48 research outputs found
Assessment of Genetic Variability and Heritability of Agronomic Traits of Ethiopian Chickpea (Cicerarietinum L) Landraces
No full textEthiopia has a large number of Desi type chickpea landraces. In the country, limited information is available on the performance of the landraces regarding of important agronomic traits. Thus, 202 chickpea landraces and two released varieties, Fetenech (early maturing) and Minjar (high yielding), were tested to evaluate the genetic variation and heritability for the selected agronomic traits. The experiment was conducted at Sirinka under rain fed condition in 2016 growing season using alpha lattice design with three replications. The data were collected, on days to 50 % flowering, days to 75 % maturity, plant height, number of pods per plant, number of seeds per pod, grain yield hectare and 100 seed weight, and analyzed by using SAS software. Analysis of variance showed highly significant difference (P<0.001) among the tested genotypes for all traits considered in the study, indicating the presence of genetic variability. Grain yield varied between 563 and 2794 kg/ ha. Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) ranged between 4.2 – 28.64% and 3.91 – 27%, respectively. The lowest PCV and GCV (4.2 and 3.91%) were obtained for days to maturity, while the highest PCV and GCV values (28.64 and 27%) were obtained for grain yield, respectively. High heritability and genetic advance as the percent of the mean were observed for grain yield, number of pods per plant and biomass yield. This indicates that these traits are governed by additive gene action, implying the possibility for genetic gain through selection. The findings of this study show that traits such as grain yield, number of pods per plant, biomass yield and hundred seed weight, which had high heritability coupled with relatively high values of GCV, and genetic advance as percentage of mean, are the most important traits, which could be improved by selection
Biodiversity of root-knot nematodes (Meloidogyne spp.) associated with tomato and characterisation and screening of tomato genotypes against Meloidogyne spp. in Ethiopia
No full textIdentification of Hydrologic Models, Inputs, and Calibration Approaches for Enhanced Flood Forecasting
No full textThe primary goal of this research is to evaluate and identify proper calibration approaches, skillful hydrological models, and suitable weather forecast inputs to improve the accuracy and reliability of hydrological forecasting in different types of watersheds. The research started by formulating an approach that examined single- and multi-site, and single- and multi-objective optimization methods for calibrating an event-based hydrological model to improve flood prediction in a semi-urban catchment. Then it assessed whether reservoir inflow in a large complex watershed could be accurately and reliably forecasted by simple lumped, medium-level distributed, or advanced land-surface based hydrological models. Then it is followed by a comparison of multiple combinations of hydrological models and weather forecast inputs to identify the best possible model-input integration for an enhanced short-range flood forecasting in a semi-urban catchment. In the end, Numerical Weather Predictions (NWPs) with different spatial and temporal resolutions were evaluated across Canada’s varied geographical environments to find candidate precipitation input products for improved flood forecasting.
Results indicated that aggregating the objective functions across multiple sites into a single objective function provided better representative parameter sets of a semi-distributed hydrological model for an enhanced peak flow simulation. Proficient lumped hydrological models with proper forecast inputs appeared to show better hydrological forecast performance than distributed and land-surface models in two distinct watersheds. For example, forcing the simple lumped model (SACSMA) with bias-corrected ensemble inputs offered a reliable reservoir inflow forecast in a sizeable complex Prairie watershed; and a combination of the lumped model (MACHBV) with the high-resolution weather forecast input (HRDPS) provided skillful and economically viable short-term flood forecasts in a small semi-urban catchment. The comprehensive verification has identified low-resolution NWPs (GEFSv2 and GFS) over Western and Central parts of Canada and high-resolution NWPs (HRRR and HRDPS) in Southern Ontario regions that have a promising potential for forecasting the timing, intensity, and volume of floods.ThesisDoctor of Philosophy (PhD)Accurate hydrological models and inputs play essential roles in creating a successful flood forecasting and early warning system. The main objective of this research is to identify adequately calibrated hydrological models and skillful weather forecast inputs to improve the accuracy of hydrological forecasting in various watershed landscapes. The key contributions include: (1) A finding that a combination of efficient optimization tools with a series of calibration steps is essential in obtaining representative parameters sets of hydrological models; (2) Simple lumped hydrological models, if used appropriately, can provide accurate and reliable hydrological forecasts in different watershed types, besides being computationally efficient; and (3) Candidate weather forecast products identified in Canada’s diverse geographical regions can be used as inputs to hydrological models for improved flood forecasting. The findings from this thesis are expected to benefit hydrological forecasting centers and researchers working on model and input improvements
