Prevalence of ixodid ticks infesting Raya cattle breeds in Semi-arid areas of Raya Azebo district, northern Ethiopia

A cross-sectional study was conducted from January to December, 2016 in Semi-arid areas of Raya Azebo District with the objectives to estimate the prevalence of ixodid ticks and assessing the difference in infestation among the different host risk factors such as age, sex and body condition scores. A total of 2697 adult ixodid ticks were collected from 488 Raya cattle breed selected randomly. Tick species were identified morphologically and the prevalence of the infested animals was estimated in relation to sex, age and body condition score. The study revealed that cattle in the study area were infested with atleast one or more ticks with an overall prevalence of 90% (405/448). Six tick species that belongs to the genera of Rhipicephalus (54%), subgenus Boophilus  (5%), Amblyomma (3%) and Hyalomma (2.7%) were also identified. The overall prevalence of ticks on cattle with the age from 6 months to 2 years, 3-5 years and greater than 5 years was 90%, 90%, and 92% respectively. A prevalence of 91% in female and 90% in male cattle was also observed. Similarly, the prevalence of ticks was 100 %, 90.7% and 70% in poor, medium and good body conditioned animals, respectively. The high prevalence and presence of many species of ticks may damage the hide and skins of the cattle and thereby contribute to reduced income from cattle. Hence, appropriate control measures using acaricides, improved cattle management practices and community awareness creation are recommended.Keywords: Body condition; Cattle; Prevalence; Semi-arid; Tic

Evaluation of Yield and Nutraceutical Traits of Orange-Fleshed Sweet Potato Storage Roots in Two Agro-Climatic Zones of Northern Ethiopia

This study evaluated the genotype by environment interactions in the yield and nutraceutical traits of the orange-fleshed sweet potato (OFSP) storage root in different agro-climatic zones of northern Ethiopia. Five OFSP genotypes were cultivated at three different locations following a randomized complete block design, and the yield, dry matter, beta-carotene, flavonoids, polyphenols, soluble sugars, starch, soluble proteins, and free radical scavenging activity were measured in the storage root. The results showed consistent variations in the nutritional traits of the OFSP storage root depending on both the genotype and the location, as well as on their interaction. Ininda, Gloria, and Amelia were the genotypes that provided the higher yield and dry matter, as well as the higher content of starch and beta-carotene; they also showed a high antioxidant power. These findings suggest that the studied genotypes have the potential to alleviate vitamin A deficiency. This study demonstrated a high possibility of sweet potato production for storage root yield in arid agro-climate regions with limited production inputs. Moreover, the results suggest that it is possible to enhance the yield, dry matter content, beta-carotene, starch, and polyphenols of the OFSP storage root through genotype selection

Tree Height Estimation from Unmanned Aerial Vehicle Imagery and Its Sensitivity on Above Ground Biomass Estimation in Dry Afromontane Forest, Northern Ethiopia

Tree height is a parameter useful for calculating above-ground forest biomass and is mostly measured traditionally by ground survey. On the other hand, measuring the forest tree height and biomass estimation through field survey is labor-intensive and time-consuming. The application of remote sensing for forest above-ground biomass (AGB) estimation without forest destruction is important in order to estimate the carbon sequestration potential of the forest. The unmanned aerial vehicle (UAV) is an elating technology, which can help to estimate tree height and it is evolving at a rapid speed. Moreover, assessing the relationship between estimated and measured tree height is necessary for the future application of estimated tree height on AGB estimation. However, tree height estimation from photogrammetric UAV imagery in the dry Afromontane Forest and its sensitivity to AGB estimation are not investigated. Thus, this study aimed to assess the accuracy of tree height estimated from photogrammetric UAV imagery and the sensitivity of the estimated tree height on AGB estimation. Photogrammetric UAV acquired images and sample trees height measured on the ground were collected in Desa’a dry Afromontane Forest, Northern Ethiopia. Tree height was estimated from photogrammetric UAV acquired images and compared with tree heights measured on the ground. Moreover, the sensitivity of the estimated tree height on AGB estimation was investigated. The estimated tree height explained 89% of the tree height measured in the field. A considerable difference between estimated and measured tree height has an insignificant effect on AGB estimation. Thus, in the dry land Afromontane Forest the application of UAV aerial imagery for tree height estimation is promising to estimate AGB.   &nbsp

