http://ir.bdu.edu.et/handle/123456789/6 2026-10-13T01:48:31Z 2026-10-13T01:48:31Z Anteneh, Mulugeta Eyasu http://ir.bdu.edu.et/handle/123456789/17410 2026-10-05T13:34:32Z 2026-06-01T00:00:00Z

The Impact of Crop Commercialization on Multidimensional Poverty In Rural Ethiopia: Evidence from Advanced Mixed and Causal Models Anteneh, Mulugeta Eyasu Poverty remains one of the most critical development challenges globally, particularly in low income agrarian countries such as Ethiopia, where rural livelihoods depend heavily on agriculture and multidimensional deprivation remains widespread. Although previous studies have examined the welfare effects of agricultural commercialization, most have relied on income-based poverty measures, conventional regression approaches, or geographically limited data, with limited attention to multidimensional poverty, spatial heterogeneity, and causal inference. This dissertation addresses these gaps by examining the causal impact of crop commercialization on multidimensional poverty among rural households in Ethiopia using nationally representative data from the fourth round of the Ethiopian Socioeconomic Survey (ESS 2018/19). The study analyzed 2,714 rural households nested within 59 administrative zones, integrating household, cluster, and spatial information. Multidimensional poverty was measured using the Alkire–Foster framework based on five dimensions and sixteen indicators covering nutrition & health, education, living standards, rural livelihoods & resources, and risk. A range of advanced statistical methods was employed, including generalized linear mixed models, multilevel spatial models, Targeted Maximum Likelihood Estimation (TMLE), multilevel causal mediation analysis, generalized propensity score methods for multiple treatments, and causal forest machine learning algorithms. The findings show that 47.8% of rural households were multidimensionally poor, with living standards contributing the largest share of deprivation. Poverty was disproportionately concentrated among subsistence-oriented and female-headed households, with significant spatial clustering across administrative zones. Across multiple modelling approaches, crop commercialization was consistently associated with a statistically significant reduction in multidimensional poverty. TMLE combined with machine learning and ensemble methods produced the most efficient and least biased estimates. Commercial-oriented households had a 3.56 percentage point lower probability of multidimensional poverty than subsistence households, while semi commercial households experienced smaller gains. Mediation analysis indicated that household savings partially explained the poverty-reducing effect of commercialization. Heterogeneity analysis further revealed stronger benefits among households located closer to markets and roads, with larger landholdings and younger household heads. The results suggest that crop commercialization can serve as an effective pathway for reducing rural xi multidimensional poverty, but its benefits depend on complementary investments in rural infrastructure, financial inclusion, education, and market access. By integrating causal inference, spatial statistics, and machine learning, this dissertation provides robust and policy relevant evidence to support targeted and place-based rural development strategies in Ethiopia.

2026-06-01T00:00:00Z Abebe, Worku Amberbir http://ir.bdu.edu.et/handle/123456789/17398 2026-09-28T07:01:15Z 2026-06-01T00:00:00Z

Ecological Dynamics, Ethnobotany, And Carbon Sequestration Potential Of Woody Plant Species In The Zegie Peninsula And Neighboring Monastic Forests, Northwest Ethiopia Abebe, Worku Amberbir The forests of the Zegie Peninsula and the nearby monastic islands of Lake Tana constitute a bioculturally, economically, and environmentally important remnant of Afromontane Forest in northwestern Ethiopia. These forests harbor high woody species diversity, store substantial amounts of carbon, and sustain long-established indigenous knowledge systems related to medicinal plant use and coffee-based agroforestry. Despite their ecological and cultural importance, the forests are increasingly threatened by anthropogenic pressures, necessitating an integrated ecological and ethnobotanical assessment. This monograph synthesizes findings from four interrelated objectives: assessment of woody species diversity, vegetation structure and regeneration; estimation of aboveground and belowground biomass and carbon sequestration potential; documentation of medicinal plant diversity and use patterns as well as threats to medicinal plants; and evaluation of indigenous shade tree species selection and management practices in coffee agroforestry systems.Vegetation and carbon stock data were collected from 165 systematically established plots across the Zegie Peninsula and three monastic islands (Kebran Gebriel, Entons Eyesus, Daga Estifanos). Ethnobotanical information was gathered through semi-structured interviews, focus group discussions, key informant interviews, and guided field walks. A total of 162 woody species, 81 medicinal plant species, and 20 shade tree species were recorded, confirming the exceptional biological and cultural richness of the study area. The forests exhibited high species diversity, relatively balanced population structures, and generally healthy regeneration patterns, although several ecologically and economically important species showed poor regeneration. Mean total carbon stock was estimated to be 196.4 tons C ha⁻¹, highlighting the strong climate change mitigation potential of these forests. Carbon storage varied significantly among sites and was more strongly influenced by topographic factors than by classical biodiversity metrics. The low contribution of soil physicochemical characteristics (31.3%) to species spatial variation indicates that anthropogenic pressures and natural disturbances play a major role in shaping the plant communities of the Zegie Peninsula and nearby monastic island forests. Generalized Linear Mixed Model (GLMM) analysis showed that selective cutting had a significant negative effect on species richness and seedling density, indicating that anthropogenic disturbance reduces biodiversity and limits natural regeneration, while stand density of mature trees remained relatively stable, suggesting a late response of forest structure to disturbance. Medicinal plant use was characterized by high informant consensus for common ailments, XIX reflecting deep-rooted traditional healthcare practices. However, ethnobotanical knowledge was unevenly distributed among social groups and showed signs of erosion, particularly among younger generations. Indigenous knowledge also played a central role in sustaining coffee-based agroforestry systems. Farmers applied complex ecological, functional, and cultural criteria in shade tree selection, favoring species that enhance coffee yield and soil fertility, minimal root competition, and microclimatic regulation. Monastic institutions was found as key custodians of indigenous tree species through long-standing religious norms and management practices. In general, the forests of the Zegie Peninsula and neighboring islands function as important biodiversity refugia, significant carbon sinks, and living repositories of indigenous knowledge on medicinal plants use and management as well as selection and management of shade tree species in coffee-based agroforestry system. Strengthening community-based conservation, promoting intergenerational knowledge transmission, and integrating carbon-focused incentive mechanisms with indigenous management practices are essential to ensure the long-term sustainability of these unique forest landscapes.