Scaling of parasitic weed and faba bean gall disease management innovations on faba bean in South Wollo Zone, Ethiopia
No full textThis report presents key achievements of the CGIAR Sustainable Farming Science Program (Area of Work 4) aimed at enhancing faba bean productivity through integrated management of parasitic weeds and diseases, early generation seed multiplication, and capacity development in the South Wollo Zone of Ethiopia. During the 2024/25 main cropping season, scaling activities were implemented in Dessie Zuria, Kutaber, and Tehulederi districts, building on prior validations conducted under the CGIAR Plant Health Initiative in previous seasons. An integrated management package was demonstrated and scaled on farmers’ fields, combining the use of the partially Orobanche-resistant fababean variety Hashenge (ILB-4358), Fungicide fungicide seed treatment with Noble 25%WP for faba bean gall disease management, two applications of sub-lethal glyphosate at flowering stages, and manual weeding. Prior to implementation, farmers, development agents, and agricultural experts were trained on the proper application of the technologies. A total of 1 ton of Hashengie seed was distributed to 21 host farmers, including three female farmers, for on-farm demonstration and scaling. Field observations and farmer evaluations showed marked improvements in crop vigor, tillering capacity, pod load, seed size, and effective suppression of parasitic weeds mainly Orobanche crenata and faba bean gall disease. The estimated grain yield under the integrated management approach reached 2.8 t ha⁻¹, compared with an average of 0.5 t ha⁻¹ under traditional farmer practices. Two field days involving farmers, extension personnel, researchers, and agricultural experts confirmed strong stakeholder interest and commitment to further scaling of the technology. To support sustainability, early generation seed multiplication of the Hashengie variety was conducted at research stations. In addition, 41 farmers, six kebele development agents, and five agricultural experts enhanced their knowledge and skills in integrated faba bean pest and disease management. Overall, the results demonstrate that the integrated management package is an effective, farmer-accepted, and scalable solution for improving faba bean productivity in Orobanche-prone areas
Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds
No full textThe final publication is available at Elsevier via http://dx.doi.org/10.1016/j.advwatres.2018.05.013 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The objective of this study is to propose an event-based calibration approach for selecting representative semi-distributed hydrologic model parameters and to enhance peak flow prediction at multiple sites of a semi-urban catchment. The performance of three multi-site calibration approaches (multi-site simultaneous (MS-S), multi-site average objective function (MS-A) and multi-event multi-site (ME-MS)) and a benchmark at-catchment outlet (OU) calibration method, are compared in this study. Additional insightful contributions include assessing the nature of the spatio-temporal parameter variability among calibration events and developing an advanced event-based calibration approach to identify skillful model parameter-sets. This study used a SWMM5 hydrologic model in the Humber River Watershed located in Southern Ontario, Canada. For MS-S and OU calibration methods, the multi-objective calibration formulation is solved with the Pareto Archived Dynamically Dimensioned Search (PA-DDS) algorithm. For the MS-A and ME-MS methods, the single objective calibration formulation is solved with the Dynamically Dimensioned Search (DDS) algorithm. The results indicate that the MS-A calibration approach achieved better performance than other considered methods. Comparison between optimized model parameter sets showed that the DDS optimization in MS-A approach improved the model performance at multiple sites. The spatial and temporal variability analysis indicates a presence of uncertainty on sensitive parameters and most importantly on peak flow responses in an event-based calibration process. This finding implied the need to evaluate potential model parameters sets with a series of calibration steps as proposed herein. The proposed calibration and optimization formulation successfully identified representative model parameter set, which is more skillful than what is attainable when using simultaneous multi-site (MS-S), multi-event multi-site (MS-ME) or at basin outlet (OU) approach.Natural Sciences and Engineering Research Council of Canada [NETGP 451456
Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation
