Application of Linear Programming to Analyze Profit of Flour Factory, in the Case of Sanate Flour Factory, at Robe Town

The purpose of this study was to analyze the total production and profit of Sanate flour factory located in Ethiopia, Oromia regional state, Bale zone, Robe town, by applying linear programming. A factory is situated within Robe town about 430 KM, from Addis Ababa (Capital of Ethiopia). Today linear programming was the most popular method of manipulating a large amount of data. Hence, Different studies bring out the necessity of using quantitative techniques for utilization in the factory. So, in this paper to analyze the production and profit of this factory, the study incorporates different steps; the first step is collecting data. A data collecting formats prepared and circulated among factory staff to executive managers, co-managers, sellers, machine operators, and technicians to determine the production, sales, and profit during five months of November 30, 2018- June 18, 2019. In the second step, a collected data is modeled to mathematical form, particularly modeled to linear program. In the third step the mathematical modeled data was solved (analyzed). Finally, depending on the empirical results (the solution of a modeled data) some problem facing the factory was indicated and the solution for the problem has been recommended

Co-developing and co-validating location-specific fertilizer and agroclimate advisory service for Wheat in Ethiopia: the Digital Green Use Case

This report addresses activities conducted during the Incubation Phase of the Excellence in Agronomy (EiA) “Digital Green Ethiopia Use Case”. The report outlines the major activities implemented from proposal development to the execution of the main activities and associated results. The focus of the Use Case was to capitalize on the datasets and resources of the coalition of the willing (CoW) led by the Alliance of Bioversity and CIAT and supported by GIZ) to develop and pilot a minimum viable product (MRV) related to the development of an agroadvisory tool incorporating fertilizer, crop planting date and wheat rust surveillance for wheat value chain in Ethiopia. EiA is generally composed of content development and associated demand partner. In this case, the Alliance and its team envisaged developing location-specific agroadvisory (content) and Digital Green would disseminate the content to extension and farmers using its agile channels. Accordingly, the Alliance team in Ethiopia supported by EiA and CoW team developed an integrated location-specific fertilizer recommendation tool that has been validated on selected farmers. in three regions and four Woredas of Ethiopia. Close to 300 farmers participated in the trails which were composed of: national blanket recommendation, local optimal recommendation (based on local research institutes, Universities, etc.), and the data-driven location-specific recommendation developed by the CoW-EiA collaborative project. Note that the ‘local optimal’ recommendation relates to commonly applied fertilizer type and rate based on suggestion by local experiences (applied in the four sites) but with no adequate documentation. Also note that the data-driven location-specific fertilizer recommendation refers to one developed through the collaborative effort of the CoW (supported by Alliance, GIZ-Ethiopia and EiA), in general referred to as the ‘Digital Green Use Case (DGUC). While evaluating the three trials, the Farmers’ field days and data analysis results clearly showed that the DGUC has produced significantly higher biomass and grain yield compared to the other two. Field validation results show that the location-specific advisory (DGUC) resulted in about 8-17% grain yield increase compared to the standard and local checks. Biomass yield of plots that received the DGUC advisory showed 8% (1 t ha-1 increase compared to the local check). This indicated location-specific fertilizer rate advisory boosted not only grain yield but also biomass yield, which is one of the most valuable products for feeding livestock in Ethiopia. In addition, thousand seed weight and plant vigor were higher with site-specific fertilizer rate compared with local fertilizer rates. This is an important achievement demonstrating the value of integrated data analytics to make date-based and knowledge-informed decision making. During the 2021/2022 season, an attempt will be made to develop and provide bundled advisories composed of onset of rains and planting date (extracted from EDACaP, Ethiopian Digital AgroClimate advisory Platform) and a weather surveillance system developed by different partners (EIAR, Alliance and CIMMYT). This report summarizes the details of activities associated with the DGUC undertaken in the 2020/2021 cropping season in Ethiopia. The report is organized into different sections, including: (1) background of the project, validation trial protocol development; (2) field trip to districts and kebeles for discussion and site selection; (3) training and planning workshop held on validation trial implementation, management, data collection and use of open data kit (ODK) for digital data collection; (4) field book preparation and customization of data forms on ODK; (5) fertilizer treatment set up for the target development group (DG); (6) barcoded identification card preparation for digital data collection; (7) validation trial inputs and research materials purchase and distribution; and (8) trial follow up and visit by Alliance (CIAT) and Digital Green team, and farmers’ field day to evaluate the three fertilizer treatment performances based on their observation; (9) validation trial data collection and submission to ONA using ODK tool; and (10) research results from the fertilizer validation trial data

