Irrigation practices, state intervention and farmer’s life: Worlds in drought-prone Tigray

Irrigation practicesWater usersDamsIrrigation canalsIrrigation managementWater allocation

TRANSBOUNDARY WATER COOPERATION IN AFRICA: THE CASE OF THE NILE BASIN INITIATIVE (NBI)

The aim of this paper is to identify the economic, social and political benefits of the transboundary cooperation by using the Nile Bain Initiative (NBI) as a case study. It also attempts to identify the obstacles that hinder transboundary cooperation in the Nile Basin. The paper argues that the riparian states in the Nile Basin should work for “benefit-sharing” rather than “water-sharing” and this should be the basis for the transboundary cooperation. It also claims that implementing the concept of benefit-sharing would help in solving problems that are caused by divergent interests among the riparian states in the Nile basin and the up stream-down stream problems frequently manifested in the area. The paper concludes by suggesting the main points that have to be considered in transboundary cooperation.“benefit-sharing”, Nile Basin Initiative, transboundary cooperation, “water-sharing.”

Rural Communities\u27 Vulnerability to Farmland Poverty in Varied Ecological Settings of Northwest Ethiopia

Environmental and climate changes are among the serious threats to the world\u27s land resources in the 21st Century. Particularly, in the developing countries the impact inevitably goes as the continuing toll on agricultural production, human lives, and properties. It is also a driving force of poverty and impediment of overall economic development in many less developed nations, like Ethiopia. Therefore, this paper assesses the rural communities\u27 vulnerability to farmland poverty in different ecological settings of northwest Ethiopia. Data were collected from 525 randomly selected farming households using questionnaire. Meteorological data were collected from Global Weather Data for soil and water assessment tool (SWAT) from 1979 to 2010. Rainfall and temperature trends were characterized using simple linear regression model. Rural communities\u27 vulnerability to farmland poverty was determined using livelihood vulnerability index (LVI). Indices were constructed using simple and weighted average approaches to measure farmlands\u27 exposure, sensitivity and adaptive capacity. Overall communities\u27 levels of vulnerability to farmlands poverty were found to be 0.76 in the lowland, 0.57 in the flat highland and 0.51 in the midland areas. In almost all indicators the lowland (Abay Valley) is more vulnerable to farmland-related troubles as the biophysical and socio-economic contexts were found to be the worst there. Communities and government and non-government officials have observed significant negative impacts of drought and extreme weather events on farmlands, pasturelands with declining availability, productivity and quality of farmlands. This study suggests education and research interventions for enhancing community-based participatory integrated watershed management approach supported with best indigenous knowledge and farmers\u27 practices. Adaptation interventions should also consider local communities\u27 resource capacity (low-cost investment in sound farmland and soil management techniques)

Cambio de actitudes hacia la MGF en Finlandia

Algunas exrefugiadas están trabajando actualmente como educadoras profesionales en las comunidades de migrantes y refugiados en Finlandia para abordar la ignorancia sobre el impacto y alcance de la ablación/mutilación genital femenina

A novel active learning technique for multi-label remote sensing image scene classification

Copyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.This paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL technique redefines active learning by evaluating the informativeness of each image based on its multiple land-cover classes. Accordingly, the proposed MLAL technique is based on the joint evaluation of two criteria for the selection of the most informative images: i) multi-label uncertainty and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the multi-label classification algorithm in correctly assigning multi-labels to each image, whereas multi-label diversity criterion aims at selecting a set of un-annotated images that are as more diverse as possible to reduce the redundancy among them. In order to evaluate the multi-label uncertainty of each image, we propose a novel multi-label margin sampling strategy that: 1) considers the functional distances of each image to all ML-SVM hyperplanes; and then 2) estimates the occurrence on how many times each image falls inside the margins of ML-SVMs. If the occurrence is small, the classifiers are confident to correctly classify the considered image, and vice versa. In order to evaluate the multi-label diversity of each image, we propose a novel clustering-based strategy that clusters all the images inside the margins of the ML-SVMs and avoids selecting the uncertain images from the same clusters. The joint use of the two criteria allows one to enrich the training set of images with multi-labels. Experimental results obtained on a benchmark archive with 2100 images with their multi-labels show the effectiveness of the proposed MLAL method compared to the standard AL methods that neglect the evaluation of the uncertainty and diversity on multi-labels.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart

