Semiarid Region Of Ethiopia Research Articles

Integrated effects of furrow irrigation systems and irrigation regimes on water productivity of onion (Allium Cepa L.) crop in a semi-arid area

Dwindling water resources with inept field water management impends sustainable agricultural production in semi-arid areas of Ethiopia. Thus, the adaption and adoption of improved irrigation water running lines are indispensable and imperative to recuperate water productivity in areas where water resources are inadequate. The ground experiments were pointed to elucidate the weight of conventional, alternate, and fixed furrow irrigation systems under irrigation levels of 100%, 70%, and 40% of crop evapotranspiration (ETc) on onion growth, yield, water productivity, and economic return in Alage, Central, Ethiopia. The field trials were conducted at nine treatments arranged in a randomized complete block design with three replications and water was managed at fixed intervals with variable depth approach. The result illustrated that furrow systems in conjunction with irrigation levels had a weighty pertain (P < 0.05) on onion growth, yield, water productivity, and economic return. The highest yield of 30.75 ton ha-1 was achieved at conventional furrow irrigation with 100%ETc dictated to zero water saving subsequently, the crop and irrigation water productivity dropped with the values of 6.86 and 5.61 kg m-3 respectively. The diminished yield of 6.39 ton ha-1 obtained at fixed furrow irrigation with 40%ETc conquered the saved water output lastly, the crop and irrigation water productivities demoted with values of 6.87 and 5.70 kg m-3 respectively. Nevertheless, 24.93ton ha-1 of ideal yield was obtained under alternative furrow irrigation with 100%ETc and the 50% hoarded water was higher than the yield decline. Consequently, the crop and irrigation water productivities rose with values of 10.96 and 9.09 kg m-3 respectively. Eventually, the saved water opened for 0.03ha extra irrigable area that will onward to the highest total expected yield of 37.40 ton ha-1 as compared with all other treatments. Under conventional furrow irrigation with 100%ETc, the obtained yield dominated the wastewater accordingly, the highest net return of 668484ETB ha-1 and benefit-cost ratio of 3.63 was achieved. Besides, the unbearable yield penalty under fixed furrow with 40% headed to negative net return and lowest befit cost ratio as related with other treatments. However, further economic results confirmed that the 50% saved water under alternative furrow with 100% can recompense for the net return reduction that will make this trial combination preferable as compared with other treatments. Other results also recommended that, under water-limiting and inefficient field water governing conditions, alternative furrow irrigation with 100%ETc can be the ideal blend to enhance water productivity in semi-arid regions of Ethiopia and elsewhere that share similar agroclimatic conditions.

Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine

In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (Apis mellifera ssps.) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9%) than RF (88.2%), CART (85.5%), or SVM (79.9%). Nonetheless, the highest overall accuracy (94.7%) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8%, 6.5%, 9.2%, and 14.8% compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.

Exploring stress-tolerant plant growth-promoting rhizobacteria from groundnut rhizosphere soil in semi-arid regions of Ethiopia

ABSTRACT The potential of rhizobacteria with plant growth promoting (PGP) traits in alleviating abiotic stresses, especially drought, is significant. However, their exploitation in the semi-arid regions of Ethiopian soils remains largely unexplored. This research aimed to isolate and evaluate the PGP potential of bacterial isolates collected from groundnut cultivation areas in Ethiopia. Multiple traits were assessed, including phosphate solubilization, indole-3-acetic acid (IAA) production, ammonia production, salt and heavy metal tolerance, drought tolerance, enzyme activities, hydrogen cyanide production, antibiotic resistance, and antagonistic activity against fungal pathogens. The identification of potent isolates was carried out using MALDI-TOF MS. Out of the 82 isolates, 63 were gram-negative and 19 were gram-positive. Among them, 19 isolates exhibited phosphate solubilization, with AAURB 34 demonstrating the highest efficiency, followed by AURB 12. Fifty-six isolates produce IAA in varying amounts and all isolates produce ammonia with AAURB12, AAURB19, and AAURB34 displaying strong production. Most isolates demonstrated tolerance to temperatures up to 40°C and salt concentrations up to 3%. Notably, AAURB12 and AAURB34 exhibited remarkable drought tolerance at an osmotic potential of −2.70 Mpa. When subjected to levels above 40%, the tested isolates moderately produced lytic enzymes and hydrogen cyanide. The isolates displayed resistance to antibiotics, except gentamicin, and all isolates demonstrated resistance to zinc, with 81–91% showing resistance to other heavy metals. AAURB34 and AAURB12 exhibited suppression against fungal pathogens, with percent inhibition of 38% and 46%, respectively. Using MALDI-TOF MS, the promising PGP isolates were identified as Bacillus megaterium, Bacillus pumilus, and Enterobacter asburiae. This study provides valuable insights into the potential of rhizobacteria as PGP agents for mitigating abiotic stresses and contribute to the understanding of sustainable agricultural practices in Ethiopia and similar regions facing comparable challenges.

