http://ir.bdu.edu.et/handle/123456789/17120 2026-09-21T09:50:58Z http://ir.bdu.edu.et/handle/123456789/17327 Adoption of Cluster Farming and Its Impact on Commercialization, Poverty Reduction, and Food Security: Evidence from Northwestern Ethiopia Eshetu, Getachew Cluster farming has emerged as a strategic intervention in Ethiopia aimed at fostering sustainable agricultural intensification, enhancing smallholder commercialization, and improving rural livelihoods. Despite strong policy emphasis, its adoption remains low, with limited empirical evidence regarding its broader impacts and the constraints farmers face. This study provides a comprehensive analysis of the determinants and multifaceted outcomes of cluster farming by synthesizing findings from a survey of 421 randomly drawn households (199 adopters and 222 non-adopters), focus group discussions, and key informant interviews. By employing various econometric models including the Heckman selection model, Beta regression, Endogenous Switching Regression (ESR), Entropy Balancing Estimation (EBE), and Augmented Inverse Probability Weighting (AIPW) estimator, the study examines the determinants of farmers’ cluster farming adoption decision, the intensity of their adoption, and the impact of cluster farming on farmers’ commercialization, multidimensional poverty reduction, and food security improvement. The results highlight that cluster farming adoption is significantly influenced by household characteristics such as gender, education, landholding size and fragmentation, access to training and extension services, proximity to main road and market center, and peer influence, while the intensity of adoption is affected by gender, farm size, household size, extension service and off-farm and/or non-farm participation. The findings reveal that the adoption of cluster farming increases farmers’ commercialization, reduces multidimensional poverty, and enhances food security. Specifically, cluster farming adopters earned higher incomes and sold a larger share of their crop production, averaging about $1,106 in crop sales compared to $731 for non-adopters, a difference statistically significant at the 1% level. Moreover, adopter households sold, on average, 50% of their total crop production, compared to 36% for non-adopters, indicating that adopters are more commercial oriented than non-adopters. Crop-specific analysis further supports this trend. Wheat cluster adopters earned $908 and sold 64% of their yield, while non-adopters earned $496 and sold 44%. Similar patterns are observed for maize and teff: cluster farming adopters earned $706 and sold 63% of their maize, compared to $392 and 44% for non-adopters. For teff, adopters earned $942 and sold 68%, while non-adopters earned $386 and sold 45. In terms of poverty, while 77.9% of non-adopters were identified as multidimensionally poor, this figure dropped to 30% among adopters. Likewise, cluster farming demonstrates a substantial positive impact on multidimensional food security, particularly in the dimensions of food quantity, acceptability, and stability. Although the quality dimension showed no statistically significant improvement, the overall food security status of cluster farmers was notably higher than that of non-adopters. These findings underscore the transformative potential of cluster farming in improving rural livelihoods. Therefore, scaling up this farming approach is recommended. To enhance the adoption of cluster farming and its intensity of adoption, the study suggests the need to address gender disparities, strengthen training and extension services, and expand opportunities for off/non-farm activities. Additionally, improving access to farmland, perhaps, through better land lease mechanisms and ownership rights, could encourage greater adoption. Facilitating voluntary land consolidation can also reduce fragmentation and improve access to farmland for cluster farming adoption. 2025-10-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/17326 Utilization of Agricultural Information and its Impact on the Welfare of Smallholder Farmers in East Gojjam Zone, AmharaNational Regional State, Ethiopia Shitaye, Zebenay Agricultural information is a vital resource for improving long-term productivity and supporting rural, social, and industrial development. However, access and effective utilization remain limited among smallholder farmers. This study investigates the utilization of agricultural information and its impact on the welfare of smallholder farmers in the East Gojjam Zone of Amhara, Ethiopia. Primary data were collected from 403 households using a multi-stage sampling technique, supplemented by focus group discussions and key informant interviews. Secondary data were obtained through document reviews and electronic sources. Data were analyzed using descriptive statistics and advanced econometric models, including Ordered Probit, Multivariate Probit, Propensity Score Matching (PSM), and Logit models. The findings indicate that smallholder farmers primarily access information via radio, the Office of Agriculture, extension workers, family members, neighboring farms, and Farmer Training Centers (FTCs). The Ordered Probit model shows that farm experience, media exposure, farm size, participation in FTCs, and access to extension services significantly influence agricultural information utilization. The Multivariate Probit model reveals that farm size, membership status, access to credit, proximity to markets, extension services, total income, and willingness to share information positively affect farmers’ preference for electronic information outlets. Conversely, frequent market visits and shorter distances to development centers negatively influence this preference. Educational level, total farm size, total income, and cooperative membership positively affect the use of printed outlets, while family size and market visit frequency have negative effects. Organizational outlet choice is positively influenced by cooperative membership, income, distance to markets, and extension access, but negatively by marital status and education. People-related information preference is strengthened by membership status but reduced by large family size. The study also evaluates the impact of Agricultural Information Utilization (AIU) on wheat productivity and income using PSM and Endogenous Switching Regression (ESR). PSM minimized selection bias and showed that users achieved, on average, 197 kg/ha more wheat and earned 8,370 ETB more income than non-users. ESR results confirmed users gained 1,300 kg/ha and 14,000 ETB more compared to their counterfactuals. Non-users were projected to gain 950 kg/ha and 18,000 ETB if they adopted information use. These findings highlight the strong positive impact of AIU on productivity and livelihoods. The study is grounded in the Diffusion of Innovations Theory, Access to Information Theory, and the Sustainable Livelihoods Framework, which explain how communication channels, access barriers, and institutional factors shape information uptake and its effects. It recommends improving access and dissemination of agricultural information through integrated communication strategies. Strengthening farmer training and extension services especially via digital platforms and community networks can enhance productivity and welfare. Policymakers and stakeholders should prioritize these actions to close existing gaps and support rural development. 