Econometrics Model on Determinants of Adoption
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Districts of Ethiopia
Region District or Woredas Zone Remarks Afar Region Argobba Special Woreda -- Independent district/woredas Afar Region Afambo Zone 1 (Awsi Rasu) Afar Region Asayita Zone 1 (Awsi Rasu) Afar Region Chifra Zone 1 (Awsi Rasu) Afar Region Dubti Zone 1 (Awsi Rasu) Afar Region Elidar Zone 1 (Awsi Rasu) Afar Region Kori Zone 1 (Awsi Rasu) Afar Region Mille Zone 1 (Awsi Rasu) Afar Region Abala Zone 2 (Kilbet Rasu) Afar Region Afdera Zone 2 (Kilbet Rasu) Afar Region Berhale Zone 2 (Kilbet Rasu) Afar Region Dallol Zone 2 (Kilbet Rasu) Afar Region Erebti Zone 2 (Kilbet Rasu) Afar Region Koneba Zone 2 (Kilbet Rasu) Afar Region Megale Zone 2 (Kilbet Rasu) Afar Region Amibara Zone 3 (Gabi Rasu) Afar Region Awash Fentale Zone 3 (Gabi Rasu) Afar Region Bure Mudaytu Zone 3 (Gabi Rasu) Afar Region Dulecha Zone 3 (Gabi Rasu) Afar Region Gewane Zone 3 (Gabi Rasu) Afar Region Aura Zone 4 (Fantena Rasu) Afar Region Ewa Zone 4 (Fantena Rasu) Afar Region Gulina Zone 4 (Fantena Rasu) Afar Region Teru Zone 4 (Fantena Rasu) Afar Region Yalo Zone 4 (Fantena Rasu) Afar Region Dalifage (formerly known as Artuma) Zone 5 (Hari Rasu) Afar Region Dewe Zone 5 (Hari Rasu) Afar Region Hadele Ele (formerly known as Fursi) Zone 5 (Hari Rasu) Afar Region Simurobi Gele'alo Zone 5 (Hari Rasu) Afar Region Telalak Zone 5 (Hari Rasu) Amhara Region Achefer -- Defunct district/woredas Amhara Region Angolalla Terana Asagirt -- Defunct district/woredas Amhara Region Artuma Fursina Jile -- Defunct district/woredas Amhara Region Banja -- Defunct district/woredas Amhara Region Belessa -- -
World Vision Ethiopia Early Warning Unit Grants Division
WORLD VISION ETHIOPIA EARLY WARNING UNIT GRANTS DIVISION RAPID NUTRITIONAL ASSESSMENT CONDUCTED IN FIVE DISTRICTS. (February 2000) Tenta South Wollo (Adjibar) Gera Keya North Shoa (Mehal Meda) Sodo zuria North omo (Damota) Humbo North omo (Damota) Tseda amba E. Tigray (Kilte Awlaelo) April 2000 Addis Ababa 1 1. Summary A rapid nutritional assessment was conducted in five districts of World Vision Ethiopia operational areas during February 2000. The objective of the In Gera keya district, it was reported that assessment was to monitor the status of two people died and 102 were sick of acute malnutrition (wasting) in the typhoid fever. As copping mechanism districts, which had high prevalence of farmers sold live animals, migrated to malnutrition during the November 1999, adjacent areas in search of food, sold survey. The districts are located in their labor, reduced frequency, quality Tigray region (Tseda amba), Amhara and quantity of their meals, and (Gera keya and Tenta) and Southern consumed less preferred foods. regional state (Humbo and Sodo zuria). 2. Methodology The assessment result reveals that the highest wasting level was observed in The sampling size was determined using Tseda Amba district 22.2%, Tenta the sampling formula (see Annex 1). 21.5%, Sodo zuria 16.7%, Gera keya Every Peasant Association was included 13.6% and Humbo 9.6%. in to the sample by randomly selected sub Peasant Associations (50% of sub Land preparation for short cycle crop has Peasant Associations). In each district not been started because the onset of over 700 children were weighed and short rain is distorted and is late by over measured using sub Peasant Associations eight weeks. -
