 10.92 0.53 0.04 Bensa 9.50 0.54 0.05 3.3. Probability of risk occurrence and severity of effects on coffee production The results indicate that unseasonal rainfall poses the highest perceived severity of impact (mean = 3.27) and also has a high probability of occurrence (mean = 3.23), suggesting it is a critical risk factor for coffee farming (Table 2 ). Change in temperature (mean probability = 3.20) and erratic rainfall (mean = 3.01) are also rated with medium-to-high likelihood and moderate severity, indicating they are likely to affect coffee production within the next decade if unmitigated. Conversely, drought and, pests and diseases are perceived to have lower probabilities (means = 2.49 and 2.35, respectively) and relatively lower severity, although their long-term cumulative effects may still be significant, especially under worsening climate conditions. Table 2 Probability of risk occurrence and severity of effects on coffee production Potential Risks Probability of occurrence Severity of effects Mean SD Mean SD Change in temperature 3.20 0.96 2.83 1.04 Erratic rainfall 3.01 2.67 2.89 1.05 Unseasonal rain 3.23 1.06 3.27 1.19 Floods 2.81 1.32 3.09 1.19 Drought 2.49 1.30 2.49 1.16 Pests and diseases 2.35 1.24 2.42 1.22 Note: The ratings for probability of occurrence and severity of effects were measured using a 5-point Likert scale, where: 1 = Very Low, 2 = Low, 3 = Medium, 4 = High, and 5 = Very High. Interpretation of likelihood: • 5 (very high): risk likely to occur within 2 years • 4 (high): within 5 years • 3 (medium): within 10 years • 2 (low): within 20 years • 1 (very low): within 40 years The findings reveal that climate variability exerts a measurable impact on the average annual coffee production of smallholder farmers in the Sidama region. As indicated in Table 3 , during favorable climatic conditions, farmers achieve an average annual production of 13.27 quintals per household. However, under adverse climatic conditions characterized by irregular rainfall, temperature shifts, and extreme weather events, average production drops to 12.62 quintals, marking a 4.90% reduction. Although this percentage may appear modest, even slight reductions in yield can significantly affect household income and food security, especially for smallholders with limited adaptive capacity. The result reinforces the importance of promoting climate-resilient farming practices, such as agroforestry diversification, soil and water conservation, and early warning systems, to sustain coffee yields under increasing climate stress. Table 3 Effects of climate conditions on average annual coffee production by smallholder farmers Climate Condition Average annual Production (quintals) Reduction in production (%) Favorable Season 13.27 – Adverse Season 12.62 0.049 4.4. Adoption status and variability of climate-smart coffee farming practices among smallholder farmers The study revealed that smallholder coffee farmers in Sidama have adopted a wide range of climate-smart farming (CSF) practices, though with varying intensity. Among the 12 practices assessed, the most widely adopted were weed control (89.8%), intercropping (89.1%), shade tree management (83.8%), stumping (82.8%), and the use of improved coffee varieties (79.2%). These reflect strong farmer efforts to enhance productivity, buffer climate stress, and improve soil and plant health. Conversely, adoption was notably lower for site cultivation (26.1%), soil moisture management (38.8%), and pruning (51.4%), indicating persistent gaps in technical knowledge, resource availability, and support services. The overall Climate-Smart Farming Practices Index was relatively high, with a mean score of 0.71 (SD = 0.14), and a range from 0.33 to 1.00. This suggests that, on average, farmers implemented 71% of the recommended CSF practices, but also points to significant variation across households likely influenced by factors such as access to extension services, labor and input availability, landholding size, and perceived vulnerability to climate change. Table 4 Adoption status and variability of climate-smart coffee farming practices among smallholder farmers in the study area (N = 360) Climate-smart farming practices Adoption status (%) Adoption level Use of improved coffee varieties 79.2 high adoption Site cultivation 26.1 low adoption Establishment of windbreaks/shelterbelts 55.8 moderate adoption Stumping 82.8 high adoption Shade tree management 83.8 high adoption Organic compost application 73.3 moderate adoption Pruning practices 51.4 moderate adoption Intercropping coffee 89.1 high adoption Spacing (planting density) 73.9 moderate adoption Soil moisture content management 38.8 low adoption Planting site mulching 77.6 moderate adoption Weed control practices 89.8 high adoption Overall climate-smart farming practices index, mean = 0.71, SD = 0.14, minimum = 0.33, maximum = 1.00 4.5. Determinants of climate-smart coffee farming practices adoption The Tobit model results reveal several significant factors influencing the adoption of climate-smart coffee farming practices among smallholder farmers in Sidama. The model demonstrates a strong explanatory power, with a pseudo R² of 92.3%, indicating that the included variables effectively explain variation in adoption levels. Sex of the household head positively and significantly influences adoption (ß = 0.129, p < 0.05), with male-headed households more likely to adopt climate-smart practices than female-headed ones. This suggests that gender disparities in access to resources, information, and decision-making power continue to shape adoption patterns. Education level is strongly and positively associated with adoption (ß = 0.041, p < 0.01), implying that each additional year of schooling increases the likelihood of adoption. Educated farmers may better understand the benefits of climate-smart techniques and have greater capacity to implement them. Labor availability shows a positive, though marginally significant effect (ß = 0.013, p < 0.10), indicating that households with more labor resources are somewhat more capable of adopting these practices, likely due to the labor-intensive nature of some climate-smart interventions. Landholding size (ß = 0.021, p < 0.10) and coffee farming experience (ß = 0.003, p < 0.10) both have marginally positive effects on adoption. Larger landowners and more experienced farmers are slightly more inclined to adopt climate-smart methods, possibly due to better resource availability and accumulated knowledge. Membership in farmers’ cooperatives is a strong positive predictor (ß = 0.071, p < 0.01), underscoring the importance of social networks and collective action in facilitating access to information, inputs, and markets, which promote adoption. Access to extension services significantly increases adoption likelihood (ß = 0.081, p < 0.01), reflecting the critical role of agricultural advisory and support services in disseminating knowledge and encouraging improved practices. Interestingly, tropical livestock unit (TLU) has a significant negative effect (ß = -0.005, p < 0.05), suggesting that greater livestock ownership may reduce the likelihood of adopting climate-smart coffee farming practices, possibly due to resource competition or competing priorities. Other factors, including age, distance to market, access to credit, social networks (beyond cooperative membership), and risk perception levels, were not statistically significant in this model. The determinants of climate-smart coffee farming practices adoption in Sidama are supported by findings from recent studies in Ethiopia. A study by Diro et al ( 2022 ) in the coffee-based farming systems of Ethiopia reported that education, access to extension services, and membership in farmers' cooperatives significantly influence the adoption of various climate-smart agricultural technologies including manure application, conservation tillage, and intercropping. These findings resonate with the current Tobit model results indicating the importance of education, extension access, and cooperative membership in adoption decisions. Similarly, Kassie (2025) highlight the critical role of social capital and institutional support in promoting