Effect of Deficit Irrigation on Wheat (Triticum aestivum L.) Yield and Water Use Efficiency in the Semi-Arid Region of Awash Basin, Ethiopia

Crop production is largely limited by water availability in arid and semi-arid regions of Ethiopia. Changing climate conditions and declining water resources demand appropriate approaches to improve crop yield and water use efficiency through a reduced and more reliable water supply. A field experiment was conducted to evaluate the effect of limited irrigation water use on bread wheat production and water use efficiency under the semi-arid climate conditions of Awash basin of Ethiopia. Five irrigation levels, that is, full irrigation (100% ETc/control), 85% ETc, 70% ETc, 55% ETc, and 40% ETc, were evaluated using a randomized complete block design (RCBD) with four replicates. Statistical analysis has shown a significant effect of irrigation levels on wheat grain yield, water use efficiency, economic profit, wheat grain quality, and aboveground biomass. The highest grain yield (5,085 kg ha−1) was obtained from 100% ETc irrigation application (i.e. 417.2 mm of water), and the lowest grain yield was obtained from 40% ETc (i.e. 223.7 mm of water) application. A deficit level of 85% ETc resulted in a yield that was comparable to that of full irrigation. Compared to other treatments, the 70% ETc application produced the highest water use efficiency (1.42 kg m−3). Using the saved water obtained from 70% ETc deficit irrigation application, 23.4% more wheat could be produced on 1.38 ha of land, resulting in the highest profit (US$2,563.9) and higher MRR (137%). The yield response factor and crop-water production function indicated that maintaining irrigation at optimal levels can prevent potential yield reductions. Consequently, a 70% ETc deficit irrigation application was found to be optimal for increasing wheat grain yield, water use efficiency, and economic benefits from irrigated wheat production. These results suggest that deficit irrigation for wheat under semi-arid climatic conditions is a viable irrigation management option for enhancing water use efficiency.

Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia

Abstract Groundwater availability is one of the key anxieties in most semi-arid regions of Ethiopia. The purpose of this study was to investigate the groundwater potential zone map of the alluvial plain of Gambela. The study applied analytic hierarchy process (AHP) models with four different machine learning algorithms: random forest classifier (RFC), gradient boosting classifier (GBC), decision tree classifier (DTC), and K-neighbor classifier (KNC). The features that are used as predictors include geology, geomorphology, slope, soil, lineament density, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic roughness index (TRI), and rainfall. The final output of the groundwater potential zone was classified as low, moderate, high, and very high potential zones. The authentication through receiver operating curve (ROC) shows 78.2, 93.4, 92.5, 72.4, and 87.7% values of area under the curve (AUC) for AHP, RFC, GBC, DTC, and KNC, respectively. The results show that RFC and GBC are the best groundwater potential zone (GWPZ) map estimator. The study also shows that rainfall and geomorphology are the primary factors influencing the GWPZ. The outcome might promote improved management alternatives in other areas of the country with a comparable climate.

Impacts of Climate Variability and Change on Sorghum Crop Yield in the Babile District of Eastern Ethiopia