Scaling out sweetpotato and potato-led interventions to improve nutrition and food security in Tigray and SNNPR, Ethiopia

This flyer is a brief of the ‘Scaling out sweetpotato and potato-led interventions to improve nutrition and food security in Tigray and SNNPR’ project, which is being implemented in the Southern Nations, Nationalities and Peoples’ Region (SNNPR) and the Tigray region in the north of Ethiopia. At present, the project covers a total of 75 kebeles (villages) in 20 woredas (districts) in the two regions. It summarizes the objectives, achievements and lessons of the project between June 2014 and July 2015

Climate Variability, Communities\u27 Perceptions and Land Management Strategies in Lay Gayint Woreda, Northwest Ethiopia

Climate variability is the fluctuation of climatic elements from the normal or baseline values. Agrarian communities are the most sensitive social groups to climate variability and associate extreme weather-induced hazards due to the fact that climate variability affects the two most important direct agricultural production inputs, such as rainfall and temperature. As Ethiopia is heavily dependent on agriculture its economic development is being hindered by climate variability coupled with many other deriving forces. Therefore, the objective of this study is to examine climate variability, local communities\u27 perceptions and land management strategies to reduce the adverse impact of climate variability in Lay Gayint Woreda, Ethiopia. Both primary and secondary data were used to complete this study. Primary data were collected and analyzed from a total of 200 randomly selected respondents reside in different agro-ecological areas. Metrology data were gathered from Nefas Mewcha Station from the years 1979 to 2010. Standardized rainfall anomaly index (SRAI), crop diversification index (CDI) and other descriptive statistical techniques were used to analyze the data. The results obtained from the climate data revealed an increase in temperature, and decrease and/or erratic in rainfall distribution. Time series SRAI from 1979 to 2010 indicates that 2002 and 2008 were characterized by extreme and severe dry conditions in order of importance with high impact on crop yields whist only 1984 and 1990 received near normal rainfall amount. Similarly, the survey result reveals that out of the total household heads, 87.5 % perceived that there was an increase in temperature over the last 20 years. The survey result also disclosed that significant numbers of households are more likely to adopt different land management strategies to reduce the negative impact of climate variability. Constructing terraces and check dams as well as planting trees were the major land management strategies used by the local communities. However, crop diversification index (CDI) was found to be 0.11 as the cultivated area is stanch to one crop indicating very low alternative crop production in the study area. Although the study area receives variable and inefficient rainfall the rugged topography and poor soil conditions have hindered the development of irrigation facilities. Local context-specific integrated watershed management activities, small-scale irrigation schemes and extension services need to be strengthened to reduce the impact of climate variability. Policy makers need also to substantially invest in establishing information dissemination systems in order to provide reliable weather information for farmers given that crop production is largely dependent on it

Markov Chain Modeling of Daily Rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Information on seasonal Kiremet and seasonal Belg rainfall amount is important in the rain fed agriculture of Ethiopia since more than 85% of the population is dependent on agriculture particularly on rain fed farming practices. The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence. A two-state Markov chain was used to describe the characteristics of rainfall occurrences in this woreda. The states, as considered were; dry (d) and rainy (r). The overall chance of rain and the fitted curve tells us that the chance of getting rain in the main rainy season is about twice as compared to the small rainy season. The first order Markov chain model indicates that the probability of getting rain in the small rainy season is significantly dependent on whether the earlier date was dry or wet. While the second order Marko chain indicates that the main rainy season the dependence of the probability of rain on the previous two dates\u27 conditions is less as compared with the small rainy season. Rainfall amounts are very variable and are usually modeled by a gamma distribution. Therefore, the pattern of rainfall is somewhat unimodial having only one extreme value in August. Onset, cessation and length of growing season of rainfall for the main rainy season show medium variation compared to the small rainy season