ABSTRACT The present study was conducted to assess the potential occurrence of coal deposits in the East Gojjam zone, specifically in Debre Eliyas woreda. It also aims to determine the quality and quantity of coal through field and laboratory techniques. A total of twenty coal samples and twenty rock samples were systematically collected from surface outcrops and analyzed at the Geological Survey of Ethiopia. Major oxides in the rock units were determined using Atomic Absorption Spectrometry (AAS), while coal samples were subjected to Gravimetric, Proximate, and Adiabatic Calorimetric analyses to quantify moisture content, volatile matter, fixed carbon, ash content, and calorific value. The geologic setup of the study area is predominantly characterized by sedimentary rock, like Sandstone, limestone, mudstone, and basaltic rocks. Geochemical analysis of sandstone, mudstone, and limestone samples reveals distinct compositional characteristics that reflect their depositional environments and diagenetic processes, providing valuable insights for resource exploration and geotechnical assessments. A geological map at a scale of 1:25,000 and three coal occurrence maps at a 1:20,000 scale were prepared based on detailed field surveys and laboratory analyses. Chemical analysis of collected coal samples revealed moisture contents ranging from 2.32% to 29.72%, volatile matter from 20.01% to 37.29%, fixed carbon from 7.12% to 31.88%, ash content from 4.27% to 66.07%, and calorific values between 2,323.044 and 9,378.684 Cal/gm. The values indicate that the coal in Debre Eliyas ranges in rank from lignite to bituminous. Across all identified coal-bearing sites, the average seam thickness ranges from 2.35 to 5.13 meters.  The total estimated coal resource of the study area is approximately 2,755,124.83 tons.  Keywords: Debre Elias, Coal Deposit, Economic Potential, Calorific value

ABSTRACT The main objective of the research is to investigate iron deposit by using petrographic, geochemical, XRD and geophysical results. To achieve the desired objective, secondary data compilation and interpretation, field work and post-field work (including petrographic result, geochemical result, XRD and geophysical result analysis) have been conducted. The study area is comprised of both Mesozoic sedimentary rocks and Tertiary - Quaternary volcanic rocks. The sedimentary rocks include sandstone, limestone, and shale, whereas the volcanic rocks are basalt and trachyte. Ternary diagrams of Al2O3-Fe2O3-SiO2 are commonly used to determine the degree of laterization. As laterization progresses increases, silica is leached out of the rock, leaving behind iron oxides.  Fe2O3-rich samples are indicative of higher degrees of lateritization, while SiO2-rich composition experienced weak lateritization (Meyer et al., 2002). Data points for iron ore samples from the study area, were plotted in moderate to strong lateritization field. Hematite, magnetite, goethite and siderite are the primary ore minerals, according to both polished section petrography and XRD investigations. Furthermore, the main gangue phases in the region are anatase, quartz and kaolinite. The mineral concentration is between 20.16 and 71.88% hematite, 7–40% goethite, 1–30 siderite, and 1-3 percent magnetite. Approximately 5–10.5% kaolinite, 3–25% quartz, and 0.5% anatase are among the related gangue minerals. Varying amplitudes of magnetic anomaly signature indicates that the ore body is not evenly distributed along the respective profile across the study area and the ore bodies suspected to be magnetic mineral exist near surface to medium depth which is between 23.33m to 52.5m. Iron occurrence resource estimation was done by a conventional approach methods, such as, resources = A (m2) *T (m) * ρ (g/cm3). As a result the total tonnage of iron resource is about 17,844,964.452 tons. Key words: Iron deposit, magnetic anomaly, geochemical result, geological map, host rock 

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña 

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña 

Executive Summary Concerns over chemical contaminants and toxins in local foods and beverages have been raised recently. These days, chronic illness is more common, and toxicants and other contaminants found in food and drink have been linked to many documented fatalities. The chemical toxicants that will be analyzed in this study include acrylamide (a processing toxicant arising from deep roasting of food samples), aflatoxin B1 (a poisonous chemical discharged by mold and fungi), pesticide residues (resulting from pesticide application to cereal, vegetable, and fruits), and methanol (a chemical toxicant produced by spontaneous fermentation of local alcoholic beverages). The acrylamide content of the food samples will be measured via LC/MS-MS. Aflatoxin analysis will be performed using high-performance liquid chromatography (HPLC), whilst pesticide residues and methanol concentrations will be assessed using a gas chromatography (GC) method equipped with computer-integrated software. This research will be undertaken in three phases. The first phase is collecting food and beverage samples (at least 30 food samples suspected of being chemically contaminated) from selected districts and assessing the toxic substances. The second phase relies on the results of the first phase; for severely contaminated foods, the sources of contamination and factors leading to the presence of chemical toxicants will be investigated. Finally, optimal processing will be investigated to remove or decrease toxic chemicals. The assessment of chemical contaminants in local foods and beverages is important as consumers become more conscious of their health and well-being. Therefore, this research aims to provide insight into the existence, concentrations, and possible health hazards associated with chemical contaminants in foods and beverages that are produced locally. It also makes actionable suggestions for resolving this pressing problem.