Nutrition Surveys 1999
NUTRITION SURVEYS 1999 – 2000 Region/Zone woreda Date of Agency Sample size Methodology Nutrition Indicatorsi survey Tigre throughout August SCF-UK 937 30 cluster Mean WHL <80%WFL W/H<-2 Z score W/H <-3 Z score 1999 92.8% 5.5% 7.7 % 1.0% Tigre Feb 2000 WVI W/H <-2 Z score W/H <-3 Z score Eastern May 2000 Feb 2000 May 2000 Feb 2000 May 2000 Asti Wenberta 685 13.1% 10.9% NA 2.6% Saesi Tsaedaemba 1412 22.3% 20.1% 3.7% 4.4% Amhara: May-June SCF-UK + 2900 58 clusters in Mean WFL < 80% WFL N.Wello Bugna 1999 worst drought 88.8% 4% Wadla affected woredas. 89,4% 7% Gidan 87.8% 3% Delanta Dawnt 89.4% 7% Gubalafto 90.0% 7% S. Wello Dessie Zuria 89.8% 7% Tenta 90.5% 3% Legambo 90.8% 5% Ambassel 90.7% 6% Mekdella 91.2% 4% Wag Hamra Dehana 88.2% 4% Oromyia Chefa 92.8% 2% Amhara Aug - Oct SCF-UK + 2500 50 clusters in Mean WFL < 80% WFL 1999 worst drought Aug Sept Oct Aug Sept Oct N.Wello Bugna affected woredas. 91.2% 88.7% 89.7% 6.6% 10.6% 7.0% Wadla 91.1% 90.7% 90.6% 7.3% 5.5% 5.7% Gidan 88.4% 88.2% 88.4% 8.9% 8.2% 9.6% S. Wello Delanta Dawnt 87.5% 87.6% 87.5% 11.0% 8.0% 7.6% Dessie Zuria 91.9% 90.9% 90.1% 4.0% 6.2% 5.2% Tenta 89.2% 89.1% 88.4% 10.0% 8.3% 11.7% Legambo 89.1% 89.7% 89.6% 8.4% 6.0% 6.4% Wag Hamra Dehana 89.7% 89.5% 90.5% 8.3% 8.2% 6.4% Amhara March- May SCF-UK + 2500 50 clusters in Mean WFL < 80% WFL N.Wello 2000 worst drought March 2000 May 2000 March 2000 May 2000 Bugna affected woredas 90.1% 90.6% Wadla 90.5% 91.1% S. -
The Case of Damot Gale District in Wolaita Zone, Ethiopia) Zegeye Paulos Borko* Department of Economics, Wolaita Sodo University, PO Box 138, Wolaita Sodo, Ethiopia
onomic c s & f E o M Borko, Int J Econ Manag Sci 2017, 6:5 l a a n n a r g u e DOI: 10.4172/2162-6359.1000450 o m J International Journal of Economics & e l n a t n S o i c t i a ISSN: 2162-6359 e n n r c e t e s n I Management Sciences Research Article Research Article Open Access Child Labor and Associated Problems (The Case of Damot Gale District in Wolaita Zone, Ethiopia) Zegeye Paulos Borko* Department of Economics, Wolaita Sodo University, PO Box 138, Wolaita Sodo, Ethiopia Abstract The study was carried out at Damot Gale district of Wolaita zone in Southern nation nationalities regional state with the main objectives to describe factors of child labor in the study area. In order to attain this objective the study made use of cross-sectional household survey data collected from 94 sample households. The data collected were analyzed and discussed by using both descriptive statistics and binary logit regression model. To this end, identifying children’s who were in child labor and those who were not in child labor; descriptive result shows that from different age category 73% of the children’s were engaged in different activity and the remaining 27% responded as they were not working. Most children’s started working below the age of 8 and major sectors of work were unpaid family work such as agriculture 58% Male and 7.24% Female and Home service 7.3% Female and 22% male. The result of the logistic regression model revealed that out of 8 variables included in the model, 4 explanatory variables were found to be significant at 1%, 5% and 10% level. -
Prevalence and Economic Significance of Hydatidosis in Bovine Slaughtered at Kindo Koysha Woreda Municipality Abattoir, Ethiopia