The impacts of various climatic conditions, such as temperature and rainfall variabilities, are very critical and sensitive to rain-fed crop production, particularly over the water stress arid and semi-arid regions of Ethiopia. This study was designed to evaluate the potential impact of climate variability and change on sorghum grain yield in the Babile district of eastern Ethiopia. The study was conducted based on observed and model-based simulated projected rainfall and temperature obtained from the Ethiopian Meteorological Institute and General Circulation Models (GCM) used by the Intergovernmental Panel on Climate Change (IPCC) of the Fifth Assessment Report CMIP5) and Agricultural Model Inter-comparison and Improvement Project (AgMIP). Three GCM models, namely GFDLESM2M, CanESM2, and HadGEM2-ES under RCP4.5, were considered to generate future climate projections for the near-term 21st century. Various univariate and multivariate statistical techniques were employed to compute and identify whether the impacts of climate variability and change on rain-fed sorghum crop performance were reasonably viable over the districts where grain yield is highly stable and productive under normal climate conditions. Our findings revealed that more stable and better rainfall performance from May to September, the season when sorghum crops are normally planted in the Babile district, was positively correlated, while the maximum and minimum temperatures of the season were negatively correlated with sorghum grain yield. A significant association has been detected between sorghum grain yield and its growing period rainfall, number of rainy days, and maximum and minimum temperature with multi-regression analysis. Thus, the variability of rainfall in August, June temperature, and the number of rainy days in September significantly impacted sorghum crop productivity. As a result, the multi-regression model adjusted R-squared indicated that 77% variance in annual sorghum yield performance was explained by rainfall and temperature conditions that prevailed during the crop growing period. During the past period, there was a significant increase in sorghum yields, which are projected to decline during the near term of the 21st century in the future. This revealed that declining and disturbed rainfall performance and increases in temperature are likely to reduce overall sorghum grain yield in the Babile district. We recommend that there is a need to enhance awareness for smallholder farmers on the adverse impact of climate variability and change on sorghum grain yield. In view of this, the farmers need to be geared toward employing climate-smart agriculture as a possible adaptation measure to reduce the negative impacts of climate variability and change on rain-fed crop production practices in the Babile district and other arid and semi-arid parts of eastern Ethiopia.

A geo-spatial assessment of drought impacts on forest cover in yabello forest, in the semi-arid region of Ethiopia

Drought is a key challenge for third-world countries whose economies are based on agriculture. Also, lack of rainfall plays a major role in accelerating climate change. Drought is resulting in a decline in vegetation greenness and plant mortality specifically in the semi-arid region. In this study, satellite-based drought assessment was used to characterize drought and its impact on forests in the semi-arid regions of Ethiopia. Drought severity and forest cover change were detected over the period between 2014 and 2022 Using geographic information systems and remote sensing techniques. Vegetation condition index (VCI) and Normalized difference vegetation index (NDVI) were used to detect drought severity in the present study. Results of this study showed that 2017, 2015, 2022 and 2019 were the most drought years. The forest resource is significantly decreased during severe drought years. The Minimum dense forest was observed in 2017 and 2015 over a spatial extent of 193 ha and 217 ha respectively during drought years. To evaluate how precipitation deficit affects forest function, simple linear regression is performed. Results of this linear regression “R2 = 0.49 and P = 0.05” indicated that nearly 50% of forest greenness is influenced by precipitation deficit. This study suggested that remote sensing indices are very crucial to characterize drought patterns at a small “scale”, particularly in the assessment of drought impact on forest resources.Article HighlightsSatellite indices like NDVI and VCI help to monitor the drought impact on forest cover.When severe drought is occurred along with precipitation deficit, the forest cover is decreased.Remote sensing data provide clear information for decision-making regarding drought's impact on the forest.

Modeling cloud seeding technology for rain enhancement over the arid and semiarid areas of Ethiopia

The government of Ethiopia has started exploring different innovative approaches to tackle the scarcity of water in arid and semi-arid regions of the country. In line with this strategy, precipitation enhancement through weather modification technology is getting strong attention and some initial attempts have been made to assess its feasibility. Therefore, this paper aims to model cloud-seeding technology for rain enhancement and check its effectiveness in arid and semiarid regions of Ethiopia. Different relevant measurements including ground-based as well as reanalysis data from 2021 to 2022 are used to improve relevant cloud-seeded models. Reanalysis data are validated with ground-based data using different error metrics. The improved cloud-seeded modeling is developed for precipitation enhancement for the arid and semiarid regions of Ethiopia. An atmospheric moisture budget is used for improving the cloud-seeded model. The results indicated that the developed model and the direct operation are well agreed upon. The relative precipitation (RP) (after the application of cloud-seeded per before the application of cloud-seeded during spring, summer, and autumn is found 1.31, 0.98, and 1.03 respectively. The changing precipitation between cloud seeded and before seeded for spring, summer, and autumn is found at 1.38, −0.19, and 0.11 mm respectively; whereas changing temperature is found at 1.08, 1.78, and −1.06 k respectively. In general, the model result indicated that cloud-seeded technology is effective over Ethiopia when the daily resultant wind speed is less than 1.5 m/s and cloud base height (CBH) is less than 1700 m. Furthermore, by observing RP from the improved cloud-seeded model results, rain enhancement science is applicable for Ethiopia during the spring and slightly autumn seasons. Hence, before artificial aerosol is seeded into the cloud, the operators should be nowcast and forecast the daily wind speed and CBH of the target area unless an economic crisis will have happened.

