Research Article
Assessment of Onion Bulb and Seed Production Potentials and Challenges in Gebiresu Zone, Afar National Regional State, Ethiopia: Survey Findings
Yitages Kuma Beji*
,
Shimelis Alemayehu Seta
Issue:
Volume 5, Issue 1, March 2025
Pages:
1-29
Received:
2 December 2024
Accepted:
16 December 2024
Published:
17 January 2025
DOI:
10.11648/j.frontiers.20250501.11
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Views:
Abstract: This study, conducted in 2024, evaluates onion production practices, identifies key challenges, and explores potential improvements across four districts: Amibara, Gewane, Haruka, and Gelealo in the Middle Awash region of Ethiopia. The research focuses on current agricultural practices, the effectiveness of existing methods, and the socio-economic factors influencing onion farming. Each district exhibited distinct variations in these practices. Amibara showed relatively better adoption of recommended practices, particularly in irrigation and balanced fertilizer use. However, pest pressures, notably from Thrips and Stemphylium leaf blight, significantly affected yields. This district’s reliance on chemical pesticides without integrated pest management (IPM) strategies poses long-term risks for soil health and pesticide resistance. Gewane and Haruka faced pronounced challenges related to water availability, leading to inconsistent irrigation practices. Gewane, with the lowest irrigation frequencies, showed reduced yields due to suboptimal water management. In both districts, pest infestations further exacerbated yield losses. This highlights the need for improved irrigation infrastructure and pest control strategies. In Gelealo, while fertilizer use was widespread, inconsistencies in application rates and a lack of IPM strategies led to lower yields. The district's reliance on local brokers for market access constrained economic outcomes. Additionally, like other districts, Gelealo lacked access to certified seeds, further limiting productivity. Pest and disease pressures were pervasive across all districts, particularly in Haruka and Amibara, where pest-related crop damage was highest. Moreover, the lack of post-harvest infrastructure and market access challenges, particularly in Gewane and Gelealo, reduced onion profitability. Overall, the findings underscore the critical need for improvements in irrigation, fertilization practices, pest control strategies, and market systems. District-specific interventions, such as promoting IPM, improving access to certified seeds, and enhancing market linkages, are essential to significantly improve onion yield, post-harvest quality, and economic returns in the Middle Awash region.
Abstract: This study, conducted in 2024, evaluates onion production practices, identifies key challenges, and explores potential improvements across four districts: Amibara, Gewane, Haruka, and Gelealo in the Middle Awash region of Ethiopia. The research focuses on current agricultural practices, the effectiveness of existing methods, and the socio-economic ...
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Research Article
Data-Driven Frequency Security Assessment Based on Generative Adversarial Networks and Metric Learning
Issue:
Volume 5, Issue 1, March 2025
Pages:
30-41
Received:
31 October 2024
Accepted:
4 January 2025
Published:
24 January 2025
Abstract: With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) and Metric Learning (ML) is proposed in this paper. Firstly, the key frequency security indicators are selected as the outputs of the model, and the input feature set is constructed. Then, distribution information of historical operation scenarios is learned through Wasserstein Generative Adversarial Network (WGAN), in order to generate operation scenarios covering typical operation modes for training sample set establishment. The generated operation scenarios are adjusted based on rejection sampling and resampling techniques, in order to increase the density of training samples near key scenes. Finally, considering inapplicability of a single assessment model for frequency security assessment in power systems with complicated changes of operation conditions, a combined assessment model for frequency security assessment composed of multiple sub-models is constructed based on Metric Learning for Kernel Regression (MLKR). The original distance metric is adjusted with metric learning techniques to make samples with similar frequency dynamics close. Then the samples with similar frequency dynamics are clustered into the same cluster, and the corresponding sub-model is established. A simplified Shandong power system example is used to verify the effectiveness of the proposed method.
Abstract: With construction of large-capacity direct current transmission projects and large-scale integration of renewable energy, frequency security of the power system is facing severe challenges. For fast and accurate online assessment of frequency security, a data-driven frequency security assessment model based on Generative Adversarial Network (GAN) a...
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Research Article
Emergency Control Scheme Decision with Two-Stage Optimization for Hvdc Blocking
Issue:
Volume 5, Issue 1, March 2025
Pages:
42-52
Received:
23 December 2024
Accepted:
6 January 2025
Published:
17 February 2025
DOI:
10.11648/j.frontiers.20250501.13
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Views:
Abstract: In view of the fact that the existing offline decision making methods cannot meet the requirements for accurate control of the actual operation mode under severe power disturbance events such as DC blocking, a two-stage optimization method of emergency control scheme including pre-optimization and online optimization is proposed by comprehensive application of time domain simulation and machine learning technology First, typical scenarios reflecting the possible future operation mode of the system are generated in the pre-optimization stage, and multi-resource coordination emergency control schemes in each scenario are optimized by using trajectory sensitivity and machine learning methods, and stored in the knowledge base In the online optimization stage, the information of the actual operation mode of the system is obtained. Based on metric learning, the appropriate control scheme is selected from the knowledge base as the initial value, and the optimal control scheme under the actual operation mode is obtained by rapid iterative optimization. A multi-dc fed terminal system is taken as an example to verify the effectiveness of the proposed method.
Abstract: In view of the fact that the existing offline decision making methods cannot meet the requirements for accurate control of the actual operation mode under severe power disturbance events such as DC blocking, a two-stage optimization method of emergency control scheme including pre-optimization and online optimization is proposed by comprehensive ap...
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