Executive Summary Concerns over chemical contaminants and toxins in local foods and beverages have been raised recently. These days, chronic illness is more common, and toxicants and other contaminants found in food and drink have been linked to many documented fatalities. The chemical toxicants that will be analyzed in this study include acrylamide (a processing toxicant arising from deep roasting of food samples), aflatoxin B1 (a poisonous chemical discharged by mold and fungi), pesticide residues (resulting from pesticide application to cereal, vegetable, and fruits), and methanol (a chemical toxicant produced by spontaneous fermentation of local alcoholic beverages). The acrylamide content of the food samples will be measured via LC/MS-MS. Aflatoxin analysis will be performed using high-performance liquid chromatography (HPLC), whilst pesticide residues and methanol concentrations will be assessed using a gas chromatography (GC) method equipped with computer-integrated software. This research will be undertaken in three phases. The first phase is collecting food and beverage samples (at least 30 food samples suspected of being chemically contaminated) from selected districts and assessing the toxic substances. The second phase relies on the results of the first phase; for severely contaminated foods, the sources of contamination and factors leading to the presence of chemical toxicants will be investigated. Finally, optimal processing will be investigated to remove or decrease toxic chemicals. The assessment of chemical contaminants in local foods and beverages is important as consumers become more conscious of their health and well-being. Therefore, this research aims to provide insight into the existence, concentrations, and possible health hazards associated with chemical contaminants in foods and beverages that are produced locally. It also makes actionable suggestions for resolving this pressing problem.

Abstract Mycotoxin contamination in stored sorghum grains poses significant threats to food safety, human health, and agricultural economies in sub-Saharan Africa, particularly in Ethiopia where poor post-harvest practices exacerbate fungal proliferation. This study, conducted in Dejen District, East Gojjam Zone, Ethiopia, aimed to assess mycotoxin contamination levels, farmers' knowledge and management practices, isolate and characterize mycotoxigenic fungi, and evaluate the efficacy of microbial antagonists and botanicals as eco-friendly control measures. A cross-sectional survey of 212 farmers revealed low awareness (29% good knowledge) and practices (40.57% effective management), with significant associations to age, education, and village location (p < 0.05, logistic regression). Mycological analysis of 120 stored sorghum samples from underground pits identified diverse fungi, dominated by Aspergillus flavus (25%) and Fusarium spp. (20%), with low mycotoxin levels (aflatoxin B1 at 1.05 µg/kg, below EU limits). Six fungal antagonists; four Trichoderma (DMUA13, DMUA14) and two Penicillium isolates were isolated and screened via dual-culture assays, achieving 44.53–75.00% inhibition of radial growth against Aspergillus and Fusarium spp. (p < 0.05). Ethanol extracts of Clematis simensis and Laggera tomentosa leaves demonstrated dose-dependent antifungal activity against A. niger, reducing spore germination by up to 68.58% and 68.32% at 100 µg/mL, respectively, comparable to ketoconazole. These findings underscore knowledge gaps among farmers and highlight the potential of native Trichoderma/Penicillium isolates and plant extracts as sustainable biocontrol agents. Targeted education, improved storage, and integration of biological controls are recommended to mitigate mycotoxin risks, enhancing food security in resource-limited settings. This research provides baseline data for policy interventions and further field validation.