Traditional groundwater exploration method for pastoralist community water supply system in semi-arid region of Ethiopia: case of Tula Sallan Borana, Southern Ethiopia

Traditional groundwater exploration method for pastoralist community water supply system in semi-arid region of Ethiopia: case of Tula Sallan Borana, Southern Ethiopia

Predicting Maize (Zea mays) productivity under projected climate change with management options in Amhara region, Ethiopia

Maize is an important cereal crop in Ethiopia. Maize yield has been declined in Ethiopia mainly due to water scarcity, low soil fertility and heat stress. Currently, limited technologies are available in the study region that increases maize productivity. If crop models are properly calibrated, they are effective tools to study crops responses to environmental factors. Assessing impact of future climate on maize crop may help to develop adaptation strategies. The objectives of this study were (1) to calibrate and evaluate the CERES maize model in DSSAT technology for simulating phenology and yield of maize (2) to assess impact of projected climate change on maize productivity (3) to develop promising management practices for maize. The impact of projected climate change was assessed using the 17 GCMs (CMIP5) run under RCP4.5 and RCP8.5 climate scenarios. Two water regimes treatments (rainfed and irrigated) and four rates of nitrogen (0, 46, 92 and 138 kg ha−1) were evaluated individually and in combinations for their effectiveness to increase maize productivity under the projected climate conditions. The model evaluation result revealed that the RMSE values were 2.5 days for anthesis, 4.4 days for physiological maturity, 258.3 kg ha−1 for grain yield and 1034 kg ha−1for above ground biomass yield with nRMSE values of 3%, 4%, 4.7% and 10%, respectively. The d-index values were 0.87, 0.80, 0.88 and 0.71 for the respective parameters. The good agreement between the simulated and the measured values indicated the maize genetic coefficients in the model were properly calibrated. The simulation results at Tehuledere site showed that maize grain yield may decrease by 11% and 20% in 2030s and by 26% and 29% in 2050s under RCP4.5 and RCP8.5 scenarios, respectively whereas in Kallu site yield may decrease by 13% and 15% in 2030s and by 17% and 19% in 2050s for the respective RCP scenarios. However, the management scenarios have shown that maize yield may substantially increase by the use of optimum nitrogen fertilizer and supplemental irrigation. Thus, it can be generalized that climate change may adversely affect maize production in the Semi-arid regions of Ethiopia but the impact could be reversed by using sound crop management practices.

Sorghum yield response to NPKS and NPZn nutrients along sorghum-growing landscapes

Summary Grain sorghum [Sorghum bicolor (L.) Moench] is the major cereal crop used as staple crop in the arid and semi-arid regions of Ethiopia. Low sorghum yields are attributed to soil, climate and topographic factors. We investigated sorghum yield response to factorial combination of nitrogen and phosphorous (NP) as well as potassium (K), sulphur (S) and zinc (Zn), and how the position of farmers’ fields belonging to different landscape positions (i.e., upslope, mid-slope, and foot slope) could explain fertilizer response and yield variability. The analysis in this study made use of dataset from two sets of on-farm experiments where trials were set at two farmers’ fields for NPKS and three farmers’ fields for NPZn experiments in each landscape position. The experiments were implemented at two sorghum-growing locations (i.e., Hayk and Sirinka) in parts of the north-eastern Amhara region in Ethiopia. Sorghum yield response to fertilizer application was strongly linked to the spatial variation along landscape positions and varied over locations. Fertilizer response was significantly higher at foot slopes compared to mid-slopes and upslope positions, where fields at foot slopes exhibited relatively homogeneous responses. Application of combined nitrogen (N) and phosphorus (P) fertilizers, landscape position and the interaction of fertilizer application and landscape positions strongly affected sorghum yield. There was a linear and significant increase in sorghum yield with the increase in the NP rates. The combined application of NP with different levels of KS as well as NP with Zn fertilizer rates did not result in significant yield difference. The results indicated that local factors were much more influential when accounting for the heterogeneity in sorghum yield response to fertilizer. This further acknowledges the importance of a landscape-based fertilizer management approach to respond yield potential variability related with the farmers’ fields and landscape environment. Further investigation is needed to develop homogeneous fertilizer response units based on spatial variability of soil and topographic attributes along the landscape.