2025-08-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/17324 Climate Variability Adaptation Strategies and Their Impact on Multidimensional Food Security of households across Agro-Ecological Zones in Northwestern Ethiopia Adane, Tewodros Climate variability is escalating rapidly and is regarded as one of the most significant global challenges of the 21st century, threatening livelihoods worldwide. To adapt current and future climate variability effects, identifying effective adaptation strategies and promoting their widespread adoption is essential. This study examined the impact of climate adaptation strategies on multidimensional household food security among farming households in Amhara Regional State, Northwestern Ethiopia. Using data collected from 383 farming household, the study analyzed it by applying Item Response Theory (IRT), ordered probit, multivariate probit, and multinomial endogenous switching regression models. Item Response Theory (IRT) analysis revealed a gap between high knowledge (Mean = 3.84) and neutral-to-negative attitudes (M = 3.51). Attitude items showed polarization and low discrimination item A7 (ɑ = 0.64), suggesting the need for refined measurement tools. The study's multidimensional food security index, calculated across four key dimensions (quantity, quality, acceptability, and stability), showed that 82.2% of households were food secure, while 17.8% were food insecure. Key factors influencing food security included agro ecology, family size, age of the household head, drought, participation in off-farm activities, and education. The findings reveal that crop diversification (79.9%); drought-resistant varieties (62%), early-maturing crops (55.6%), and soil and water conservation (49.4%) are the most widely adopted adaptation strategies across the three agro ecological zones. The Multivariate Probit (MVP) model result indicated several key factors influencing farmers' choice of adaptation strategies, including age, education, family size, farm size, access to extension services, agro ecological zone, livestock ownership, market proximity and annual income. The analysis demonstrates that households’ adoption of climate adaptation strategies significantly enhances their multidimensional food security status. Using full packages (crop diversification, short maturing crops and drought resistance crops) had biggest impact; increase Food security by over 31 percentage points. The analysis underscores how policy interventions targeting education, rural infrastructure, family planning services, financial access, market linkages, and adaptation training can significantly improve food security outcomes for farmers. Future research should employ longitudinal designs to track adaptation patterns across multiple seasons and expand sampling to include additional agro ecological zones and non-farming households was recommended for future researchers. 2025-09-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/17323 Value Chain Analysis of Fish Production and Marketing From Lake Tana, Northwestern Ethiopia Genanew, Tigist Fisheries are one of the renewable resources that play a significant role on the livelihood of many rural and urban communities. Despite these, the sector is yet underdeveloped in Ethiopia and the fish value chain was not well addressed in Lake Tana. This study was carried out in Lake Tana during the 2022 season with four objectives: (1) to map the fish value chain, (2) identify actors and their roles, (3) to analyze the market margin of the actors along the fish value chain, (4) to identify factors that affect the quantity of fish market supply, and (5) to determine the factors affecting value addition participation and intensity. A three stage sampling procedure was adopted to randomly select the respondents from fishermen, fish traders, hotel and restaurant and consumers. The primary quantitative data were collected from 226 fishermen and 34 from traders, hotel and restaurants, and consumers. Descriptive statistics and econometric models, such as multiple linear regression with ordinary least square (OLS), double hurdle (DH), and multivariate probit models were employed for data analysis. The results reveal that input supplier, fishermen, wholesalers, retailers, collectors, cooperatives, vendors, hotels and restaurants, and consumers were the primary actors along the fish value chain. In all cases, hotels and restaurants followed by wholesalers have received the highest profit margin. The OLS result showed that education level, fishing experience, training, and market information have positive influence on fish market supply. On the other hand, farm land, engagement in fishing activity, boat type, market place problem, cold storage problem, and price fluctuation influenced the fish market supply negatively. According to the DH model result, boat type, fish catch/day and fishing experience found to have a positive and significant influence on the intensity of value addition. Whereas, total farm land, cold storage problem and labor cost affect the intensity of value addition negatively. The result of multivariate probit model indicated that education level, total farm land, boat type, engagement in fishing activities, market information, fish catch/day, and distance from landing site to main fish market significantly affected the fish market outlet of the fishermen. All these, suggest that fishery sector have several bottlenecks which needs intervention to better utilize the resources in the future. 2023-12-01T00:00:00Z