GEOGRAPHIC INFORMATION SYSTEMS FOR ASSESSMENT OF CLIMATE CHANGE EFFECTS\nON TEFF IN ETHIOPIA
Bibliographic record
Abstract
The value of Geographic Information Systems (GIS) for assessing climate change impacts on crop productivity cannot be over-emphasised.This study evaluated a GIS based methodology for teff (Eragrostis tef) production in Ethiopia.We examined the spatial implications of climate change on areas suitable for teff, and estimated the effects of altered environments on teff's productivity.There was a non-linear relationship between suitability indices, the output of spatial analysis and teff yield data collected from diverse ecological zones.This served as the basis for country-wide crop yield analysis for both current and future climate scenarios.To complement this effort, a socio-economic survey was carried with a thrust of understanding the agricultural activities in the study area.With the current climatic conditions, 87.7% of Ethiopia is suitable for teff.On the other hand, approximately 67.7% of Ethiopia is expected to be suitable for teff production by 2050.Suitability index (SI) and the actual crop yield data showed a strong positive correlation (r = 74%).There is a predicted severe drop in teff yield (-0.46 t ha -1 ) by the year 2050.Based on the current area under teff in Ethiopia, this equals an overall reduction in national production of about 1,190,784.12t, equivalent to a loss of US$ 651 million to farmers.The results indicate that crop yield varied significantly as a function of climatic variation and that the model is applicable in assessing the impact of climate change on crop productivity at various levels taking into consideration spatial variability of climate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".