Multi-criteria decision analysis for wind farm location selection in Bahir Dar City and its surroundings, Northwestern Ethiopia.

While there is a consensus on the multifaceted advantages of wind farms, only a handful of developing countries harness it to the fullest potential. Among the various factors that contributed to the low development of wind farms in those countries, the dearth of methods for identifying a suitable site is the leading one. Studies conducted elsewhere on wind farm industry identifies criteria such as slope, wind speed, land use/land cover, geology, distance from electric transmission grids, roads, and settlements. However, the relative magnitude of each factor to the optimum location of the wind farm is inadequately examined. This study focuses on the combined use of geographic information systems (GIS) and spatial multi-criteria decision analysis (MCDA) for selecting the most appropriate sites for wind-farm development. Using a pairwise comparison method of the analytical hierarchical process, we computed the relative importance and weight of parameters affecting wind farm sites in a weighted overlay technique. Based on the analysis of pertinent data collected from Bahir Dar City and its surroundings, northwestern Ethiopia, we categorized the study area into five suitability classes ranging from extremely suitable to not suitable. Our result, revealed a spatial pattern of the potential sites suitable for wind farm investments that are economical, environmentally sound, and aesthetically amenable. We recommend replicating the method developed in this study in other areas of developing countries where transitioning to renewable energy sources through windfarm development is sought.

Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North Wollo, Ethiopia.

Agriculture is the most sensitive sector which has largely been affected by the impacts of drought. The study aims to detect and characterize agricultural droughts using MODIS-based multiple indices in North Wollo, Ethiopia. Two Moderate Resolution Imaging Spectroradiometer (MODIS) datasets (MOD13Q1 and MOD11A2) for the period 2000 to 2019 were used to generate Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Accordingly, NDVI anomaly, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) were computed to characterize agricultural droughts during the crop growing season. Both the NDVI anomaly and VCI confirmed that there was no single drought-free year in the area throughout the study period. TCI showed relatively exaggerated drought stress than the other indices. However, VHI indicated lower area coverage and a lower level of stress than its aggregates (VCI and TCI). Specifically, 2002, 2004, 2009, 2010, and 2015 were all identified as severe drought years, where over 60% of the area was affected by droughts. Results of the regression analysis indicated that VCI, TCI, and VHI were having significant positive trends with precipitation in the majority of the districts. Using the aggregated drought frequency of each index, 13.5, 73.7, and 12.8% of the area were under moderate, high, and extremely high levels of agricultural drought occurrence, respectively, and the likelihood of implied risks. Therefore, all the districts of North Wollo were affected by persistent drought stress. Such drought recurrences have the potential to impose significant impacts on the agro-based livelihoods of the local community demanding ongoing drought monitoring and the application of effective early warning systems.

Agricultural livelihood resilience in the face of recurring droughts: Empirical evidence from northeast Ethiopia.

The main purpose of this study was to characterize the livelihood resilience of smallholder farmers in the face of recurring droughts in northeast Ethiopia. The data was collected using a cross-sectional survey of 274 households and five focus group discussions. Principal component analysis and multiple linear regression models were employed to analyze the data. The Livelihood Resilience Index (LRI), framed on absorptive, adaptive, and transformative capacities, was used to quantify the households' livelihood resilience. The results indicated that about 57% of the surveyed respondents were non-resilient, while 43% were resilient to different degrees. Abay Tekeze watershed (ATW) livelihood zone exhibits the highest proportion of resilient households (57.4%), while North Wollo highland belg has the lowest proportion (22.7%). The high resilience in ATW was attributed to the relatively lower persistence of droughts, better accessibility in enabling institutions, more access to agricultural inputs, and the training and support given to farmers. The better-off were more resilient (90.9%) than the medium (52.1%) and the poor (34.6%) households. Among the latent dimensions, sensitivity with ß value -0.372, climate change and variability (-0.33), and enabling institutions and environments (0.288) showed a significant (p < 0.0001) influence on LRI. This was followed by adaptive capacity and food access (0.249), agricultural practice and technology (0.213), and asset possession (0.19), in respective order. It implies that the absorptive capacity of households showed the leading influence in determining LRI, while adaptive and transformative capacities had nearly similar low effects. Thus, it is recommended that future planning for building livelihood resilience and drought risk interventions in the area should address the levels of resilience identified and the relative importance of each latent dimension indicated.

Woody species diversity and regeneration status of Sub-Alpine forest of Mount Adama exclosure site, Northwestern highlands of Ethiopia.

