Status of Groundwater Table Depth Under Long-Term Irrigation in Wonji Plain: Concerns for Sustainability of Wonji-Shoa Sugar Estate, Upper Awash Valley, Ethiopia
Bibliographic record
Abstract
The present study attempted to highlight the concerns of shallow groundwater table depth (GWTD) to the sustainability of Wonji-Shoa Sugar Estate (WSSE), Upper Awash Valley of Ethiopia. The recent magnitude and fluctuation of GWTD is mapped in ArcView GIS (using universal kriging technique) from the monthly GWTD monitoring data (2007-2009) using piezometer tubes. The study result clearly showed that the GWTD at WSSE was extremely shallow, showed great spatio-seasonal variability and rising trend; thus, threatening the sustainability of WSSE significantly. About 90% of the plantation fields have GWTD above the critical depth of 1.5 m below the soil surface and, hence, critically waterlogged. As a result, the estate is recently achieving only 55% of the production potential realized in the 1960s. Past trends indicate that the GW has the potential to inundate Wonji plain and is anticipated to devastate production during the next 10-15 years. Therefore, in order to sustain production system in the region, there is an urgent need to identify the possible causes of waterlogging and investigate the feasible remedial measures to combat its problems. The spatial mapping of GWTD with identified problematic areas, indicated in the current study, is believed to provide a tool for water management and, hence, crucial for the decision making and actions taking processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".