International Journal of Research Studies in Biosciences (IJRSB) Volume 6, Issue 7, 2018, PP 31-37 ISSN No. (Online) 2349-0365 DOI: http://dx.doi.org/10.20431/2349-0365.0607005 www.arcjournals.org Prevalence and Economic Significance of Hydatidosis in Bovine Slaughtered at Kindo Koysha Woreda Municipality Abattoir, Ethiopia Dawit Dana Wolaita sodo univeresity, School of veterinary medicine *Corresponding Author: Dawit Dana, Wolaita sodo univeresity, School of veterinary medicine Abstract: A cross-sectional study of bovine hydatidosis was conducted in Bele Municipality Abattoir from December, 2016 to August, 2017 to estimate the prevalence and economic impact of hydatidosis in cattle slaughtered at Bele Municipality Abattoir. Abattoir survey of hydatidosis was conducted during routine meat inspection activity on randomly selected 384 cattle encountered at Bele Municipality Abattoir. Ante-mortem examination was conducted to note the breed, age, sex and body condition of study animals. These animals were given number in order to identify them during the postmortem examination. Post mortem examination was conducted to note the presence of hydatid cysts. A total of 56 (14.57%) cattle were affected with hydatid cyst. The study shows that there was significant variation in the prevalence of hydatid cyst in different peasant association (p<0.05). However, no such association was observed in prevalence of hydatid cysts according to sex, age, breed and body condition of slaughtered animals (p=0.499, p=0.086, p=0.613, p=0.140, respectively). The study period of the economic loss due to organ condemnation associated to bovine hydatidosis at the abattoir was estimated 6,720.00 Ethiopian Birr. -
Factors That Influencing the Profitability of Saving and Credit Cooperatives: the Case of Boloso Sore District, Wolaita Zone, Southern Region, Ethiopia
European Journal of Business and Management www.iiste.org ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) DOI: 10.7176/EJBM Vol.11, No.19, 2019 Factors that Influencing the Profitability of Saving and Credit Cooperatives: The Case of Boloso Sore District, Wolaita Zone, Southern Region, Ethiopia Yisehak Ossa Jokka Lecturer, College of Business and Economics, Wolaita Sodo University, Wolaita Sodo, Ethiopia Abstract Savings and credit cooperatives are user-owned financial intermediaries. They have many names around the world, including credit unions, SACCOs, etc. Members typically share a common bond based on a geographic area, employer, community, or other affiliation. Members have equal voting rights, regardless of how many shares they own. Savings and credit are their principal services, although many offer money transfers, payment services, and insurance as well. Sometimes savings and credit cooperatives join together to form second -tier associations for the purposes of building capacity, liquidity management, and refinancing. The study was undertaken to identify factors influencing of profitability of saving and credit cooperative in Boloso Sore Woreda Wolaita zone, Southern Ethiopia .The general objective of the study was to identify factors that influencing the profitability of saving and credit cooperatives. A multistage sampling technique was employed to select 305 households from the in order to respond questionnaire . Both quantitative and qualitative data were collected from sampled households. Both descriptive and econometric data analyses techniques were applied. The findings of this study showed that, from the eleven independent variables seven of them significantly affect the profitability of saving and credit cooperative were age, education status, training access, family size, loan repayment, saving habit and service delivery. -