climate-smart agricultural practices among smallholder coffee farmers in Ethiopia's southern regions, reinforcing the relevance of cooperative networks and extension services showcased in Sidama. The marginal effects of landholding size and labor availability align with Fentie and Beyene ( 2024 ) who emphasize resource endowments as facilitators for adoption of labor-intensive climate-smart practices. The negative impact of livestock ownership also follows the patterns identified by Jahanger, et al ( 2022 ), stressing competing livelihood priorities that may constrain investment in climate-smart coffee practices. Collectively, these studies illustrate the complex socioeconomic and institutional context shaping climate-smart practice adoption among Ethiopian coffee farmers, underscoring the need for gender-sensitive and resource-aware interventions to enhance adaptive capacity. Table 5 Maximum Likelihood Estimates of the Two-Limit Tobit Model for climate-smart coffee farming practices adoption Explanatory variables Coefficient (ß) Standard Error (S.E.) t-ratio Age 0.0001 0.0015 0.958 Sex of household head 0.129** 0.065 1.99 Education level 0.041*** 0.010 3.88 Labor availability 0.013* 0.007 1.94 Farmland owned in hectare 0.021* 0.011 1.87 Coffee farming experiences 0.003* 0.001 1.89 Membership in framer’s cooperative 0.071*** 0.025 2.91 Distance to common market 0.001 0.000 1.08 Access to credit 0.005 0.026 0.19 Access to extension services 0.081*** 0.032 2.56 Tropical livestock unit (TLU) -0.005** 0.002 -2.00 Social network 0.0707 0.0162 0.726 Level of risk perceptions 0.0107 0.0158 0.500 Constant 0.469*** 0. 088 5.32 Sigma 0.188** 0.007 *** , ** , * represents 1%, 5% and 10% level of significance; Log likelihood = 45.53; Number of obs. = 360; P = 0.00; Pseudo R 2 = 92.3; 3 = left-censored observations at index ≤ 0.17; 325 = uncensored observations; 32 = right-censored observations at index ≥ 1. 4. Conclusions and Policy Implications 4.1. Conclusions This study sheds light on the challenges and opportunities facing smallholder coffee farmers in the Sidama region. Although a large area is dedicated to coffee cultivation, production and yields have fluctuated significantly between 2014 and 2020. Even when coffee farms expanded, the expected increase in production did not always follow. Factors such as unpredictable rainfall, rising temperatures, pests, and soil problems have made it harder for farmers to maintain steady coffee yields, reflecting similar trends seen across Ethiopia. Looking at climate data, rainfall and temperatures have been relatively stable over the past 30 years in key coffee-growing areas. Still, the conditions are slightly below what Arabica coffee ideally needs to thrive. It appears that farmers’ use of climate-smart practices like managing shade trees, mulching, and intercropping helps protect their crops from extreme weather and supports a more resilient farming system. Farmers identified unseasonal rains as the biggest risk to their coffee production, with temperature changes and erratic rainfall also seen as serious threats. The study confirmed that during tough seasons, coffee yields drop noticeably, showing how vulnerable smallholder farmers are to these changing climate patterns. On a positive note, many farmers have embraced important climate-smart farming techniques, especially weed control, intercropping, managing shade trees, stumping, and planting improved coffee varieties. However, some practices like site cultivation, managing soil moisture, and pruning are less widely adopted, highlighting areas where farmers need more support, training, and resources. The analysis also showed that several factors influence whether farmers adopt these climate-smart practices. Male-headed households, better education, more available labor, larger farms, experience with coffee farming, membership in cooperatives, and access to extension services all increase the likelihood of adoption. Interestingly, owning more livestock seemed to reduce the chances of adopting these practices, possibly because livestock compete for time and resources. 4.2. Policy Implications The findings of this study highlight several critical areas where policy interventions can strengthen the resilience and productivity of smallholder coffee farmers in Ethiopia. First, there is a clear need to enhance access to education and agricultural extension services, as these significantly influence the adoption of climate-smart farming practices. Policies that invest in farmer training, knowledge dissemination, and technical support can empower farmers to implement effective adaptation strategies. Second, supporting and expanding farmer cooperatives should be a priority, given their strong positive role in facilitating access to inputs, credit, and markets. Strengthening cooperative structures will improve farmers’ collective bargaining power and enable better resource sharing and innovation diffusion. Third, addressing gender disparities is essential. Male-headed households were more likely to adopt climate-smart practices, indicating that female farmers face barriers in accessing resources and information. Gender-sensitive policies and programs that provide targeted support for women farmers will help bridge this gap and ensure more inclusive agricultural development. Fourth, policy efforts should encourage diversified livelihood strategies that balance coffee farming with livestock management, as livestock ownership appeared to reduce adoption of climate-smart practices. Integrated approaches that optimize both crop and livestock production can enhance overall farm resilience. Lastly, given the vulnerability of coffee production to climatic risks such as unseasonal rainfall and temperature fluctuations, there is an urgent need for investment in climate risk management tools. This includes developing early warning systems, promoting climate-resilient crop varieties, and incentivizing sustainable land and water management practices. Declarations Funding This study was funded by Hawassa University through the Third Round HU Thematic Research Fund (2018–2019 Academic Year) under the project titled "Building Climate Change Resilience through Co-evolution of Innovations to Adapt to Changes in the Coffee Agroforestry Systems of Sidama Zone, Southern Ethiopia." Conflicts of Interest/Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics Approval Ethical clearance for this study was obtained from the Institutional Review Board (IRB) of Hawassa University prior to data collection. All methods were performed in accordance with the relevant guidelines and regulations of the Institutional Review Board of Hawassa University. Consent to Participate Declaration Informed consent was obtained from all participants prior to their inclusion in the study. This study did not involve participants under the age of 16. Consent to Publish Declaration Not applicable. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors’ Contributions Teshome Kassahun conceptualized the study, led data collection and analysis, and drafted the manuscript. Girma Gezimu contributed to methodology design, literature review, and interpretation of results. Deribe Kaske supported statistical analysis and critical revisions. Aneteneh Ashebir assisted in fieldwork coordination, data cleaning, and final editing. All authors read and approved the final manuscript. Acknowledgments We extend our heartfelt gratitude to Hawassa University for funding this study through the Third Round HU Thematic Research Fund (2018–2019 Academic Year) for the project "Building Climate Change Resilience through Co-evolution of Innovations to Adapt to Changes in the Coffee Agroforestry Systems of Sidama Zone, Southern Ethiopia." We appreciate the university's commitment to research in climate change and agroforestry, which has allowed us to contribute meaningfully to the resilience of local coffee system References Diro S, Tesfaye A, Erko B. Determinants of adoption of climate-smart agricultural technologies and practices in the coffee-based farming system of Ethiopia. Agric Food Secur. 