CHOICE OF CLIMATE CHANGE ADAPTATION STRATEGIES AND ASSOCIATED FACTORS: EVIDENCE FROM SOUTHEAST ETHIOPIAN SMALLHOLDER FARMERS

The aim of this study is focusing on identification of the determinants of smallholder farmers’ choice of adaptation strategies to climate change and their associated factor in southeast Ethiopia. To get the data both qualitative and quantitative data were collected from primary and secondary sources. To get carry out the study 155 household heads were employed. Interview schedule, survey questionnaires, FGDs, key informant interview and field observation were also employed to collect the data. To analyze the data both descriptive statistics and econometric model were employed. Multivariate probit model was employed to identify the determinants of smallholder farmers’ choice of adaptation strategies to climate change and the result showed that the likelihood of households to adopt planting tree, terracing practice, improved varieties of crops, adjust planting date, and use crop diversification were 76.5%, 74%, 51%, 46.7%, and 40.4%, respectively. Therefore, future policy should focus on awareness creation on climate change and its adaptation strategies

Perceptions of Parents towards the Academic Performance of Female Students: The Case of Kutto Sorfella Primary School, Sodo Zuria Woreda, Southern Ethiopia

The objectives of the study were to assess the current status of the academic performance of females in grade seven and eight and to study how perception of parents affect the academic performance of female students in Kutto Sorfella Primary School, Sodo Zuria Woreda, SNNPR, Ethiopia. To achieve the objectives of this research both qualitative and quantitative data were collected during the survey from primary as well as secondary sources.  For this study 23 household heads were selected by simple random techniques and interviewed for the purpose of the study. Instruments of data collection included structured survey questionnaire, guided interviews, key informant interviews, and focus group discussions. To analysis data qualitative techniques such as summarization, and categorization were used but concerning to quantitative data, the computer software; SPSS-16 version was used. The other specific methods of data analysis such as tabulation and cross-tabulation were also used. Computation of descriptive statistic such as frequency, percentage and mean and were also carried out. From the result the researchers found out perception, attitudes, and opinion of parents towards female education, affected the academic performance of female students negatively so that the academic achievement of female students is very low in the study area. Therefore, in order to overcome these problems some strong commitment and reformation should be done in the study area by parents, students, local leaders, policy makers, GOs and NGOs. Keywords: Perception, self-esteem, aspiration of Parents, academic performanc

On-Farm Activities and Households Food Security in Wolaita Zone, Ethiopia

Objectives of the study were to measure status of household food security, to measure the severity levels of household food insecurity, and to analyze factors affecting on-farm activities. In the study multi-stage sampling techniques were used to select 5 kebeles and 140 sample households out of 23 kebeles of the study area. Primary data were collected through structured interview schedule, key informants interview, focus group-discussions and direct observation. Various documents were also reviewed to collect the secondary data. The quantitative data were analyzed by descriptive statistics and qualitative data were analyzed through generalization summarization and categorization. Household food security was measured using calorie consumption method. A binary logit model was used to analyze factors affecting on-farm activities. The findings of the study revealed that about 57% of the rural households were food insecure and 43% were food secure. A total of eleven explanatory variables were included in the model from which seven variables showed a significant effect on household food security.  The estimated model correctly predicted 92.1% of the total sample households. To estimate the extent of food insecurity FGT index was used. Accordingly, the incidence of food insecurity, food insecurity gap and severity of food insecurity were found to be 57%, 24% and 11.67%, respectively. Keywords: Food security, on-farm activities, binary logistic regression model, incidence of food insecurity, food insecurity gap, severity of food insecurity, FGT index

Factors influencing adoption of improved potato (Belete) variety: evidence from Ethiopian smallholder farmers