Understanding Farmers: Explaining Soil and Water Conservation in Konso, Wolaita and Wello, Ethiopia
Document2 29-01-2003 15:29 Pagina 1 Understanding Farmers Explaining soil and water conservation in Konso, Wolaita and Wello, Ethiopia Tesfaye Beshah .tesfaye.thesis 29-01-2003 15:12 Pagina 1 Understanding Farmers Explaining Soil and Water Conservation in Konso, Wolaita and Wello, Ethiopia Tesfaye Beshah 2003 .tesfaye.thesis 29-01-2003 15:12 Pagina 2 Promotoren Prof. Dr. Ir. N.G. Röling Hoogleraar Landbouwkennissystemen in Ontwikkelingslanden, Wageningen Universiteit Prof. Dr. Ir. L. Stroosnijder Hoogleraar in de erosie en bodem- en waterconservering, Wageningen Universiteit Promotiecommissie Prof. Dr. Wouter de Groot, Katholieke Universiteit Nijmegen Prof. Dr. Ir. Tom Veldkamp, Wageningen Universiteit Prof. Dr. Leontine Visser, Wageningen Universiteit Prof. Dr. Ir. Cees Leeuwis, Wageningen Universiteit Prof. Dr. Paul Richards, Wageningen Universiteit .tesfaye.thesis 29-01-2003 15:12 Pagina 3 Understanding Farmers Explaining Soil and Water Conservation in Konso, Wolaita and Wello, Ethiopia Tesfaye Beshah Proefschrift ter verkrijging van de graad van doctor op gezag van de rector magnificus van Wageningen Universiteit, Prof. Dr. Ir. Speelman, in het openbaar te verdedigen op maandag 17 februari 2003 des namiddags om vier uur in de Aula .tesfaye.thesis 29-01-2003 15:12 Pagina 4 Tesfaye Beshah (2003) Understanding Farmers: Explaining soil and water conservation in Konso, Wolaita and Wello, Ethiopia. PhD Thesis, Wageningen University and Research Centre ISBN 90-5808-795-6 Copyright © 2003 Tesfaye Beshah Cover Photo: Lake Maybar catchment, South Wello, Ethiopia .tesfaye.thesis 29-01-2003 15:12 Pagina 5 To Asnakech, Wondimagegn, Gebriel and Tsega .tesfaye.thesis 29-01-2003 15:12 Pagina 7 Preface This doctoral dissertation deals with soil and water conservation (SWC) practices that are vital to Ethiopia’s agriculture and economy. -
Under Peer Review
UNDER PEER REVIEW 1 PARTICIPATORY VARIETIES SELECTION AND EVALUATION OF IMPROVED 2 SWEET POTATO (Ipomoea Batatas (L.)VARIETIES ON-FARM AT 3 DIFFERENT AGRO-ECOLOGIES IN WOLAITA ZONES 4 5 6 Abstract: 7 Ethiopia is considered to be the one of the major producer of sweet potato and involves major 8 lands for this purpose. Whereas, varieties of sweet potato that yields maximum are not yet 9 known. So this particular study is aimed to identify the variety for high yield, quality sweet 10 potato, as well as its quantitative evaluation to be done. 11 12 Materials and methods: Area situated at Wolaita zone of SNNP regional state is considered 13 as the major study venue; whereas performance of fields are evaluated and specified the 14 number of crop yield of that particular area. 15 16 Result and Discussion: Eleven sweet potato varieties and four local varieties were prepared 17 for the preliminary evaluation. Observations came up with the following varieties namely 18 OFSP1, Kulfo, koka 6 and Hawassa 83 which were mostly preferred by farmers. The 19 varieties were divided into four sets, with each set having two test varieties and the check 20 variety. This is to ensure that farmers will not have difficulty in evaluating and comparing too 21 many varieties. 22 23 Conclusion: Variety of the potatoes that are preferred by the farmers is different from each 24 other, which are come up with Participatory varietal selection (PVS) technique. So as 25 differences in ranking are also preferred by them, which later ensure the genetically diverse factors 26 and differentials in growing yield of the crops. -