2022;11(1). https://doi.org/10.1186/s40066-022-00385-2 . Fentie A, Beyene AD. (2024) ‘Climate-smart agricultural practices and welfare of rural smallholders in Ethiopia: Does planting method matter?’, Environment for Development. https://www.efdinitiative.org/sites/default/files/publications/efd_disc_eth_climate-smart_agricultural_practices_and_welfare.pdf Girma B. (2023). Climate Change and Coffee Quality: Challenges and Strategies for a Sustainable Future. Deleted Journal. https://doi.org/10.11648/j.abb.20231102.12 International Coffee Organization (ICO). (2024) Global Coffee Market Reports . https://ico.org/. International Growth Centre (IGC). (2024). Adapting Ethiopia’s coffee sector to climate change through value addition. [online] Available at: https://www.theigc.org/blogs/climate-priorities-developing-countries/adapting-ethiopias-coffee-sector-climate-change Jahanger A, Usman M, Balsalobre-Lorente D. Linking institutional quality to environmental sustainability. Sustain Dev. 2022;30(6). https://doi.org/10.1002/sd.2345 . Kaske KD. (2023) ‘Climate information: Does dissemination channels matter? Analysis of coffee agroforestry system in the Sidama Region, Ethiopia’, Cogent Social Sciences, 9(1). Available at: https://www.tandfonline.com/doi/full/ 10.1080/23311932.2023.2292372 National Meteorological Agency (NMA), Ethiopia. (2024) Monthly Agrometeorological Bulletin. https://www.ethiomet.gov.et/documents/197/MONTH__OF_AUGUST_2024_AGROMETEOROLOGICAL_BULLETIN_Final.pdf Nigussie Z. (2025) Impact of climate variability on coffee yield and production in Ethiopia. Available at: http://ir.haramaya.edu.et/hru/bitstream/handle/123456789/6424/Zinash%20Nigussie.pdf Olana Jawo T, Teutscherová N, Negash M, Sahle K, Lojka B. Smallholder coffee-based farmers’ perception and their adaptation strategies of climate change and variability in South-Eastern Ethiopia. International Journal of Sustainable Development & World Ecology; 2023. pp. 1–15. https://doi.org/10.1080/13504509.2023.2167241 . Perfect Daily Grind. (2020) Everything You Need to Know About Coffee From Sidama, Ethiopia. Available at: https://perfectdailygrind.com/2020/06/everything-you-need-to-know-about-coffee-from-sidama-ethiopia/ Sidama Coffee Farmers’ Cooperative Union (SCFCU). (2025) About Us . https://coopcoffees.coop/sidama-union/ United States Agency for International Development (USAID). (2025) Coffee Sector Development Projects in Ethiopia . https://www.usaid.gov/ethiopia/coffee-sector World Coffee Research. (2024) Coffee and Climate Change: Addressing Vulnerabilities in Arabica Production. Available at: https://worldcoffeeresearch.org/news/2025/2023-annual-report Yona Y, Matewos T, Sime G. (2024). Analysis of rainfall and temperature variabilities in Sidama regional state, Ethiopia. Heliyon, 10(7), p.e28184. https://doi.org/10.1016/j.heliyon . 2024.e28184. Zinash N. (2023) Coffee production and climate change in Ethiopia. Available at: https://www.sciencedirect.com/science/article/pii/S2666154325000663 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 28 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Kassahun¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYHACNiBOYGBgbwDSBhaE1fPAtfAcAGmRIEWLRAKIT4QWe/b2Zw8+MKTJy898fnXDjwIJBv727gT8tvCcMTecwZBjuOF2TtnNHqDDJM6c3YBfi0QOmzQPQwXjBumctBs8QC0GErkEtMg/fyb9h6HCfv7MM2k3/xClRYLBTJqBISex4Qb7sdvE2XImx0yyxyAtecOZHLbbMgYSPAT9wt5+/JnEj4pk2/lAxs03f2zk+Nt78WuBAAOwhRCSCOUICx+QonoUjIJRMApGEAAArRlCTVZxZWkAAAAASUVORK5CYII=","orcid":"","institution":"Hawassa University","correspondingAuthor":true,"prefix":"","firstName":"Teshome","middleName":"","lastName":"Kassahun¹","suffix":""},{"id":516606734,"identity":"5803155d-85be-41b2-bf68-64adf5d41aaa","order_by":1,"name":"Gezimu Girma","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Gezimu","middleName":"","lastName":"Girma","suffix":""},{"id":516606735,"identity":"f8d09825-3678-4e66-84b7-45fda3ab1034","order_by":2,"name":"Deribe 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14:32:29","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78826,"visible":true,"origin":"","legend":"","description":"","filename":"3d9b772787b940c0ac4ff2d8664d86461structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/f814f612912947a5ce34f1e3.xml"},{"id":91722201,"identity":"15243e9f-2afd-45ba-a68f-8d487e67b411","added_by":"auto","created_at":"2025-09-19 14:24:29","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85755,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/d855f053bd4703f565d6f173.html"},{"id":91722191,"identity":"e0a941a8-6879-4bfe-83dd-417c7b3c2d12","added_by":"auto","created_at":"2025-09-19 14:24:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96211,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study areas related to national locations\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/d7cbc870ec2555937e1e32c1.jpeg"},{"id":91722196,"identity":"78669aae-75e7-4435-aadd-30af0545b516","added_by":"auto","created_at":"2025-09-19 14:24:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":535425,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of coffee production and productivity in study area\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/afda1c8e84969db9b7ad3bd3.jpeg"},{"id":91722194,"identity":"e28d806f-0b82-4edd-9bfb-d24f5e543f9b","added_by":"auto","created_at":"2025-09-19 14:24:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504018,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of coffee production and productivity in Ethiopia\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/ee03c23127ae505a70db02aa.jpeg"},{"id":91723663,"identity":"b4f20c49-a293-4723-97a8-a2aabbb95df6","added_by":"auto","created_at":"2025-09-19 14:40:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2090901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7297913/v1/dbaf39c4-a14c-479d-b2f6-d2d937ffc0ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coffee Production Trends and Adoption of Climate-Smart Agroforestry Practices Among Smallholder Farmers in Sidama Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEthiopia, the birthplace of Coffea arabica, relies heavily on coffee as a cornerstone of its economy and rural livelihoods. Coffee contributes approximately 30\u0026ndash;35% of the country\u0026rsquo;s foreign exchange earnings and supports the livelihoods of over five million smallholder farmers, influencing the well-being of more than 15\u0026nbsp;million people along its value chain (USDA, 2025). Most Ethiopian coffee is cultivated under traditional agroforestry systems, where coffee is intercropped with food crops and shade trees (SCFCU, 2025). These systems not only support coffee production but also enhance ecosystem services such as biodiversity conservation, soil fertility, and water regulation (ICO, 2024).\u003c/p\u003e\u003cp\u003eAmong Ethiopia\u0026rsquo;s coffee-producing regions, the Sidama National Regional State is one of the most prominent, producing a significant portion of the country\u0026rsquo;s high-quality Arabica coffee. Sidama\u0026rsquo;s unique microclimates, fertile soils, and altitudinal range between 1,500 and 2,200 meters above sea level create ideal growing conditions, enabling the slow ripening of coffee cherries and contributing to the region\u0026rsquo;s international reputation for flavor-rich specialty coffee (Perfect Daily Grind, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coffee in Sidama is primarily grown by smallholder farmers operating on less than five hectares of land, often using semi-traditional methods and supported by cooperative networks such as the Sidama Coffee Farmers\u0026rsquo; Cooperative Union (SCFCU) (SCFCU, 2024).