No full textThe transportation sector is recognized as one of the largest contributors to the problems of global warming and environmental pollution, and is responsible for a great deal of global energy consumption, which is heavily dependent upon scarce crude oil reserves. Different countries have adopted promotional policies to replace conventional internal combustion engine vehicles with electric vehicles as a means of mitigating global warming. Nevertheless, the current market share of eco-friendly vehicles remains stagnant in many parts of the world. This study aims to investigate the impact and relative importance of financial, technical, and political measures on the market penetration of electric vehicles using an agent-based simulation. More specifically, a series of agent-based simulation experiments is carried out following the statistical experimental design scheme to systematically assess the diffusion of electric vehicles. Affected by various factors and measures, the choice behavior of individual agents is modeled with a multinomial logit utility function of experimental factors. The simulated data are analyzed using different analysis methods, including full factorial analysis, response surface methodology, and support vector machine, in order to scrutinize the effects of different measures. It is advocated that factors affecting the choice of vehicle by individuals, including two-way interactions among various measures as well as policy measures such as purchase subsidies and tax breaks, have more significant effects on the widespread adoption of electric vehicles than do technical improvements in terms of battery charging times and driving mileage. This implies that the adoption of such measures needs to be carefully designed in order to account for potential interactions among individual measures as well as their main effects on the diffusion of electric vehicles
Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin
No full textThe hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byråns Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash–Sutcliffe efficiency and volume error index. Different time of concentration (Tc) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the Tc among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (FC) affects most the peak flow via the HBV-light model, whereas the MAXBAS parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin
Demonstration and Scaling of Integrated Management of Parasitic Weeds on Faba bean in Ethiopia, Morocco and Tunisia
No full textParasitic weeds (Orobanche spp.) severely hinder food legume production in the Mediterranean and East African highlands. To combat this issue, a multi-pronged approach was implemented in Ethiopia, Tunisia, and Morocco during the 2023/24 cropping season. This strategy involved integrating partially resistant faba bean varieties with sub-lethal herbicide application and mixed cropping techniques. In Ethiopia, the combined application of sub-lethal herbicides and fungicide seed treatments significantly boosted faba bean productivity, with widespread adoption by both male and female farmers. In Tunisia and Morocco, Corum herbicide application effectively reduced parasitic weed infestation on faba bean crops. To further drive innovation, the production of early-generation seeds of partially resistant varieties should be facilitated in Ethiopia and Tunisia. Additionally, exploring mixed cropping crops within the cropping system, as well as validating the use of Curum herbicide to control both parasitic and non-parasitic weeds in North Africa, are crucial steps for the 2024/25 cropping season.
Hydropower Energy Simulation Using Mike 11 Model; A Case Study In South Germany\u27s Small Run-Of-River Hydropower Plants
No full textRenewable energy production is a basic supplement to stabilize rapidly increasing global energy demand and skyrocketing energy price as well as to balance the fluctuation of supply from non-renewable energy sources at electrical grid hubs. The European energy traders, government and private company energy providers and other stakeholders have been, since recently, a major beneficiary, customer and clients of Hydropower simulation solutions. The relationship between rainfall-runoff model outputs and energy productions of hydropower plants has not been clearly studied. In this research, association of rainfall, catchment characteristics, river network and runoff with energy production of a particular hydropower station is examined. The essence of this study is to justify the correspondence between runoff extracted from calibrated catchment and energy production of hydropower plant located at a catchment outlet; to employ a unique technique to convert runoff to energy based on statistical and graphical trend analysis of the two, and to provide environment for energy forecast. For rainfall-runoff model setup and calibration, MIKE 11 NAM model is applied, meanwhile MIKE 11 SO model is used to track, adopt and set a control strategy at hydropower location for runoff-energy correlation. The model is tested at two selected micro run-of-river hydropower plants located in South Germany. Two consecutive calibration is compromised to test the model; one for rainfall-runoff model and other for energy simulation. Calibration results and supporting verification plots of two case studies indicated that simulated discharge and energy production is comparable with the measured discharge and energy production respectively
Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems
No full textMulti-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III)
