Agriculture is a sector that is very vulnerable to the effects of climate change while contributing to anthropogenic greenhouse gas (GHG) emissions to the atmosphere. Therefore, applying Climate-Smart Agriculture (CSA) technologies and practices (referee hereafter as CSA technologies) that can sustainably boost productivity, improve resilience, and lower GHG emissions are crucial for a climate resilient agriculture. This study sought to identify the CSA technologies used by farmers and assess adoption levels and factors that influence them. A cross-sectional survey was carried out gather information from 384 smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Data were analyzed using percentage, chi-square test, t test, and the multivariate probit model. Results showed that crop diversification, agroforestry, and integrated soil fertility management were the most widely practiced technologies. The results of the chi-square and t tests showed that there are differences and significant and positive connections between adopters and non-adopters based on various attributes. The chi-square and t test results confirmed that households who were older and who had higher incomes, greater credit access, climate information access, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had positive and significant associations with CSA technology adopters. The model result showed that age, sex, and education of the head; farmland size; livestock ownership; income; access to credit; access to climate information; training; and extension contact influenced the adoption of CSA technologies. Therefore, considering barriers to the adoption of CSA technologies, in policy and action is anticipated to support smallholder farmers in adapting to climate change while lowering GHG emissions.

Smallholder farmers who rely on home garden agroforestry are experiencing significant impacts from climate change. To mitigate these effects, it is crucial for farmers to have access to various adaptation strategies. This study collected data from 384 randomly selected respondents in 18 kebeles over three districts, using descriptive statistics and a multivariate probit model to evaluate the factors influencing smallholder farmers’ decisions on their adaptation strategies against climate change. In Ethiopia’s Gedeo zone, this study found that smallholder farmers employ a range of adaptation methods, including expanding their agroforestry system, implementing modern agriculture techniques, conserving soil and water, diversifying their livelihoods, and employing various coping mechanisms. By analyzing data using the multivariate probit model, this study found several factors that had a significant impact on smallholder farmers’ choice of adaptation options. These factors include social network, age, education level, farming experience, household size, cultivated land size, annual income, and livestock holding. In addition, factors such as perception of climate change, previous experience of crop failure, recurrent drought, and access to information about climate change, occurrence of frost, agricultural extension contacts, access to farmer-to-farmer extension services, and perception of land infertility also influence their decision-making process. Our findings highlight the importance of improving institutional services in rural areas, promoting education on climate change, and strengthening social networks to enhance the ability of smallholder farmers to cope with the effects of climate change.

Some studies have quantified the extent and examined the determinants of the global multidimensional poverty index (G-MPI) which contains only three dimensions of welfare. But studies on the extent and determinants of rural multidimensional poverty index (R-MPI) which contains rural livelihood as one dimension of welfare are scarce. Hence, this study quantified the extent and examined the determinants of rural multidimensional poverty index using the Alkire–Foster method and the ordered logistic regression model, respectively. Data were gathered from 415 random sample households in southern Ethiopia. The incidence, intensity, and adjusted headcount ratio of rural multidimensional poverty are 72.3%, 57.3%, and 41.4% respectively. Besides, 7.47%, 20.24%, 30.60%, and 41.69% of households are non-poor, vulnerable, poor, and extremely poor, respectively. The living standard and rural livelihood dimensions jointly contributed 60% to rural multidimensional poverty. Fuel for cooking, electricity, and housing are the primary contributors to rural poverty from the living standard dimension, while land ownership, off-farm activity, and tropical livestock units are the leading contributors from the rural livelihood dimension to rural deprivation. Regression results showed that education, land size, tropical livestock unit, off-farm participation, and saving are negatively and significantly related to rural poverty, while dependency ratio, distance from market, distance from the road, and sickness of family members are positively and significantly related to rural poverty. Female-headed households were more deprived than male-headed households. Hence, promoting access to land, non-farm employment, markets, electricity, drinking water, and a road network would reduce rural multidimensional poverty in southern Ethiopia.