Crop productivity and tree growth in intercropped agroforestry systems in semi-arid and sub-humid regions of Ethiopia

Agroforestry has been practiced traditionally by smallholder farmers in tropical countries, including Ethiopia. However, scientific information on tree-crop interactions isn’t widely available. Long-term agroforestry field experiments were conducted, in sub-humid and semi-arid regions of Ethiopia, to explore the impacts of intercropping on crop yield and tree growth. The treatments in the sub-humid areas were; tree alone (Cordia, Grevillea, Croton, and Acacia abyssinica), (2) crop alone, teff (Eragrostis tef), maize (Zea mays) and finger millet (Eleusine coracana), (3) tree + crop, (4) mix of four tree species and (5) mix of tree species + crops. The treatments in the semi-arid area included (1) tree + crop (teff with Faidherbia, Moringa, Acacia nilotica and Cordia, (2) crop alone, and (3) mix of trees + crops. The species choice was determined by suitability to local contexts and from farmer prioritisation participatory design workshops. Crop yield, tree height, and diameter were measured from each treatment. Crop yields under tree + crop treatments were not significantly different (α = 0.05) from crop yield in the sole crop treatments (in both areas), except crop yields under Acacia abyssinia, which had the lowest crop yields compared to the rest of the treatments in the sub-humid areas. In the semi-arid area, teff yield under Faidherbia in 2019, has increased by 64%, compared to the yield in 2017. The best growth performance was attained by Grevillea and Cordia in the sub-humid areas, whereas Acacia nilotica performed well in the semi-arid areas. These results suggest that the selection of appropriate tree and crop species can enhances crop and tree production in agroforestry systems of Ethiopia.

Adoption of Road Water Harvesting Practices and Their Impacts: Evidence from a Semi-Arid Region of Ethiopia

In the drylands of Ethiopia, several road water harvesting practices (RWHP) have been used to supplement rain-fed agriculture. However, factors affecting adoption of RWHP and their impacts were not studied systematically. Understanding the factors influencing the adoption of RWHP for sustainable agricultural intensification and climate resilience is critical to promoting such technologies. This paper investigates the impacts of using rural roads to harvest rainwater runoff and the factors causing farmers to adopt the practice. Road water harvesting is considered a possible mechanism for transformative climate change adaptation. By systematically capturing rainfall with rural road infrastructure, rain-related road damage is reduced, erosion and landscape degradation due to road development is lessened, and farm incomes increase due to the beneficial use of harvested water, resulting in an increased climate change resilience. This paper uses a binary probit model and propensity score matching methods based on a household survey of 159 households and 603 plots. The results of the probit model show that the education level of the household, family labor, access to markets, and distance of the farming plot from the farmer’s dwelling are statistically significant in explaining farmers’ adoption of RWHP in the study area. The casual impact estimation from the propensity score matching suggests that RWHP has positive and significant impacts on input uses (farmyard manure and fertilizer), crop yield, and farm income among the sample households.

Contributions of water harvesting technologies intervention in arid and semi-arid regions of Ethiopia, in ensuring households’ food security, Tigray in focus

Interventions of water harvesting technologies (WHTs) in drought-prone areas like the Tigray region (northern Ethiopia) is an option less strategy to alleviate food insecurity issues emanating from water scarcity. Hence, wide spectrums of WHTs were applied in Tigray Region in the last three decades. Thus, this study aimed to assess the WHTs and the subsequent contributions in ensuring households’ food security in Kilete-Awlaelo district of the Tigray Region. Quantitative and qualitative data were collected through a household survey (n = 246), focus group discussion, key informants interview, and field observation, and subjected to descriptive and inferential statistical analysis on the SPSS environment. The explanatory power of the WHTs to food security was determined using a regression model. The result revealed that 64.6% of the households applied WHTs while 35.4% not. Though a statistically significant positive relationship (p < 0.05) between the WHTs (predictor) and PCC acquisition as food security parameter (predictand) was observed, the magnitude was not strong enough where only 6.1% of WHTs users and 0.81% of non-users had achieved the average standard PCC requirement (2100 kcal) from their farm production. Hence, strengthening and expanding the functional domain of the WHTs fitting to the socio-economic, environmental, and biophysical context of the locality is profoundly indispensable.