Human impact on sediment fluxes within the Blue NBile and Atbara River basins

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.status: publishe

Hyper-temporal SPOT-NDVI dataset parameterization captures species distributions

Hyper-temporal SPOT NDVI images contain useful information about the environment in which a species occurs, including information such as the beginning, end, peak, and curvature of photosynthetically active vegetation (PAV) greenness signatures. This raises the question: can parameterization of hyper-temporal SPOT NDVI images be useful to predict species distribution? A set of SPOT-NDVI images for the whole of Ethiopia covering nine years was classified using the unsupervised ISODATA clustering algorithm to group similar NDVI pixel values. The HANTS (Harmonic ANalysis of Time Series) algorithm, that fits series of smoothing cosine waves, was then applied to the time series for each of the NDVI classes to generate seven output Fourier components. These components, together with the topographic parameters slope and elevation, were used as predictors in a species distribution model using MAXENT. Presence-only data of one test species, Boswellia papyrifera, were modelled. This species is diminishing at an alarming rate and requires conservation. The performance of the model was evaluated by the area under curve (AUC) of the receiver-operating characteristics value. The output distribution map was tested for its agreement with the NDVI-clustering approach and conventional B. papyrifera distribution map using Kappa. The relative contributions of the first four predictors to the MAXENT in sequence were: 2nd harmonic phase, elevation, amplitude of the 1st harmonics, and amplitude of the 2nd harmonics. The average AUC test result for the 100 runs was 0.98 with a standard deviation of 0.002. The probability distribution map clearly shows high correlation with the B. papyrifera occurrence data. In addition, the distribution map was found to be in agreement with the NDVI-clustered and conventional map with improved details. Classifying hyper-temporal NDVI images and extracting their parameters through the use of the HANTS algorithm captures the PAV greenness behaviour (parameters) of the environment of the species studied. These parameters have proved successful in predicting the distribution of B. papyrifera

Predictors of drought-induced crop yield/losses in two agroecologies of southern Tigray, Northern Ethiopia

The consequences of prolonged precipitation-deficient periods are primarily substantial water deficit. The spatial characteristics of drylands and various socioeconomic factors worsen droughts’ impacts and deepen poverty among agrarian communities, with attendant food security (stability dimension) implications. This study utilizes a combination of climate, remote sensing and field survey data to obtain first-hand information on the impacts of recent (2015 and 2017) droughts on crop yield in southern Tigray, northern Ethiopia. Annual and seasonal rainfall, annual and seasonal Normalized Difference Vegetation Index (NDVI) and Deviation of NDVI (Dev-NDVI), and monthly Standardized Precipitation Index (SPI) (SPI-1, SPI-3 and SPI-12) for June to October, were considered as likely factors that could relate with yield and yield loss in the area. Correlation and multiple linear stepwise regression statistical techniques were used to determine drought-yield relationships, and identify more accurate predictors of yield and yield losses in each of the drought years. The area witnessed a more widespread precipitation deficit in 2015 than in 2017, where the lowland area recorded entire crop (sorghum) losses. Also, droughts manifested spatiotemporal variations and impacts across the two different agroecologies—primarily reduction in vegetation amounts, coinciding with the planting and maturing stages of barley and sorghum. Crop failures, therefore, translated to food shortages and reduced income of smallholder farmers, which denotes food insecurity in the time of droughts. Seasonal rainfall and June Dev-NDVI predicted 66.9% of 2015 barley and sorghum yield-loss, while NDVI predicted 2017 sorghum yield by 96%. Spate irrigation should be further popularized in the low-lying areas of Raya Azebo to augment for future deficiencies in the kiremt rainfall

Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia : a remote sensing approach

This study aims at characterizing agricultural drought in Ethiopia and understanding the effects of drought on crop yield. Monthly, seasonal and annual Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) values were calculated using MODIS (MOD13Q1) from the year 2003 to 2017. The relationships between NDVI, VCI, and crop yield were examined to predict the possibility of drought impacts on crop productivity. We found that VCI and NDVI data provides consistent and spatially explicit information for operational drought monitoring in Ethiopia. Results also indicated that the most extreme agricultural drought in recent years occurred in 2003, 2004, 2008, 2009, and 2015. These findings also show that mild to severe droughts have a great chance of occurrence in Ethiopia. However, only severe drought has significant impacts on crops. The food crops yield data used in this study include cereals, legumes, and tubers. It was observed that cereals such as (Zea mays), teff (Eragrostis tef), haricot beans (Phaseolus vulgaris) are more sensitive to agricultural drought when compared to the tubers such as sweet potato (Ipomoea batatas) and taro (Colocasia esculenta). Thus, drought preparedness programs need to pay more attention to the cultivation of these crops under severe drought conditions