Small-scale irrigation is one of the agricultural activities used by rural farmers to improve the overall livelihood of the rural community by increasing income, securing food, meeting social requirements, and reducing poverty. The main objective of this study was to look into the factors that influence small-scale irrigation for poverty reduction among small-holder farmers in the Offa Woreda, Wolaita Zone. Government and nongovernmental organizations supplied small-scale irrigation and training to small-holder farmers in the study area at various times; however, the key determinants impacting small-scale irrigation use were not fully identified in the study area. The study location was chosen for this study purpose because no prior in-depth research had been conducted. Simple random sampling was used to select the three kebeles for the study. A total of 134 people were included in the study sample frame. The sample was chosen using simple random sampling and the proportional probability-to-size (pps) method, which was proportional to the size and used multistage sampling procedure. The primary data were collected using the interview schedule. Both quantitative and qualitative data were analyzed using descriptive and econometric analyzes in SPSS version 21. Qualitative data were also collected through focus groups and key informant interviews. A factor that influences small-scale irrigation use was investigated using a binary logistic regression model. The binary logistic regression model revealed that both the irrigation participants (37.3%) and the nonparticipant (62.7%) families had a combination of personal and demographic, economic, institutional, and social influencing factors that hinder their likelihood of using irrigation. HH age, annual income of the household, distance from the closest farm sight, farm experience, participation in training, access to irrigation, and sex of HH were positively and significantly influencing factors in the model, while total ownership of livestock of households and health status of households were negatively significant variables. As a result, the Agricultural and Natural Resources Development Office, as well as the Water Mining and Energy Offices, should devote scientific attention to the significant factors that influence irrigation use to boost farm family production and productivity.

One of the agricultural activities of rural farmers is dairy farming. Furthermore, by providing income, it has the potential to make farm households rich. The objective of this study was to analyze the general factors that influence adoption decisions, as well as the status of adoption of dairy technology in the Offa district, Wolaita zone, southern Ethiopia. The study area has fundamental factors influencing the adoption of dairy technologies which were unknown. This research was started to fill a knowledge gap for responsible bodies. Four kebeles were selected randomly for the study. 150 sample households (63 adopters and 87 nonadopters) were chosen using the SRS (systematic random sampling) method. The interview schedule was used to collect primary data from these households. Secondary data were gathered from a variety of sources, including reports, research findings, documents, and publications. Both quantitative and qualitative data were analyzed by using descriptive and econometric analyzes by SPSS version 21. Focus group discussions and key informant interviews were also used to collect qualitative data. A binary logistic regression model was used to examine the factors that influence the adoption of dairy technologies. The results of the binary logistic regression model revealed that both dairy technology participants and nonparticipant households experienced a combination of personal, demographic, economic, and institutional factors that hindered adoption. Respondent age, access to credit services, size of household landholding, frequency of the extension contact, involvement of outside income, livestock ownership, and distance from the nearest market were all significant negative contributing factors. On the other hand, farm income and dairy farming experience were positively significant variables in the model. Technical assistance to improve farm productivity and income, experience sharing and field visit programs to the fields of early experienced dairy farmers, improvement of farmland usage, improvement of road infrastructure in the study area, provision of appropriate and modernized training and extension services, and so on are among the recommendations.