Long-run Spatial and Temporal Yield Variability Analysis of Three Major Crops Affected by Fertilizer Use and Rainfall in Ethiopia over the Past 15 Years (2004/05–2018/19)
Bibliographic record
Abstract
Ethiopia is one of the major producers of maize and wheat and the only producer of teff at a larger scale for grain in sub-Saharan Africa (SSA). Various efforts have been made by the government of Ethiopia to increase productivity over the past 15 years. Here we analyze a dataset with more than 1,260 yield observations from 2004/05 to 2018/19 for three crops (teff, maize, and wheat) in the 28 zones of the two major cereal-growing regions of the country. These two regions, Amhara, and Oromia represent around 81% of cropped area, 75% of fertilizer use, and 82% of cereal production of Ethiopia annually. Zonal level crop production data were used to analyze spatial and temporal patterns of teff, wheat, and maize yield. Zones were categorized as wet and dry based on annual rainfall. We dissect the evolution of yield trends over time and space, analyze yield variation, and evaluate whether growth of yields has increased, decreased, or stalled in recent years. We found that productivity of teff, wheat, and maize continued to increase from 0.95 to 1.76 metric tons per hectare (mt ha -1), 1.56 to 2.76 mt ha -1, and 1.72 to 4.0 mt ha -1 respectively, between 2004/05 and 2018/19. There was an average annual increase of 5%–8% during this time. The data also show a strong correlation of yield with rainfall and fertilizer use patterns; therefore, we recommend that the fertilizer advisory service should also make use of the rainfall conditions of the different locations to fine-tune fertilizer recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".