2026-06-01T00:00:00Z Mesfin, Mulu http://ir.bdu.edu.et/handle/123456789/17397 2026-09-25T12:42:00Z 2026-07-01T00:00:00Z

Statio-Temporal Modeling and Deep Learning–Based Forecasting Of Severe Food Insecurity In Africa Mesfin, Mulu Food insecurity remains a major humanitarian and development challenge in African countries. Understanding its distribution, underlying determinants, and future trajectories is essential for strengthening early-warning systems and targeted interven tions. Therefore, this study examined the spatio-temporal dynamics and determinants of severe food insecurity across 52 African countries during 2015–2021 and projected its future trajectories. The dataset was sourced and compiled from food and agricul tural organization, world bank, malaria atlas project, and Ethiopian statistical service. Inference was carried out using integrated nested Laplace approximation algorithm with Gaussian Markov random fields. The analyses employed different methodolog ical approaches including spatial and spatio-temporal clustering analytical tools, ma chine learning models, spatial models, spatio-temporal models, spatio-temporal vary ing coefficient model and deep learning architectures. Among the models considered in each methodological class, extreme gradient boost, geoadditive mixed model, spatio temporal type I interaction model, and spatio-temporal graph convolutional network outperformed the other models within their respective classes. The results showed that malaria incidence (β = 0.01, SD = 0.003), climate change (β = 0.65, SD = 0.31), livestock production ( β = −0.05 , SD = 0.02), and investment inflow (β = −0.03, SD = 0.01) exhibited relatively consistent and statistically significant linear fixed ef fects across countries and over time at 95% credible intervals, whereas unemployment, greenhouse gas emissions, gross domestic product per capita, dietary energy supply, and dietary protein supply demonstrated statistically significant spatial and temporal hetero geneity in both the magnitude and, in some instances, the direction of their effects. The findings also showed that severe food insecurity was significantly spatially dependent (τ 2 θ = 2705.77) and temporally correlated (τ 2 α = 10.75) across the continent. In addition, the space-time interactions (τ 2 δ = 29,438.77) contributed additional variability. The find ings also revealed substantial and persistent disparities across African countries over time. Throughout the study period, South Sudan, Central Africa Republic, Republic of the Congo, Malawi, Guinea, Cameroon, and Somalia experienced higher severe food insecurity risks compared to other countries. The southern, eastern and central subre gions exhibited strong spatial dependence in severe food insecurity. In addition, across much of the study period, Kenya, Ethiopia, Niger, and Burkina Faso frequently expe rienced unusual country-year-specific shocks in severe food insecurity crises compared to other countries. Data-driven forecasts further suggested that severe food insecurity is expected to intensify in Africa. In particular, severe deterioration was projected in South Sudan, the Central African Republic, the Republic of Congo, Malawi, Guinea, Somalia, viii Ethiopia, Kenya, Democratic Republic of Congo, Angola, Namibia, Niger and Nigeria. These findings indicated the coexistence of cross-country shared structural influences and country-specific factors in shaping severe food insecurity across Africa. Policy makers should prioritize targeted and context-specific interventions in high-risk areas, strengthen national multi-sectoral and regional coordination, and emphasize proactive and anticipatory strategies, including early warning systems, to support progress toward achieving sustainable development goal 2 (Zero Hunger) across Africa.

2026-07-01T00:00:00Z Tadesse, Baye http://ir.bdu.edu.et/handle/123456789/17335 2026-09-07T08:18:27Z 2026-06-29T00:00:00Z

A Comparative Analysis of A Three-Stage Cmos Op-Amp and A Two-Stage Cmos Op-Amp With Current Buffer Tadesse, Baye Over the past decades CMOS technology has been continuously scaling and resulted in sustained improvement in transistor speeds. However, achieving higher DC open-loop gain from the op-amps became challenging. Also, the transistor threshold voltages did not decrease at the same rate as the supply voltage. This thesis work presents a detailed comparative study on the DC open loop gain and voltage swing from two different CMOS topologies: A three-stage CMOS op-amp and a two-stage CMOS op amp with current buffer circuit. We have designed, simulated, and analyzed the two circuit topologies using industry standard Cadence OrCAD and Allegro SPB v17.4 software. The three-stage CMOS op-amp exhibited 156.55 dB, 428.67 MHz, and 670 as DC gain, GBW, and PM respectively. Whereas, the two-stage CMOS op-amp with current buffer circuit show 133.33 dB, 6.87 MHz, and 73.60 results of DC gain, GBW, and PM

2026-06-29T00:00:00Z