The water allocation problem is complex and requires a combination of regulations, policies, and mechanisms to support water management to minimize the risk of shortage among competing users. This paper compiles the application of multi-criteria decision-making (MCDM) related to water allocation. In this regard, this paper aims to identify and to discern the pattern, distribution of study regions, water problem classifications, and decision techniques application for a specific water allocation problem. We applied a systematic literature review study from 2000 to 2019 by using four literature databases (Web of Science, Scopus, Science Direct, and Google Scholar). From 109 papers, 49 publications have been identified and information extracted. This study reveals that in the past two decades the application of MCDM in the area of water allocation has increased particularly after 2014. Around 65% and 12% of study papers were conducted in Asia and Europe, respectively. Water shortage, water use management, and water quality were consecutively the most top-ranked discussed water problems. NSGA II (non-dominated sorting genetic algorithm), GA (genetic algorithm), and LP (linear programming) are the more often applied decision methods to solve water allocation problems. The key findings of this study provide guidelines for future research studies.

Selecting an appropriate landfill site is one of the major problems of towns and cities. This study was aimed to map the optimum suitable landfill sites in Gondar town, Ethiopia. To achieve the objectives, a GIS-based multi-criteria evaluation method was employed. Nine-criteria were considered to select appropriate landfill sites. The result of the suitability map shows that about 4.5% of the study area is highly suitable for the landfill site. The result of this study could be used as an initial input for further detailed identification of landfill site selection to enhance the waste disposal management of Gondar town.

Land suitability analysis is a prerequisite for sustainable agricultural production. This study was aimed at evaluating the current physical land suitability for major cereal crops: teff, wheat, barley and maize in the Dabo-Hana district, Bunno Bedelle Zone, Southwest Ethiopia. A GIS and RS technique with a multi-criteria evaluation approach was applied for evaluating the physical land suitability for the major crops. Various physical land attributes, namely temperature, rainfall, altitude, slope, soil (soil depth, PH, texture, and drainage), land use land cover, accessibility to market and proximity to road have been used as input parameters. Physical land suitability maps were generated for the major cereal crops. The optimum vector overlay analysis results revealed that from the total area, 15,298 ha (20.34%) and 12,494 ha (16.8%) is highly suitable and moderately suitable for all teff, wheat, barley, and maize crop production, respectively. The remaining area has a mixed suitability status (suitable for some crops and unsuitable for others) for all the analyzed crops. This study demonstrated that based on physical land suitability analysis the study area has huge potential for cereal crop production. Topographic factor (altitude) and climatic factors (temperature and rainfall) are the dominant factors that influence the suitability of agricultural land for the major crops in the study area. This study report urges the concerned stakeholders to properly use and adopt precisely the optimum physical land suitability planning to expend the present land resources for more cereal crop productivity in a sustainable manner for better socio-economic development of the region.

Ensete plant (Enset ventricosum) is a monocarpic perennial crop, found under genus Ensete. It is mainly known and planted by farmers as a drought resistant crop and staple food in the rural community of southern Ethiopia. Large numbers of enset clones are growing in a wide range of altitude. Currently, enset plant diversity and production has been endangered by devastated enset bacterial wilt disease (EBW) which is caused by Xanthomonas campestris pv. musacearum. The study aimed at to assess the spatial distribution of enset clone richness and diversity, and enset bacterial wilt disease in Yem special district of southern Ethiopia using ArcGIS geostatistical (Kriging) techniques. The highest diversity was recorded in Semu Awash kebele with Shannon index of 3.38 and the lowest was in Nuba kebele an index of 2.56. The Sorenson similarity/variation indices showed that Gurmina Hangeri/Meleka and Ediya/Gurmina Hangeri each pairs of kebeles share 18 clones with index value of 0.81 and 0.67, respectively. While lowest share recorded between Laignaw (upper) Kesheli and Tachignaw (lower) Kesheli kebeles with Sorenson indices of 0.16 (only five clones in common recorded). Enset bacterial wilt disease is distributed in all sampled kebeles in varying ranges. The rate of disease prevalence, incidence and severity lies between ranges of 75–100%, 46.8–88.9%, and 36.1–81.4%, respectively. Semu Awasho and Laignaw Kesheli kebeles scored 100% prevalence while in Shemona Metelo kebele it is scored 75%. Therefore, the analyzed results indicate that it is necessary to compact EBW distribution and to conserve enset richness and diversity in the district for the sake of enhancing food security of farmers at household level in Yem special district. Otherwise, in the longer run the food security and enset plant species would be endanger and it may have significant impact on socio-economic development of the rural community. This study considered limited bio-physical factors for EBW analysis, we recommend in further research in the future should be conducted including other socio-biophysical factors.

This study applied the Gebre optimization model to optimize the land and water usage in the Omo-Gibe river basin, Ethiopia, where competition among stakeholders and growing demands pose a challenge. This model was applied through a nexus approach to maximize benefits and minimize conflicting trade-offs. The main objective was to maximize the economic benefit from land and water allocation with the framework of the land-water-food-energy-environment nexus under climate change mitigation and river ecosystem services (LWFEEN). This model takes into account multiple dimensions, including economic, environmental, social, and technical factors, going beyond ordinary optimization models. It also incorporates an innovative crop succession allocation concept not often seen in the literature. This crop succession proposal includes sequences of cropping patterns and fallow land use options that closely resemble real-world farming practices. The results demonstrated that the Gebre optimization model effectively resolves the existing constraint conflicts and maximizes economic benefits by reducing costs, penalties, and environmental impacts, promoting sustainable use of natural resources in the Omo-Gibe river basin and avoiding conflicts among stakeholders. Therefore, this study offered decision-makers a strategic perspective on how to apply the Gebre-model within the context of the land-water-food-energy-environment nexus(LWFEEN) approach in river basins such as the Omo-Gibe, with the ultimate goal of achieving sustainable development.