Demography and Health
SNNPR Southern Nations Nationalities and Peoples Demography and Health Aynalem Adugna, July 2014 www.EthioDemographyAndHealth.Org 2 SNNPR is one of the largest regions in Ethiopia, accounting for more than 10 percent of the country’s land area [1]. The mid-2008 population is estimated at nearly 16,000,000; almost a fifth of the country’s population. With less than one in tenth of its population (8.9%) living in urban areas in 2008 the region is overwhelmingly rural. "The region is divided into 13 administrative zones, 133 Woredas and 3512 Kebeles, and its capital is Awassa." [1] "The SNNPR is an extremely ethnically diverse region of Ethiopia, inhabited by more than 80 ethnic groups, of which over 45 (or 56 percent) are indigenous to the region (CSA 1996). These ethnic groups are distinguished by different languages, cultures, and socioeconomic organizations. Although none of the indigenous ethnic groups dominates the ethnic makeup of the national population, there is a considerable ethnic imbalance within the region. The largest ethnic groups in the SNNPR are the Sidama (17.6 percent), Wolayta (11.7 percent), Gurage (8.8 percent), Hadiya (8.4 percent), Selite (7.1 percent), Gamo (6.7 percent), Keffa (5.3 percent), Gedeo (4.4 percent), and Kembata (4.3 percent) …. While the Sidama are the largest ethnic group in the region, each ethnic group is numerically dominant in its respective administrative zone, and there are large minority ethnic groups in each zone. The languages spoken in the SNNPR can be classified into four linguistic families: Cushitic, Nilotic, Omotic, and Semitic. -
Ethiopia: Administrative Map (August 2017)
Ethiopia: Administrative map (August 2017) ERITREA National capital P Erob Tahtay Adiyabo Regional capital Gulomekeda Laelay Adiyabo Mereb Leke Ahferom Red Sea Humera Adigrat ! ! Dalul ! Adwa Ganta Afeshum Aksum Saesie Tsaedaemba Shire Indasilase ! Zonal Capital ! North West TigrayTahtay KoraroTahtay Maychew Eastern Tigray Kafta Humera Laelay Maychew Werei Leke TIGRAY Asgede Tsimbila Central Tigray Hawzen Medebay Zana Koneba Naeder Adet Berahile Region boundary Atsbi Wenberta Western Tigray Kelete Awelallo Welkait Kola Temben Tselemti Degua Temben Mekele Zone boundary Tanqua Abergele P Zone 2 (Kilbet Rasu) Tsegede Tselemt Mekele Town Special Enderta Afdera Addi Arekay South East Ab Ala Tsegede Mirab Armacho Beyeda Woreda boundary Debark Erebti SUDAN Hintalo Wejirat Saharti Samre Tach Armacho Abergele Sanja ! Dabat Janamora Megale Bidu Alaje Sahla Addis Ababa Ziquala Maychew ! Wegera Metema Lay Armacho Wag Himra Endamehoni Raya Azebo North Gondar Gonder ! Sekota Teru Afar Chilga Southern Tigray Gonder City Adm. Yalo East Belesa Ofla West Belesa Kurri Dehana Dembia Gonder Zuria Alamata Gaz Gibla Zone 4 (Fantana Rasu ) Elidar Amhara Gelegu Quara ! Takusa Ebenat Gulina Bugna Awra Libo Kemkem Kobo Gidan Lasta Benishangul Gumuz North Wello AFAR Alfa Zone 1(Awsi Rasu) Debre Tabor Ewa ! Fogera Farta Lay Gayint Semera Meket Guba Lafto DPubti DJIBOUTI Jawi South Gondar Dire Dawa Semen Achefer East Esite Chifra Bahir Dar Wadla Delanta Habru Asayita P Tach Gayint ! Bahir Dar City Adm. Aysaita Guba AMHARA Dera Ambasel Debub Achefer Bahirdar Zuria Dawunt Worebabu Gambela Dangura West Esite Gulf of Aden Mecha Adaa'r Mile Pawe Special Simada Thehulederie Kutaber Dangila Yilmana Densa Afambo Mekdela Tenta Awi Dessie Bati Hulet Ej Enese ! Hareri Sayint Dessie City Adm. -