Field-based investigation on phytoremediation potentials of Lemna minor and Azolla filiculoides in tropical, semiarid regions: Case of Ethiopia

ABSTRACTThis study investigated the concurrent accumulation of eight heavy metals by two floating aquatic macrophytes (Lemna minor and Azolla filiculoides) cultivated in ambient media and blended wastewaters in the semiarid regions of Ethiopia. Both species accumulated heavy metals in varying degrees with a significant concentration gradient within the immediate water media. Highest bioconcentration factor (BCF) was determined for Mn and Fe in both plants. Results revealed that L. minor was high phytoaccumulator for Fe, Mn, Zn, and Co but moderate for Cd, Cu, Ni, and Cr. On the other hand, A. filiculoides was a high accumulator for Fe, Mn, Zn, and Cu, but its potency was moderate for Co, Cr, and Ni, but lower for Cd. Both species exhibited significant difference in accumulating Co, Zn, and Mn (p < 0.05). In general, the BCFs for both plants were comparable within the same treatment. In this study, stronger associations between the heavy metal concentrations in the plant tissues and in the grown water media were observed for A. filiculoides.

Anthropogenic impact on land use land cover: influence on weather and vegetation in Bambasi Wereda, Ethiopia

Land Use Land Cover Changes (LULCC) has impacts on a wide range of environmental and landscape attributes including the quality of land, water and air. The main objective of this study was to analysis the impact of anthropogenic activities and LULCC on weather (rainfall and temperature) and vegetation in the study area over a period of 28 years. The study has employed temporal LANDSAT sensors data to identify the changes in LULC and Vegetation Indices through anthropogenic influence. Moreover, statistical analysis of temperature and rainfall data (1985–2015) has also been done of Bambasi station. It has been found that the average temperature has been increased approximately 2.2 °C and the average rainfall amount was declined throughout the period. The drastic changes have been noticed in LULC, vegetation health and its area through NDVI during the first interval (1987–2001) but the land change growth was less in second interval (2001–2015). The increasing population, urbanization and resettlement scheme for refugees were the responsible factors for changes. This case study indicates that the impact of anthropogenic activities leads the change in LULC and the climate has been influenced or vice versa over the time period in this semi-arid region of Ethiopia.

Spatial mapping and testing the applicability of the curve number method for ungauged catchments in Northern Ethiopia

Understanding the spatial variability of land and water resources has significant importance for its planning, management, and utilization. It is also significant in understanding the response behavior of a catchment in order to model the basic physical processes. In this study, a weighted overlay analysis technique using ArcGIS was implemented for developing a geo-database of the standard curve number (SCN) in a catchment around Northern Ethiopia. The spatial data were used to investigate a 'standard curve number method' for the simulation of the direct runoff at the outlet of the catchment. Both spot based rainfall and runoff measuring techniques were adopted for deriving an instant observed flow measurement, and to make a comparison with the simulated flow values. The results showed that the model underestimated most of the simulated values with a coefficient of regression of R2 = 0.52, with a proportion of higher variances between the simulated and observed runoff events. The result suggests that the accuracy of the model leaves room for significant improvement and the method could not be easily adopted in the catchment and other similar catchments in the semi arid regions of Ethiopia. For improving the prediction capacity of the model, further research in adjusting loss factors in the method is recommended. It is also suggested for developing a localized and modified SCN values by considering geologic, climatic and seasonal variation. The results of this study and the maps generated can be used for improving the hydrological understanding of the catchment. The study is useful for further investigation of the SCN methodology in other un-gauged catchments around the world.