Abstract Mycotoxin contamination in stored sorghum grains poses significant threats to food safety, human health, and agricultural economies in sub-Saharan Africa, particularly in Ethiopia where poor post-harvest practices exacerbate fungal proliferation. This study, conducted in Dejen District, East Gojjam Zone, Ethiopia, aimed to assess mycotoxin contamination levels, farmers' knowledge and management practices, isolate and characterize mycotoxigenic fungi, and evaluate the efficacy of microbial antagonists and botanicals as eco-friendly control measures. A cross-sectional survey of 212 farmers revealed low awareness (29% good knowledge) and practices (40.57% effective management), with significant associations to age, education, and village location (p < 0.05, logistic regression). Mycological analysis of 120 stored sorghum samples from underground pits identified diverse fungi, dominated by Aspergillus flavus (25%) and Fusarium spp. (20%), with low mycotoxin levels (aflatoxin B1 at 1.05 µg/kg, below EU limits). Six fungal antagonists; four Trichoderma (DMUA13, DMUA14) and two Penicillium isolates were isolated and screened via dual-culture assays, achieving 44.53–75.00% inhibition of radial growth against Aspergillus and Fusarium spp. (p < 0.05). Ethanol extracts of Clematis simensis and Laggera tomentosa leaves demonstrated dose-dependent antifungal activity against A. niger, reducing spore germination by up to 68.58% and 68.32% at 100 µg/mL, respectively, comparable to ketoconazole. These findings underscore knowledge gaps among farmers and highlight the potential of native Trichoderma/Penicillium isolates and plant extracts as sustainable biocontrol agents. Targeted education, improved storage, and integration of biological controls are recommended to mitigate mycotoxin risks, enhancing food security in resource-limited settings. This research provides baseline data for policy interventions and further field validation.

ABSTRACT Background: Blood donation is vital for saving lives, yet Ethiopia faces chronic shortages. Bridging the gap between willingness and actual donation is essential for sustaining blood supplies. This study examined socio-demographic, psychological, and contextual factors influencing blood donation intention and practice in East Gojjam Zone. Methods: A community-based cross-sectional survey of 1,332 urban adults who met inclusion criteria was conducted using multistage cluster sampling. Data were collected via a structured, pre-tested questionnaire. Descriptive statistics summarized donation patterns, while binary logistic and Zero-Inflated Negative Binomial regressions identified predictors of donation intention and frequency, respectively. Structural Equation Modeling (SEM) examined associations among socio-demographic characteristics, psychological factors, donation intention, practice, and behavior. Results: Although 74.8% (95% CI: 72.5–77.1) expressed willingness to donate, only 28.8% (95% CI: 26.4–31.2) had ever donated, and 20.7% (95% CI: 18.5–22.9) had donated in the past two years. Male gender, younger age, higher education, and employment in health or education sectors positively predicted intention and practice. Psychological factors—including self-efficacy (β = 0.53), positive attitudes (β = 0.46), knowledge (β = 0.38), altruism, and social influence (β = 0.28)—were strongly associated with donation. SEM confirmed that intention strongly predicted actual donation (β = 0.62). Barriers included fear of needles, health concerns, and limited awareness. Conclusions: Despite high willingness, actual donation remains low. Multi-sectoral, culturally sensitive strategies—addressing fears, improving knowledge, leveraging social influence, and enhancing accessibility—are critical, especially for women, older adults, and less-educated individuals, to build a resilient, community-driven blood supply. Keywords: Blood donation, intention, practice, socio-demographic factors, psychological determinants, Ethiopia, Structural Equation Modeling

ABSTRACT Background: Blood donation is vital for saving lives, yet Ethiopia faces chronic shortages. Bridging the gap between willingness and actual donation is essential for sustaining blood supplies. This study examined socio-demographic, psychological, and contextual factors influencing blood donation intention and practice in East Gojjam Zone. Methods: A community-based cross-sectional survey of 1,332 urban adults who met inclusion criteria was conducted using multistage cluster sampling. Data were collected via a structured, pre-tested questionnaire. Descriptive statistics summarized donation patterns, while binary logistic and Zero-Inflated Negative Binomial regressions identified predictors of donation intention and frequency, respectively. Structural Equation Modeling (SEM) examined associations among socio-demographic characteristics, psychological factors, donation intention, practice, and behavior. Results: Although 74.8% (95% CI: 72.5–77.1) expressed willingness to donate, only 28.8% (95% CI: 26.4–31.2) had ever donated, and 20.7% (95% CI: 18.5–22.9) had donated in the past two years. Male gender, younger age, higher education, and employment in health or education sectors positively predicted intention and practice. Psychological factors—including self-efficacy (β = 0.53), positive attitudes (β = 0.46), knowledge (β = 0.38), altruism, and social influence (β = 0.28)—were strongly associated with donation. SEM confirmed that intention strongly predicted actual donation (β = 0.62). Barriers included fear of needles, health concerns, and limited awareness. Conclusions: Despite high willingness, actual donation remains low. Multi-sectoral, culturally sensitive strategies—addressing fears, improving knowledge, leveraging social influence, and enhancing accessibility—are critical, especially for women, older adults, and less-educated individuals, to build a resilient, community-driven blood supply. Keywords: Blood donation, intention, practice, socio-demographic factors, psychological determinants, Ethiopia, Structural Equation Modeling