Saabunud / Received 15.08.2019 ; Aktsepteeritud / Accepted 11.12.2019 ; Avaldatud veebis / Published online 25.12.2019 ; Vastutav autor / Corresponding author: Guta Regasa e-mail: [email protected] the adoption of improved crop varieties is very important to increase income, reduce hunger, sustain food security, and to reduce poverty in sub-Saharan Africa like Ethiopia. Similarly, Belete potato variety is one of the improved varieties that have been utilized by Ethiopian farmers, but this variety was not conjointly adopted in all parts of the country. Thus, this research was intended to analyze factors influencing rural farmers' decision for the adoption of improved potato varieties in Southern Ethiopia. Both qualitative and quantitative data were collected from primary and secondary sources. To select the sample respondents, two-stage sampling techniques were employed and finally, 146 households' heads were selected. To get the data survey questionnai-res, interview schedules, Focused Group Discussions, observations and key informant interviews were employed. To analyze the data, both descriptive statistics and econometric model were employed. Accordingly, the econometric model indicated that family labour, access to fertilizer, access to credit service, frequency of extension contacts, participation in training and field day, and educational level were positively and significantly influenced the adoption of Belete potato adoption, however, the market distance was influenced negatively. Therefore, this result implies that researchers, policymakers, extension service providers and other concerned bodies should be given attention to increasing the adoption of improved Belete potato variety

Analysis of Credit Service Utilization by Rural Households: The Case of Humbo Woreda, Wolaita Zone, Ethiopia

The objectives of the study were to analyze credit service utilization by rural households and to assess the prevailing challenges faced by microfinance institutions in the provision of credit service in the study area. In this study multi-stage sampling techniques were used to select 5 kebeles out of 42 kebeles of the study area and 150 sample households were randomly selected, of which 82 were credit users and 68 of them were non-users. Primary data were collected through structured and semi- structured interview schedule, Key Informant Interviews and Focus Group-Discussions. Various documents were reviewed to collect secondary data. To analyze the data, descriptive statistics such as mean, standard deviation, percentage, and frequency distribution were used to describe the institutional, socio-economic and demographic characteristics of the sample households. In addition, t-tests and chi-square tests were used to compare credit user and non-user sample groups with respect to the explanatory variables. Socio-economic factors such as income, possession of fixed asset, risk fearing, saving habit and opinion on group lending had showed significant relationship with credit service utilization. In addition to this, institutional factors like lending procedure, type of collateral required, and training and technical advice had showed significant relationship with credit service utilization. Therefore, credit service providers should give attention to factors that significantly influence credit service utilization by rural households, focus to institutional capacity building, implementing a working and effective follow-up system, and designing human resource development plan need to be implemented by the concerned authorities to improve the performance of microfinance service provision and credit service utilization. Keywords: Credit Service, Rural Households, Credit Utilization, Microfinanc

Socio-Economic Determinants of Credit Service Utilization by Smallholder Households at Wolaita Zone, Ethiopia

The objective of the study was to identify the determinants of credit service utilization in the study area. In this study multi-stage sampling techniques were used to select 5 kebeles out of 42 kebeles of the study area and 150 sample households were randomly selected, of which 82 were credit users and 68 of them were non-users. Primary data were collected through structured interview schedule. Various documents were reviewed to collect the secondary data. In the study Econometric model (binary logit model) was used to identify the determinants of credit service utilization. A total of fifteen explanatory variables were included in the model. Out of these, seven were found to be statistically significant and most of the coefficients of these variables exhibited the expected signs with the hypothesis. These variables include total income, saving habit, collateral type, training and technical advice, possession of fixed assets, risk fearing and lending procedure. Therefore, credit service providers should give attention to factors that significantly influence credit service utilization in order to improve the performance of microfinance service provision and credit service utilization. Keywords: Credit, binary logistic regression model, Determinants, Credit Utilization, Smallholder Households, Wolaita Zone  

Analysis of Credit Service Utilization by Rural Households: The Case of Humbo Woreda, Wolaita Zone, Ethiopia