Background: Childhood stunting and malnutrition condemn millions of people globally to a life of disadvantage and cognitive and physical impairment. Though increasing egg consumption is often seen as an important solution for low and middle income countries (including Ethiopia), emerging evidence suggests that greater exposure to poultry feces may also inhibit child growth due to the effects of enteric bacteria, especially Campylobacter, on gut health. Methods: In this rapid ethnographic study, we explored village poultry production, child dietary practices, and environmental hygiene conditions as they relate to Campylobacter risk and intervention in 16 villages in Haramaya Woreda, Eastern Ethiopia. Results: In the study area, we found that women assumed primary responsibility to care for both chickens and children: in feeding, housing, and healthcare. Most chickens were free-range local indigenous breeds, and flock sizes were small and unstable due to epidemics, seasonal trends, reproductive patterns, and lack of food. Generally, eggs were seen as “too luxurious” to be eaten, and were predominantly sold at local markets for scarce cash, despite high malnutrition rates. Local narratives of extreme poverty, social dietary norms, parental fatalism, and lack of “dietary consciousness” (as it was called) were invoked to explain this. We found that homesteads were highly contaminated with human and animal feces. Although community members viewed chicken feces and poultry gastrointestinal contents as particularly noxious in comparison to other animals because of their feeding behaviour, they did not relate them to any particular disease. Shared human-animal housing and childcare practices place children at high risk of exposure to enteric bacteria from animal manure, despite daily routines designed to manage the domestic landscape. Conclusions: Addressing childhood stunting and malnutrition through egg production in rural landscapes like Haramaya must navigate three distinct health and care regimes: for children, chickens, and home environments. Interventions should be based on a holistic approach to social and economic empowerment, one that considers both women and men and integrates nutrition, health, and community change as its overarching goal.

More than 95% of crop production in Ethiopia comes from smallholder farmers operating on a farm size of less than 2 hectares. This implies that agriculture is dominantly operated by smallholders nationally. Agricultural commercialization is believed to improve farmers’ livelihoods; hence, commercializing agriculture in Ethiopia means contributing to the smallholder farmer’s welfare. To do so, a study of participation and level of commercialization and their determinant factors is vital. Hence, this study was initiated to evaluate crop output commercialization status and its determinants in rural Ethiopia. Heckman’s two-step model was employed for the analysis. The mean commercialization index (CI) for the sample was 43.40% while 26% and 35% of households were commercially oriented and subsistent farmers respectively. The decision to participate in crop output marketing was affected by age, market distance, and instrument for the level of farm mechanization negatively. In contrast, it was affected positively by livestock size (TLU), being in mid-highland agroecology, ownership of equines, amount of annual income, and access to market information. The intensity of commercialization was positively affected by annual income, access to market information, and number of oxen; while it was negatively affected by operational land, level of crop diversification, and market distance significantly. Improving the resource endowment, and minimizing transaction costs by improving access to market centers, market information, and means of transportation can further enhance commercialization. Improving access to farm mechanization and enhancing productivity in highland areas are also issues that shall get policy and development practitioners’ focus.

Evidences suggested that use levels of inorganic fertilizers are below the recommended rates in maize production. It is, therefore, necessary to investigate the reasons behind the failure to adopt the recommended rates of these fertilizers on maize production. This study analyzed determinants of intensity of adoption using a survey data collected from 383 randomly selected maize producing households. For this purpose, a two-limit Tobit model was applied. The econometric result revealed that variation in districts, family size, membership to cooperatives, distance to FTC, and livestock holding significantly affected smallholders’ intensity of adoption of DAP in maize production. On the other side, variation in district, farming experience, farm size, membership to cooperatives, dependency ratio, and annual income significantly determined intensity of adoption of Urea. It is, therefore, necessary to give due emphasis to the indicated determinants in order to assist maize producing farmers by boosting maize productivity.