\u003c/p\u003e\u003cp\u003eHowever, climate variability and change pose serious threats to the sustainability of coffee production in the region. Increasing temperatures, erratic rainfall patterns, and prolonged dry spells are disrupting flowering, fruit development, and yields, while also intensifying the incidence of pests and diseases. Recent data indicate a decline in coffee yields across Ethiopia from 0.92 to 0.68 tons per hectare over the past two decades, reflecting a broader vulnerability within the Arabica coffee sector, which is highly sensitive to temperature fluctuations above 23\u0026deg;C and irregular rainfall (Nigussie, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zinash, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These climatic stressors threaten the stability of rural livelihoods and the economic viability of Ethiopia\u0026rsquo;s coffee sector (World Coffee Research, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To mitigate these risks, climate-smart agriculture (CSA) has emerged as a critical strategy. CSA promotes practices that simultaneously enhance productivity, build resilience, and reduce greenhouse gas emissions. In the context of coffee agroforestry, CSA includes interventions such as the use of improved coffee varieties, shade tree management, intercropping, soil and water conservation, mulching, and integrated pest management. These practices can buffer the adverse impacts of climate variability, improve ecological resilience, and help sustain coffee yields. However, despite their potential, the adoption of CSA practices remains uneven.\u003c/p\u003e\u003cp\u003eIn Sidama, the effects of climate change are already visible, with coffee productivity declining from 1.05 to 0.95 tons per hectare over the past eight years. Yet, CSA adoption remains modest among smallholder farmers, who often lack the resources, knowledge, and institutional support necessary for widespread implementation. Understanding how farmers perceive climate risks, the factors influencing their decision-making, and the extent to which CSA is being adopted is critical for designing effective adaptation strategies and policy interventions. This study aims to examine the interlinked dynamics of climate variability, coffee production, and the adoption of climate-smart practices among smallholder farmers in the Sidama Region. Specifically, the study seeks to: (1) analyze trends in the area harvested, production, and yield of smallholder coffee; (2) examine trends and variability in rainfall and temperature within coffee-based agroforestry systems; (3) assess the probability and severity of climate-related risks affecting coffee production; (4) investigate the adoption status and variability of CSA practices; and (5) identify the key socio-economic and institutional determinants influencing CSA adoption. The findings are expected to inform evidence-based strategies for enhancing the resilience and sustainability of coffee production in climate-vulnerable regions of Ethiopia and beyond.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Study area\u003c/h2\u003e\u003cp\u003eThis study was conducted in the Sidama National Regional State of Ethiopia. Agriculture, including both crop and livestock production, is the primary livelihood for rural households in the region. Sidama is renowned as one of Ethiopia's leading coffee-producing areas, significantly contributing both washed and unwashed coffee to both domestic and international markets. The region's production system is predominantly characterized by smallholder garden coffee cultivation.\u003c/p\u003e\u003cp\u003eSidama is composed of 28 coffee-growing districts, but this study specifically focuses on four districts: Shebedino, Dale, Aleta-Wondo, and Bensa. Geographically, the study area lies between latitudes 6\u0026deg;20'0''N and 7\u0026deg;0'0''N, and longitudes 38\u0026deg;20'0''E and 39\u0026deg;0'0''E. The altitude in the region ranges from 500 to 3,500 meters above sea level, with average temperatures between 15\u0026deg;C and 24.9\u0026deg;C. The average annual rainfall varies between 1,200 mm and 1,999 mm. The selected study districts are shown in Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Data collection methods\u003c/h2\u003e\u003cp\u003eThis study focuses on key aspects such as climate-smart coffee farming practices, the adoption and uptake of technology, trends in rainfall and temperature, and the determinants influencing technology uptake. The target population consists of smallholder farmers in coffee-producing areas, specifically smallholder garden coffee farmers in Sidama, Ethiopia. Primary data were collected from 360 coffee farming households through a semi-structured questionnaire. Additionally, secondary data were gathered from relevant scientific journals, annual reports from local offices, and rainfall and temperature data provided by the National Meteorology Agency of Ethiopia. A multistage sampling technique was used to select the study districts, kebeles (small administrative units), and households. Initially, four representative districts were purposively selected from the 28 coffee-growing districts in consultation with experts from the Sidama Region Coffee and Tea Authority. Next, 12 kebeles were selected from the 118 kebeles in the chosen districts, based on variations in agro-ecology and coffee production potential. Finally, 360 coffee farming households were randomly selected from the sampled kebeles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data analysis\u003c/h2\u003e\u003cp\u003eThe collected data were analyzed using both qualitative and quantitative methods, combining analytical and statistical tools. Qualitative data collected through interviews were analyzed using a narrative analysis approach. This method involved identifying key themes and patterns emerging from participant responses. Descriptive statistics were used to characterize the households and their coffee production, as well as to examine trends in rainfall and temperature. Graphs and coefficients of variation were employed to illustrate trends and the magnitude of changes in rainfall and temperature. A risk prioritization matrix was also utilized to assess the likelihood of risks arising from climate change. For the econometric analysis, a two-limit Tobit model was used to identify factors influencing the adoption of climate-smart agricultural (CSA) coffee farming practices. CSA coffee farming practices were treated as the dependent variable, measured through a sustainable coffee production package index. This index includes various production technologies, such as the use of improved coffee varieties, mulching, site cultivation, windbreaks, stumping and pruning practices, shade tree management, compost application, intercropping, optimal spacing, weed control, and moisture content management. The CSA coffee farming practices index is calculated using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Inde{x}_{i}=\\frac{\\sum\\:_{i=1}^{n}{x}_{i}}{n}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Index denotes the CSA coffee farming practices index for the i\u003csup\u003eth\u003c/sup\u003e farmer, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e​ represents each specific practice, and \u003cem\u003en\u003c/em\u003e is the total number of practices.\u003c/p\u003e\u003cp\u003eGiven that the index is a fractional value ranging from zero to one, a double-censored regression model, or two-limit Tobit model, was utilized. This model is particularly suited for scenarios where the dependent variable is censored, allowing for the use of data at both limits to estimate the latent variable \u003cem\u003ey\u003c/em\u003e\u003csup\u003e\u0026lowast;\u003c/sup\u003e. The model is expressed as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{y}^{*}={x}_{i}{\\beta\\:}_{i}+{ϵ}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this equation, \u003cem\u003ey\u003c/em\u003e\u003csup\u003e\u0026lowast;\u003c/sup\u003e represents the unobserved index, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is a vector of explanatory variables (), \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is a vector of unknown parameters, and \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the error term. The two-limit Tobit model defines the observed dependent variable \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e for the i\u003csup\u003eth\u003c/sup\u003e smallholder farmer as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}=\\left\\{\\begin{array}{c}0\\:\\:\\:\\:if{y}_{i}^{*}\\le\\:0\\\\\\:{y}^{*}\\:\\:if\\:0\u0026lt;{y}_{i}^{*}\\\\\\:1\\:\\:if{y}_{i}^{*}\u0026gt;1\\end{array}\\right.