Forests provide a wide range of ecosystem services. Despite these facts, the expansion of agriculture and settlement at the expense of forest resources has threatened the forest resources and results in biodiversity loss. To halt this problem, various conservation practices that believed to restore the degraded lands and biodiversity of the country have been implemented. Area exclosure is among the conservation strategies that have been used to restore the degraded lands in Mount Adama forest. However, its role in woody species regeneration in Mount Adama was not investigated. Thus, the objective of the study was to evaluate the impact of area exclosure on woody plant species composition, regeneration status, structure and diversity in Mount Adama. A systematic transect sampling method was used to collect vegetation data. Hence, 53 plots with 400 m2 area were laid along 11 transects. Then, within the main plots, five subplots with 1 m2 were laid to determine the abundance and frequency of seedlings. The results showed that about 31 woody species that belong to 30 genera and 19 families including four endemic species were identified. The majority (67.74%) of the species were categorized under shrub habitat, while the remaining 19.35% and 12.90% were trees and lianas or climbers, respectively. Asteraceae family was dominant by contributing 4 species followed by Rosaceae and Solanaceae each contributed 3 species. Hypericum revolutum was the dominant species with 53.38 important value index followed by Erica arborea and Hagenia abyssinica with 49.12 and 40.05, respectively. The overall Shannon- Wiener diversity index and Shannon evenness in the exclosure site were 2.6 and 0.73, respectively. Furthermore, the number of seedlings and saplings were higher in the exclosure than the untreated site. The results of the study evidently showed that area exclosure that implemented in Mount Adam successfully contributed to the biodiversity restoration. Therefore, further conservation efforts targeting species with low IVI values are needed for sustainable management and ecological recovery of the area.

Spatial coverage of water hyacinth (Eichhornia crassipes (Mart.) Solms) on Lake Tana and associated water loss.

The introduction of water hyacinth poses a serious threat to economic viability of Lake Tana and its environments. This study aimed to capture the spatial coverage of water hyacinth and its effect on water loss in Lake Tana using quantitative research methods. Four satellite images representing each season of 2019 were downloaded from USGS. In addition, pan evaporation data were taken from the National Meteorological Agency. ArcGis 10.4, Envi 5.3, Qgis 3.12.1 plug in CSP and Excel used to manage land use land cover classification and water loss estimation analysis. The seasonal coverage of water hyacinth was 15.35, 4.14, 11.82 and 13.59 km2 in winter, autumn, summer and spring 2019 respectively representing 0.63 and 0.17 percent of the Lake as a maximum and minimum coverage. The mean daily evaporation of Lake Tana was 5.14 mm/day, but this increased to 18.85 mm/day due to the presence of water hyacinth. The mean net daily water loss due to water hyacinth at Lake Tana was 0.14 km2 while 52,62 km3 in 2019. The study concludes that water hyacinth caused enormous negative impact on the water volume reduction in Lake Tana. Management of the Lake Tana environment and control of the water hyacinth weed are recommended to sustain the Lake.

Combining Multi-Element Analysis with Statistical Modeling for Tracing the Origin of Green Coffee Beans from Amhara Region, Ethiopia.

Characterization of coffee terroirs is important to determine authenticity and provide confidence for consumers to select the right product. In this regard, Amhara Region, which is located at the northwestern part of Ethiopia, produces various local coffee types with distinct cup qualities. The coffees are, however, not yet registered with certification marks or trademarks for indications of their geographical origins. This study was aimed at developing analytical methodology useful to determine the geographical origin of green coffee beans produced in Amhara Region based on multi-element analysis combined with multivariate statistical techniques. For this, a total of 120 samples of green coffee beans, collected from four major cultivating zones (West Gojjam, East Gojjam, Awi, and Bahir Dar Especial Zones) were analyzed for K, Mg, Ca, Mn, Fe, Cu, Fe, Co, Ni, Zn, Si, Cr, Cd, and Pb using inductively coupled plasma-optical emission spectroscopy. The elemental analysis data were subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). PCA was used to explore the natural groupings of samples and the discriminatory ability of elements. Accordingly, the elements K, Mg, Ca, and Na were found to be the main discriminators among samples. LDA provided a model to classify the coffee samples based on their production zones with an accuracy of 94.2% and prediction ability of 93.4%. Thus, the elemental composition of green coffee beans can be used as a chemical descriptor in the authentication of coffee produced in Amhara Region.