D.Table 9.5-1 Number of PCO Planned 1
D.Table 9.5-1 Number of PCO Planned 1. Tigrey No. Woredas Phase 1 Phase 2 Phase 3 Expected Connecting Point 1 Adwa 13 Per Filed Survey by ETC 2(*) Hawzen 12 3(*) Wukro 7 Per Feasibility Study 4(*) Samre 13 Per Filed Survey by ETC 5 Alamata 10 Total 55 1 Tahtay Adiyabo 8 2 Medebay Zana 10 3 Laelay Mayechew 10 4 Kola Temben 11 5 Abergele 7 Per Filed Survey by ETC 6 Ganta Afeshum 15 7 Atsbi Wenberta 9 8 Enderta 14 9(*) Hintalo Wajirat 16 10 Ofla 15 Total 115 1 Kafta Humer 5 2 Laelay Adiyabo 8 3 Tahtay Koraro 8 4 Asegede Tsimbela 10 5 Tselemti 7 6(**) Welkait 7 7(**) Tsegede 6 8 Mereb Lehe 10 9(*) Enticho 21 10(**) Werie Lehe 16 Per Filed Survey by ETC 11 Tahtay Maychew 8 12(*)(**) Naeder Adet 9 13 Degua temben 9 14 Gulomahda 11 15 Erob 10 16 Saesi Tsaedaemba 14 17 Alage 13 18 Endmehoni 9 19(**) Rayaazebo 12 20 Ahferom 15 Total 208 1/14 Tigrey D.Table 9.5-1 Number of PCO Planned 2. Affar No. Woredas Phase 1 Phase 2 Phase 3 Expected Connecting Point 1 Ayisaita 3 2 Dubti 5 Per Filed Survey by ETC 3 Chifra 2 Total 10 1(*) Mile 1 2(*) Elidar 1 3 Koneba 4 4 Berahle 4 Per Filed Survey by ETC 5 Amibara 5 6 Gewane 1 7 Ewa 1 8 Dewele 1 Total 18 1 Ere Bti 1 2 Abala 2 3 Megale 1 4 Dalul 4 5 Afdera 1 6 Awash Fentale 3 7 Dulecha 1 8 Bure Mudaytu 1 Per Filed Survey by ETC 9 Arboba Special Woreda 1 10 Aura 1 11 Teru 1 12 Yalo 1 13 Gulina 1 14 Telalak 1 15 Simurobi 1 Total 21 2/14 Affar D.Table 9.5-1 Number of PCO Planned 3. -
AFET-22 Appeal Target: US$ 27,030,875 Balance Requested from ACT Network: US$ 26,995,619
150 route de Ferney, P.O. Box 2100 1211 Geneva 2, Switzerland Appeal Tel: 41 22 791 6033 Fax: 41 22 791 6506 e-mail: [email protected] Ethiopia Coordinating Office Assistance to Drought Affected - AFET-22 Appeal Target: US$ 27,030,875 Balance Requested from ACT Network: US$ 26,995,619 Geneva, 21 November 2002 Dear Colleagues, According to the report released in September 2002 by the Ethiopia Federal Disaster Preparedness and Prevention Commission (DPPC), over 6.8 million people in Ethiopia are facing severe food shortages during the current period, with the numbers expected to rise to about 10 million people in the early part of 2003. The Ethiopian Prime Minister, Meles Zenawi was quoted earlier this month as saying that the country faces a famine worse than that of 1984 which killed nearly one million people and sparked off huge international relief efforts. He was further quoted “if the 1984 famine was a nightmare, then this will be too ghastly to contemplate”. Whether this statement could be considered accurate or not, the truth of the matter is that Ethiopia is once more faced with a very severe food crisis which calls for the international community to intervene with massive food aid. As a country highly dependent on rain fed agriculture, this years’ partial or total failure of the belg (short rains) in many areas aggravated the problem of food insecurity. The meher rains (longer rains) also came late and were erratic affecting the long cycle crops and the availability of pasture and water supplies in many pastoral areas.