\u0026lt;1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere 0 is the lower-censoring limit and 1 is the upper-censoring limit. The two-limit Tobit model assumes that the error term is normally distributed; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\in\\: \\sim N\\left(0,{\\delta\\:}^{2}I\\right).\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Trends in smallholder coffee area harvested, production, and yields\u003c/h2\u003e\u003cp\u003eThe findings reveal that coffee production in the Sidama region is characterized by significant spatial coverage but also notable fluctuations in harvested area, production volume, and yield over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026amp;4). The region cultivates approximately 165,253 hectares of coffee, of which 139,028 hectares are productive, yielding a total production of 1,315,205 quintals with an average productivity of 9.46 quintals per hectare. Despite the wide cultivation area, the trend analysis between 2014 and 2020 indicates marked variability. The area under coffee cultivation peaked in 2015 at 197,217.69 hectares, yet the harvested area remained relatively low, averaging 35,681 hectares and reaching its minimum (33,593 hectares) in 2019. Total production followed a similar pattern, peaking at 456,828 quintals in 2015 before declining to 204,829 quintals by 2020. Over the six-year period, average production was 349,550 quintals, with yield per hectare declining from 11 quintals in 2014 to 8.6 quintals in 2024. These findings suggest that expanding coffee area does not necessarily translate into increased production or improved productivity. Instead, they point to biophysical, climatic, and agronomic constraints including erratic rainfall, temperature rise, pest infestations, and soil degradation as contributing factors to declining performance. This trend is consistent with national data from FAOSTAT (2021), which show increasing area under coffee cultivation across Ethiopia without corresponding improvements in yield. National yield peaked at 0.92 tons per hectare in 2006 but has remained volatile, with a low of 0.54 tons in 2003 and a recovery to 0.68 tons in 2020.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Trends and variability in rainfall and temperature in coffee-based agroforestry systems\u003c/h2\u003e\u003cp\u003eAn analysis of long-term climate data from key coffee-growing districts in the Sidama region; Aleta Wondo, Shebedino, Bensa, and Dale shows that rainfall and temperature patterns have remained relatively stable over the past 30 years. The mean annual rainfall in these districts ranges from 1,185.59 mm in Shebedino to 1,294.14 mm in Aleta Wondo (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The coefficients of variation for rainfall between 8.61% and 10.59% suggest that year-to-year fluctuations have been relatively low, indicating a stable rainfall regime in the area. Temperature patterns display a similar trend of stability. Mean annual maximum temperatures range from 24.22\u0026deg;C in Aleta Wondo to 26.46\u0026deg;C in Shebedino, with a CV of just 0.02%. Likewise, mean annual minimum temperatures fall between 9.50\u0026deg;C in Bensa and 11.29\u0026deg;C in Shebedino, with similarly low variation (coefficients of variations between 0.04% and 0.05%). This consistent temperature profile further reinforces the impression of a relatively stable climate in Sidama\u0026rsquo;s coffee-producing zones. One possible explanation for this microclimatic stability is the increased use of climate-smart agricultural practices by smallholder farmers. Techniques such as shade management, mulching, intercropping, and improved soil moisture conservation appear to be enhancing the landscape\u0026rsquo;s capacity to buffer climatic extremes. These practices help farmers not only adapt to long-term climate trends but also reduce vulnerability to short-term weather fluctuations, thereby contributing to a more resilient coffee agroforestry system. The rainfall variability observed in Sidama is consistent with data from other coffee-producing regions in Ethiopia. For example, in the Alwero watershed, average annual rainfall is reported at 1,686.2 mm with a coefficients of variation of 8.9%, according to the National Meteorological Agency (NMA). The NMA classifies a coefficient of variation below 20% as indicative of low climate variability, reinforcing the idea that smallholder farmers in Sidama enjoys relatively stable weather conditions. However, while rainfall and temperature remain stable, their absolute values fall slightly below the optimal thresholds for Arabica coffee cultivation, which requires annual rainfall of 1,600\u0026ndash;2,000 mm and a temperature range of 15\u0026ndash;25\u0026deg;C. This means that despite favorable trends, current climatic conditions may still present challenges to long-term coffee productivity and sustainability. Continued investment in adaptive strategies will be essential to mitigate these risks and secure the livelihoods of smallholder coffee farmers in the face of changing climate dynamics.\u003c/p\u003e\u003cp\u003eThe finding of relatively stable rainfall and temperature patterns in Sidama\u0026rsquo;s coffee-growing districts aligns with several related studies assessing climate variability and adaptation in the region. For instance, Yona et al (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirm modest fluctuations in rainfall and temperature over recent decades, emphasizing a generally stable microclimate conducive to coffee production. Similarly, Kaske (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlights that effective dissemination of climate information and adoption of climate-smart practices such as shade management and soil conservation have enhanced farmers\u0026rsquo; ability to buffer against climatic extremes. This reinforces the observed microclimatic stability in Sidama and underscores the role of adaptive management in mitigating climate risks. International Growth Centre (IGC), \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Olana Jawo et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further report that smallholder farmers\u0026rsquo; perceptions of climate variability in Sidama correspond with gradual climatic trends, while their ongoing adoption of diverse adaptation strategies supports resilience in coffee agroforestry systems. However, Girma (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) caution that despite these positive developments, absolute levels of rainfall and temperature sometimes fall below the ideal thresholds necessary for optimal Arabica coffee growth, presenting a persistent challenge to long-term sustainability.\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\u003eMean annual rainfall and temperature with coefficients of variation in major coffee-growing districts of Sidama Region (1993\u0026ndash;2023).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy Areas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Rainfall (in mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficients of Variation (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAleta Wondo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1294.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShebedino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1185.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBensa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1277.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1239.