Morphological dynamics of gully systems in the subhumid Ethiopian Highlands: the Debre Mawi watershed

Abstract. Gully expansion in the Ethiopian Highlands dissects vital agricultural lands with the eroded materials adversely impacting downstream resources, for example as they accumulate in reservoirs. While gully expansion and rehabilitation have been more extensively researched in the semiarid region of Ethiopia, few studies have been conducted in the (sub)humid region. For that reason, we assessed the severity of gully erosion by measuring the expansion of 13 selected permanent gullies in the subhumid Debre Mawi watershed, 30 km south of Lake Tana, Ethiopia. In addition, the rate of expansion of the entire drainage network in the watershed was determined using 0.5 m resolution aerial imagery from flights in 2005 and 2013. About 0.6 Mt (or 127 t ha−1 yr−1) of soil was lost during this period due to actively expanding gullies. The net gully area in the entire watershed increased more than 4-fold from 4.5 ha in 2005 to 20.4 ha in 2013 (&gt; 3 % of the watershed area), indicating the growing severity of gully erosion and hence land degradation in the watershed. Soil losses were caused by upslope migrating gully heads through a combination of gully head collapse and removal of the failed material by runoff. Collapse of gully banks and retreat of headcuts was most severe in locations where elevated groundwater tables saturated gully heads and banks, destabilizing the soils by decreasing the shear strength. Elevated groundwater tables were therefore the most important cause of gully expansion. Additional factors that strongly relate to bank collapse were the height of the gully head and the size of the drainage area. Soil physical properties (e.g., texture and bulk density) only had minor effects. Conservation practices that address factors controlling erosion are the most effective in protecting gully expansion. These consist of lowering water table and regrading the gully head and sidewalls to reduce the occurrence of gravity-induced mass failures. Planting suitable vegetation on the regraded gully slopes will in addition decrease the risk of bank failure by reducing pore-water pressures and reinforcing the soil. Finally, best management practices that decrease runoff from the catchment will reduce the amount of gully-related sediment loss.

Best Bet Technology Package Development to Improve Sorghum Yields in Ethiopia Using the Decision Support System for Agro-Technology Transfer (DSSAT) Model

Sorghum is grown mainly in the semi-arid areas. In spite of the fact that there was observed high climate variability in the last few decades, rain fed sorghum [Sorghum bicolor (L.) Moench] production is still an important source of food and feed in the semiarid regions of Ethiopia. Although sorghum is realized as crop tolerant to water deficit, compared with other semiarid crops in Ethiopia, climate variability and change has been challenging its production and no intensive crop simulation modeling was done as it was desired. In this study the CERES-Sorghum Model of Decision Support System for Agro-Technology Transfer (DSSAT) has been tested over the north Rift Valley of Ethiopia. We have checked what would be the best combination of management options under research and farmers’ practice conditions for each sites for the historical climatological periods (1980-2010) in which we have found that the model performs well in assimilating the real situation in our sentinel sites in both research and farmers’ management practices. The potential grain yield from the DSSAT model would go up to 2.5T/ha under best scenario rainfall seasons without applying the developed technology package application (which we call it farmer’s condition). The same sorghum variety has a potential yield of 6.2 T/ha if one can apply the recommended best bet technology packages (planting date, planting population, sowing data, fertilizer application rate and time) within the same season. Hereby we can assert that the application of the developed technology packages would make a difference of up to 3.7 T/ha of grain sorghum yield under the same season. Even though applying the technology packages according to the prevailing seasons would significantly matter the expected grain yield, the worst possible grain yield lose would be minimized by applying the best bet technology packages that fits the specific season. Moreover, the selected sentinel sites were few, the result can be extrapolated using the calibrated crop simulation modeling to larger areas to develop strategic plans to improve grain yield of sorghum in Ethiopia.

Pastoral livelihoods under pressure: Ecological, political and socioeconomic transitions in Afar (Ethiopia)

The Afar pastoralists that reside in arid and semi-arid regions of Ethiopia have fallen under increasing pressure as rangelands and natural resources are affected by recurrent droughts, overgrazing, erosion processes, alien plant invasion and governmental land policies. This paper investigates the impact of these environmental, institutional and cultural changes on natural resource management strategies, using empirical research undertaken in four villages of western Afar (Ethiopia) to assess the related challenges to local livelihoods. Qualitative interviews with various stakeholders reveal that the authority and use of traditional common property regimes have been considerably diminished and traditional livelihood practices threatened. Many pastoralists have adopted agriculture in a move away from pure pastoralism to agro-pastoralism, a transition exaggerated by changing property rights and the Federal Government's sedentarisation program, which is presented as a means of reducing poverty. On-going land privatisation and an increased government presence in the region weaken indigenous institutions and cultural practices, with no clear local understanding of the impact on future generations and Afar identity.