Isolation and characterization of Listeria monocytogenes and other Listeria species in foods of animal origin in Addis Ababa, Ethiopia

Listeriosis is a disease of humans and animals, in which it is one of the important emerging bacterial zoonotic diseases worldwide. Among the different species of the genus Listeria, Listeria monocytogenes (L. monocytogenes) is known to cause listeriosis in humans and animals with low incidence but high case fatality rate. Information on the occurrence and distribution of L. monocytogenes and other Listeria species is very limited both in the veterinary and public health sectors in Ethiopia. The objective of this study was to isolate and characterize L. monocytogenes and other Listeria species from foods of animal origin (cottage cheese, raw beef, raw milk and liquid whole egg) in Addis Ababa, Ethiopia. A total of 391 food samples of animal origin were collected randomly, using a cross-sectional study design from November 2008 to March 2009. L. monocytogenes isolation and characterization were performed according to mainly the United States Food and Drug Administration procedures. Of the samples examined, 102 (26.1%) were found to be positive for Listeria. Listeria species were isolated in 39 (51.3%), 37 (32.2%), 22 (22%) and 4 (4%) of the raw beef, liquid whole egg, raw milk and cottage cheese samples respectively. L. monocytogenes was detected in 5.4% of the samples analyzed. It was isolated mainly from raw milk (13%) and liquid whole egg (4.3%) followed by raw beef (2.6%) and cottage cheese (1%). In addition to L. monocytogenes, other Listeria species were identified as L. innocua (60.8%), L. welshimeri (6.9%), L. seeligeri (3.9%), L. murrayi (2.9%) and L. grayi (2.9%) and L. ivanovii (1.9%). It was shown that L. monocytogenes and other Listeria species are widely spread in occurrence in foods of animal origin in Addis Ababa, Ethiopia. Keywords: Listeria monocytogenes, Listeriosis, Public health, Veterinary, Foods of animal origi

Coronavirus and Paramyxovirus Shedding by Bats in a Cave and Buildings in Ethiopia.

Bats are important hosts of zoonotic viruses with pandemic potential, including filoviruses, MERS-Coronavirus (CoV), SARS-CoV -1, and likely SARS-CoV-2. Viral infection and transmission among wildlife are dependent on a combination of factors that include host ecology and immunology, life history traits, roosting habitats, biogeography, and external stressors. Between 2016 and 2018, four species of insectivorous bats from a readily accessed roadside cave and buildings in Ethiopia were sampled and tested for viruses using consensus PCR assays for five viral families/genera. Previously identified and novel coronaviruses and paramyxoviruses were identified in 99 of the 589 sampled bats. Bats sampled from the cave site were more likely to test positive for a CoV than bats sampled from buildings; viral shedding was more common in the wet season; and rectal swabs were the most common sample type to test positive. A previously undescribed alphacoronavirus was detected in two bat species from different taxonomic families, sampling interfaces, geographic locations, and years. These findings expand knowledge of the range and diversity of coronaviruses and paramyxoviruses in insectivorous bats in Ethiopia and reinforce that an improved understanding of viral diversity and species-specific shedding dynamics is important for designing informed zoonotic disease surveillance and spillover risk reduction efforts