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Max Temperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficients of Variation (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAleta Wondo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShebedino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBensa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Min Temperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficients of Variation (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAleta Wondo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShebedino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBensa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Probability of risk occurrence and severity of effects on coffee production\u003c/h2\u003e\u003cp\u003eThe results indicate that unseasonal rainfall poses the highest perceived severity of impact (mean\u0026thinsp;=\u0026thinsp;3.27) and also has a high probability of occurrence (mean\u0026thinsp;=\u0026thinsp;3.23), suggesting it is a critical risk factor for coffee farming (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Change in temperature (mean probability\u0026thinsp;=\u0026thinsp;3.20) and erratic rainfall (mean\u0026thinsp;=\u0026thinsp;3.01) are also rated with medium-to-high likelihood and moderate severity, indicating they are likely to affect coffee production within the next decade if unmitigated. Conversely, drought and, pests and diseases are perceived to have lower probabilities (means\u0026thinsp;=\u0026thinsp;2.49 and 2.35, respectively) and relatively lower severity, although their long-term cumulative effects may still be significant, especially under worsening climate conditions.\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\u003eProbability of risk occurrence and severity of effects on coffee production\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePotential Risks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProbability of occurrence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSeverity of effects\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\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\" colname=\"c1\"\u003e\u003cp\u003eChange in temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErratic rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnseasonal rain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFloods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrought\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePests and diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: The ratings for probability of occurrence and severity of effects were measured using a 5-point Likert scale, where: 1\u0026thinsp;=\u0026thinsp;Very Low, 2\u0026thinsp;=\u0026thinsp;Low, 3\u0026thinsp;=\u0026thinsp;Medium, 4\u0026thinsp;=\u0026thinsp;High, and 5\u0026thinsp;=\u0026thinsp;Very High. Interpretation of likelihood:\u003c/p\u003e\u003cp\u003e\u0026bull; 5 (very high): risk likely to occur within 2 years\u003c/p\u003e\u003cp\u003e\u0026bull; 4 (high): within 5 years\u003c/p\u003e\u003cp\u003e\u0026bull; 3 (medium): within 10 years\u003c/p\u003e\u003cp\u003e\u0026bull; 2 (low): within 20 years\u003c/p\u003e\u003cp\u003e\u0026bull; 1 (very low): within 40 years\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\u003eThe findings reveal that climate variability exerts a measurable impact on the average annual coffee production of smallholder farmers in the Sidama region. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, during favorable climatic conditions, farmers achieve an average annual production of 13.27 quintals per household. However, under adverse climatic conditions characterized by irregular rainfall, temperature shifts, and extreme weather events, average production drops to 12.62 quintals, marking a 4.90% reduction. Although this percentage may appear modest, even slight reductions in yield can significantly affect household income and food security, especially for smallholders with limited adaptive capacity. The result reinforces the importance of promoting climate-resilient farming practices, such as agroforestry diversification, soil and water conservation, and early warning systems, to sustain coffee yields under increasing climate stress.\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\u003eEffects of climate conditions on average annual coffee production by smallholder farmers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimate Condition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage annual Production (quintals)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReduction in production (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFavorable Season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdverse Season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Adoption status and variability of climate-smart coffee farming practices among smallholder farmers\u003c/h2\u003e\u003cp\u003eThe study revealed that smallholder coffee farmers in Sidama have adopted a wide range of climate-smart farming (CSF) practices, though with varying intensity. Among the 12 practices assessed, the most widely adopted were weed control (89.8%), intercropping (89.1%), shade tree management (83.8%), stumping (82.8%), and the use of improved coffee varieties (79.2%). These reflect strong farmer efforts to enhance productivity, buffer climate stress, and improve soil and plant health. Conversely, adoption was notably lower for site cultivation (26.1%), soil moisture management (38.8%), and pruning (51.4%), indicating persistent gaps in technical knowledge, resource availability, and support services. The overall Climate-Smart Farming Practices Index was relatively high, with a mean score of 0.71 (SD\u0026thinsp;=\u0026thinsp;0.14), and a range from 0.33 to 1.00. This suggests that, on average, farmers implemented 71% of the recommended CSF practices, but also points to significant variation across households likely influenced by factors such as access to extension services, labor and input availability, landholding size, and perceived vulnerability to climate change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdoption status and variability of climate-smart coffee farming practices among smallholder farmers in the study area (N\u0026thinsp;=\u0026thinsp;360)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimate-smart farming practices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdoption status (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdoption level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of improved coffee varieties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSite cultivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003elow adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstablishment of windbreaks/shelterbelts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emoderate adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStumping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShade tree management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic compost application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emoderate adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePruning practices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emoderate adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercropping coffee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpacing (planting density)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emoderate adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil moisture content management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003elow adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanting site mulching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emoderate adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeed control practices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigh adoption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eOverall climate-smart farming practices index, mean\u0026thinsp;=\u0026thinsp;0.71, SD\u0026thinsp;=\u0026thinsp;0.14, minimum\u0026thinsp;=\u0026thinsp;0.33, maximum\u0026thinsp;=\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Determinants of climate-smart coffee farming practices adoption\u003c/h2\u003e\u003cp\u003eThe Tobit model results reveal several significant factors influencing the adoption of climate-smart coffee farming practices among smallholder farmers in Sidama. The model demonstrates a strong explanatory power, with a pseudo R\u0026sup2; of 92.3%, indicating that the included variables effectively explain variation in adoption levels. Sex of the household head positively and significantly influences adoption (\u0026szlig; = 0.129, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with male-headed households more likely to adopt climate-smart practices than female-headed ones. This suggests that gender disparities in access to resources, information, and decision-making power continue to shape adoption patterns. Education level is strongly and positively associated with adoption (\u0026szlig; = 0.041, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), implying that each additional year of schooling increases the likelihood of adoption. Educated farmers may better understand the benefits of climate-smart techniques and have greater capacity to implement them. Labor availability shows a positive, though marginally significant effect (\u0026szlig; = 0.013, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), indicating that households with more labor resources are somewhat more capable of adopting these practices, likely due to the labor-intensive nature of some climate-smart interventions. Landholding size (\u0026szlig; = 0.021, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) and coffee farming experience (\u0026szlig; = 0.