The objectives of the study were to analyze credit service utilization by rural households and to assess the prevailing challenges faced by microfinance institutions in the provision of credit service in the study area. In this study multi-stage sampling techniques were used to select 5 kebeles out of 42 kebeles of the study area and 150 sample households were randomly selected, of which 82 were credit users and 68 of them were non-users. Primary data were collected through structured and semi- structured interview schedule, Key Informant Interviews and Focus Group-Discussions. Various documents were reviewed to collect secondary data. To analyze the data, descriptive statistics such as mean, standard deviation, percentage, and frequency distribution were used to describe the institutional, socio-economic and demographic characteristics of the sample households. In addition, t-tests and chi-square tests were used to compare credit user and non-user sample groups with respect to the explanatory variables. Socio-economic factors such as income, possession of fixed asset, risk fearing, saving habit and opinion on group lending had showed significant relationship with credit service utilization. In addition to this, institutional factors like lending procedure, type of collateral required, and training and technical advice had showed significant relationship with credit service utilization. Therefore, credit service providers should give attention to factors that significantly influence credit service utilization by rural households, focus to institutional capacity building, implementing a working and effective follow-up system, and designing human resource development plan need to be implemented by the concerned authorities to improve the performance of microfinance service provision and credit service utilization. Keywords: Credit Service, Rural Households, Credit Utilization, Microfinanc

DETERMINANTS OF SMALLHOLDER FRUIT COMMERCIALIZATION: EVIDENCE FROM SOUTHWEST ETHIOPIA

The aim of the study was to identify determinants of smallholder fruit commercialization in southwest Ethiopia. To get the sampled respondents multi-stage sampling techniques were used and in view of that, three districts were selected purposively from Jimma zone by selecting eight kebeles randomly. At the end, total of 240 sample households were randomly selected from these kebeles. To answer the research questions and objective of the study both qualitative and quantitative data were collected from primary and secondary sources. The primary data were collected from fruit producers as sampled households, agricultural experts, local leaders and other subject matter specialists on various aspects of commercializing fruits. Primary data were collected from the respondents using a pre-testing questionnaires, structured interview schedule and closed and open-ended questionnaires by well-trained enumerators closely supervised by the researchers. Secondary data were previous research findings and reports collected from kebeles, districts, agricultural offices, ministry of trade, trade and revenue offices. Moreover, qualitative data were collected through discussions with different agents by using focus group discussion and key informant interviews. The collected data were analysed using descriptive statistics (frequency, percentage, average mean, standard deviation, chi square and t-test) and econometric model (Probit model) to analyse determinant factors affecting smallholder’s participation decision in marketing of fruits. From the result, age of household heads, household family size, access to transport services, off-farm activities, access to extension services, distance to market, improved fruit seeds and perishability of fruit were significantly affecting smallholder farmer’s participation decision in commercialization of fruits. Therefore, to overcome the investigated problems strong commitment and reformation should be done by stakeholders including farmers, extension agents, researchers, policy makers

Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Reducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes. Methods We estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest. Findings From an estimated 13·7 million (95% UI 10·9–17·1) infection-related deaths in 2019, there were 7·7 million deaths (5·7–10·2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13·6% (10·2–18·1) of all global deaths and 56·2% (52·1–60·1) of all sepsis-related deaths in 2019. Five leading pathogens—Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa—were responsible for 54·9% (52·9–56·9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185–285) per 100 000 population, and lowest in the high-income super-region, with 52·2 deaths (37·4–71·5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths. Interpretation The 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development

Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Summary Background Reducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes. Methods We estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest. Findings From an estimated 13·7 million (95% UI 10·9–17·1) infection-related deaths in 2019, there were 7·7 million deaths (5·7–10·2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13·6% (10·2–18·1) of all global deaths and 56·2% (52·1–60·1) of all sepsis-related deaths in 2019. Five leading pathogens—Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa—were responsible for 54·9% (52·9–56·9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185–285) per 100 000 population, and lowest in the high-income super-region, with 52·2 deaths (37·4–71·5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths. Interpretation The 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development