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) both have marginally positive effects on adoption. Larger landowners and more experienced farmers are slightly more inclined to adopt climate-smart methods, possibly due to better resource availability and accumulated knowledge. Membership in farmers\u0026rsquo; cooperatives is a strong positive predictor (\u0026szlig; = 0.071, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), underscoring the importance of social networks and collective action in facilitating access to information, inputs, and markets, which promote adoption. Access to extension services significantly increases adoption likelihood (\u0026szlig; = 0.081, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reflecting the critical role of agricultural advisory and support services in disseminating knowledge and encouraging improved practices. Interestingly, tropical livestock unit (TLU) has a significant negative effect (\u0026szlig; = -0.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that greater livestock ownership may reduce the likelihood of adopting climate-smart coffee farming practices, possibly due to resource competition or competing priorities. Other factors, including age, distance to market, access to credit, social networks (beyond cooperative membership), and risk perception levels, were not statistically significant in this model.\u003c/p\u003e\u003cp\u003eThe determinants of climate-smart coffee farming practices adoption in Sidama are supported by findings from recent studies in Ethiopia. A study by Diro et al (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the coffee-based farming systems of Ethiopia reported that education, access to extension services, and membership in farmers' cooperatives significantly influence the adoption of various climate-smart agricultural technologies including manure application, conservation tillage, and intercropping. These findings resonate with the current Tobit model results indicating the importance of education, extension access, and cooperative membership in adoption decisions. Similarly, Kassie (2025) highlight the critical role of social capital and institutional support in promoting climate-smart agricultural practices among smallholder coffee farmers in Ethiopia's southern regions, reinforcing the relevance of cooperative networks and extension services showcased in Sidama. The marginal effects of landholding size and labor availability align with Fentie and Beyene (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who emphasize resource endowments as facilitators for adoption of labor-intensive climate-smart practices. The negative impact of livestock ownership also follows the patterns identified by Jahanger, et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), stressing competing livelihood priorities that may constrain investment in climate-smart coffee practices. Collectively, these studies illustrate the complex socioeconomic and institutional context shaping climate-smart practice adoption among Ethiopian coffee farmers, underscoring the need for gender-sensitive and resource-aware interventions to enhance adaptive capacity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaximum Likelihood Estimates of the Two-Limit Tobit Model for climate-smart coffee farming practices adoption\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (\u0026szlig;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error (S.E.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-ratio\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of household head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.129**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor availability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmland owned in hectare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.021*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoffee farming experiences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMembership in framer\u0026rsquo;s cooperative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to common market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to extension services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.081***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical livestock unit (TLU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial network\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of risk perceptions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.469***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0. 088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.188**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e***\u003c/sub\u003e, \u003csub\u003e**\u003c/sub\u003e, \u003csub\u003e*\u003c/sub\u003e represents 1%, 5% and 10% level of significance; Log likelihood\u0026thinsp;=\u0026thinsp;45.53; Number of obs. = 360; P\u0026thinsp;=\u0026thinsp;0.00; Pseudo R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;92.3; 3\u0026thinsp;=\u0026thinsp;left-censored observations at index\u0026thinsp;\u0026le;\u0026thinsp;0.17; 325\u0026thinsp;=\u0026thinsp;uncensored observations; 32\u0026thinsp;=\u0026thinsp;right-censored observations at index\u0026thinsp;\u0026ge;\u0026thinsp;1.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions and Policy Implications","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Conclusions\u003c/h2\u003e\u003cp\u003eThis study sheds light on the challenges and opportunities facing smallholder coffee farmers in the Sidama region. Although a large area is dedicated to coffee cultivation, production and yields have fluctuated significantly between 2014 and 2020. Even when coffee farms expanded, the expected increase in production did not always follow. Factors such as unpredictable rainfall, rising temperatures, pests, and soil problems have made it harder for farmers to maintain steady coffee yields, reflecting similar trends seen across Ethiopia. Looking at climate data, rainfall and temperatures have been relatively stable over the past 30 years in key coffee-growing areas. Still, the conditions are slightly below what Arabica coffee ideally needs to thrive. It appears that farmers\u0026rsquo; use of climate-smart practices like managing shade trees, mulching, and intercropping helps protect their crops from extreme weather and supports a more resilient farming system. Farmers identified unseasonal rains as the biggest risk to their coffee production, with temperature changes and erratic rainfall also seen as serious threats. The study confirmed that during tough seasons, coffee yields drop noticeably, showing how vulnerable smallholder farmers are to these changing climate patterns. On a positive note, many farmers have embraced important climate-smart farming techniques, especially weed control, intercropping, managing shade trees, stumping, and planting improved coffee varieties. However, some practices like site cultivation, managing soil moisture, and pruning are less widely adopted, highlighting areas where farmers need more support, training, and resources. The analysis also showed that several factors influence whether farmers adopt these climate-smart practices. Male-headed households, better education, more available labor, larger farms, experience with coffee farming, membership in cooperatives, and access to extension services all increase the likelihood of adoption. Interestingly, owning more livestock seemed to reduce the chances of adopting these practices, possibly because livestock compete for time and resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Policy Implications\u003c/h2\u003e\u003cp\u003eThe findings of this study highlight several critical areas where policy interventions can strengthen the resilience and productivity of smallholder coffee farmers in Ethiopia. First, there is a clear need to enhance access to education and agricultural extension services, as these significantly influence the adoption of climate-smart farming practices. Policies that invest in farmer training, knowledge dissemination, and technical support can empower farmers to implement effective adaptation strategies. Second, supporting and expanding farmer cooperatives should be a priority, given their strong positive role in facilitating access to inputs, credit, and markets. Strengthening cooperative structures will improve farmers\u0026rsquo; collective bargaining power and enable better resource sharing and innovation diffusion. Third, addressing gender disparities is essential. Male-headed households were more likely to adopt climate-smart practices, indicating that female farmers face barriers in accessing resources and information. Gender-sensitive policies and programs that provide targeted support for women farmers will help bridge this gap and ensure more inclusive agricultural development. Fourth, policy efforts should encourage diversified livelihood strategies that balance coffee farming with livestock management, as livestock ownership appeared to reduce adoption of climate-smart practices. Integrated approaches that optimize both crop and livestock production can enhance overall farm resilience. Lastly, given the vulnerability of coffee production to climatic risks such as unseasonal rainfall and temperature fluctuations, there is an urgent need for investment in climate risk management tools. This includes developing early warning systems, promoting climate-resilient crop varieties, and incentivizing sustainable land and water management practices.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003eThis study was funded by Hawassa University through the Third Round HU Thematic Research Fund (2018–2019 Academic Year) under the project titled \u003cem\u003e\"Building Climate Change Resilience through Co-evolution of Innovations to Adapt to Changes in the Coffee Agroforestry Systems of Sidama Zone, Southern Ethiopia.\"\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest/Competing Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ethical clearance for this study was obtained from the Institutional Review Board (IRB) of Hawassa University prior to data collection. All methods were performed in accordance with the relevant guidelines and regulations of the Institutional Review Board of Hawassa University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Informed consent was obtained from all participants prior to their inclusion in the study. This study did not involve participants under the age of 16.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp; \u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeshome Kassahun conceptualized the study, led data collection and analysis, and drafted the manuscript. Girma Gezimu contributed to methodology design, literature review, and interpretation of results. Deribe Kaske supported statistical analysis and critical revisions. Aneteneh Ashebir assisted in fieldwork coordination, data cleaning, and final editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cbr\u003eWe extend our heartfelt gratitude to Hawassa University for funding this study through the Third Round HU Thematic Research Fund (2018–2019 Academic Year) for the project \u003cem\u003e\"Building Climate Change Resilience through Co-evolution of Innovations to Adapt to Changes in the Coffee Agroforestry Systems of Sidama Zone, Southern Ethiopia.\"\u003c/em\u003e We appreciate the university's commitment to research in climate change and agroforestry, which has allowed us to contribute meaningfully to the resilience of local coffee system\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiro S, Tesfaye A, Erko B. Determinants of adoption of climate-smart agricultural technologies and practices in the coffee-based farming system of Ethiopia. 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Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S2666154325000663\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S2666154325000663\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate variability, Coffee production, Climate-smart farming, Smallholder farmers, Agroforestry systems","lastPublishedDoi":"10.21203/rs.3.rs-7297913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7297913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the effects of climate variability on coffee production and the adoption of climate-smart farming (CSF) practices among smallholder farmers in Sidama, Ethiopia. Using a mixed-methods design, data were collected from 360 randomly selected coffee farmers across four districts, complemented by long-term climate data and secondary sources. Descriptive statistics, trend analysis, and a two-limit Tobit model were employed to examine production trends, climate variability, and CSF adoption drivers. Coffee yields declined from 11 quintals/ha (2014) to 8.6 quintals/ha (2024), and total production fell from 456,828 quintals (2015) to 204,829 quintals (2020), despite the expansion of cultivated land. Climate data show moderately stable rainfall (1,185\u0026ndash;1,294 mm) and temperatures (24.2\u0026ndash;26.5\u0026deg;C), but still suboptimal for Arabica coffee. Farmers report unseasonal rains, erratic patterns, and temperature shifts as key threats. Under adverse weather, average household coffee yield dropped by 4.9%. Adoption of CSF practices is moderately high (index\u0026thinsp;=\u0026thinsp;0.71), especially for weed control (89.8%), intercropping (89.1%), and shade management (83.8%), but lower for site-specific planting (26.1%) and soil moisture management (38.8%). The Tobit model (pseudo R\u0026sup2; = 0.923) shows adoption is positively influenced by male headship, education, extension access, and cooperative membership, while livestock ownership has a slight negative effect. These findings call for improved advisory services, farmer education, and targeted interventions to enhance resilience and secure coffee-based livelihoods under changing climate conditions.\u003c/p\u003e","manuscriptTitle":"Coffee Production Trends and Adoption of Climate-Smart Agroforestry Practices Among Smallholder Farmers in Sidama Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 14:24:24","doi":"10.21203/rs.3.rs-7297913/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T17:12:27+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"126623394237034591349727294885933049669","date":"2025-11-09T11:48:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T17:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186199041792509377418122031983582860343","date":"2025-11-04T16:12:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T04:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246801118545416804329869768752164312159","date":"2025-10-21T04:51:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T07:01:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8617221969272106235234626939716759602","date":"2025-10-03T18:02:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T13:39:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T10:03:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T08:28:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-08-28T08:25:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e48885b-720d-4577-bd18-ad0b5d4d87d8","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T03:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 14:24:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7297913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7297913","identity":"rs-7297913","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}